Thesis/thesis.lyx

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\begin_layout Title
Bioinformatic analysis of complex, high-throughput genomic and epigenomic
 data in the context of CD4
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 T-cell differentiation and diagnosis and treatment of transplant rejection
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A thesis presented 
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by
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 Ryan C.
 Thompson
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 to
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 The Scripps Research Institute Graduate Program 
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in partial fulfillment of the requirements for the degree of
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 Doctor of Philosophy in the subject of Biology
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 for
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 The Scripps Research Institute
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 La Jolla, California
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\begin_layout Date
October 2019
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© 2019 by Ryan C.
 Thompson
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All rights reserved.
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[Thesis acceptance form]
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For Dan, who helped me through the hard times again and again.
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He is, and will always be, fondly remembered and sorely missed.
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Acknowledgements
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My path through graduate school has been a long and winding one, and I am
 grateful to all the mentors I have had through the years – Drs.
 Terry Gaasterland, Daniel Salomon, and Andrew Su – all of whose encouragement
 and support have been vital to my development into the scientist I am today.
 I am also thankful for my collaborators in the Salomon lab: Drs.
 Sarah Lamere, Sunil Kurian, Thomas Whisenant, Padmaja Natarajan, Katie
 Podshivalova, and Heather Kiyomi Komori; as well as the many other lab
 members I have worked with in small ways over the years.
 In addition, Steven Head, Dr.
 Phillip Ordoukhanian, and Terri Gelbart from the Scripps Genomics core
 have also been instrumental in supporting my work.
 And of course, I am thankful for the guidance and expertise provided by
 my committee, Drs.
 Nicholas Schork, Ali Torkamani, Michael Petrascheck, and Luc Teyton.
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Finally, I wish to thank my parents, for instilling in me a love of science
 and learning from an early age and encouraging me to pursue that love as
 a career as I grew up.
 I am truly lucky to have such a loving and supportive family.
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It is included as an integral part of the thesis and should immediately
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Preparing your Abstract.
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Transplant rejection mediated by adaptive immune response is the major challenge
 to long-term graft survival.
 Rejection is treated with immune suppressive drugs, but early diagnosis
 is essential for effective treatment.
 Memory lymphocytes are known to resist immune suppression, but the precise
 regulatory mechanisms underlying immune memory are still poorly understood.
 High-throughput genomic assays such as microarrays, RNA-seq, and ChIP-seq
 are heavily used in the study of immunology and transplant rejection.
 Here we present 3 analyses of such assays in this context.
 First, we re-analyze a large data set consisting of H3K4me2, H3K4me3, and
 H3K27me3 ChIP-seq data and RNA-seq data in naïve and memory CD4
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 T-cells using modern bioinformatics methods designed to address deficiencies
 in the data and extend the analysis in several new directions.
 All 3 histone marks are found to occur in broad regions and are enriched
 near promoters, but the radius of promoter enrichment is found to be larger
 for H3K27me3.
 We observe that both gene expression and promoter histone methylation in
 naïve and memory cells converges on a common signature 14 days after activation
, consistent with differentiation of naïve cells into memory cells.
 The location of histone modifications within the promoter is also found
 to be important, with asymmetric associations with gene expression for
 peaks located the same distance up- or downstream of the TSS.
 Second, we demonstrate the effectiveness of fRMA as a single-channel normalizat
ion for using expression arrays to diagnose transplant rejection in a clinical
 diagnostic setting, and we develop a custom fRMA normalization for a previously
 unsupported array platform.
 For methylation arrays, we adapt methods designed for RNA-seq to improve
 the sensitivity of differential methylation analysis by modeling the heterosked
asticity inherent in the data.
 Finally, we present and validate a novel method for RNA-seq of cynomolgus
 monkey blood samples using complementary oligonucleotides to prevent wasteful
 over-sequencing of globin genes.
 These results all demonstrate the usefulness of a toolbox full of flexible
 and modular analysis methods in analyzing complex high-throughput assays
 in contexts ranging from basic science to translational medicine.
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Thank you so much for agreeing to read my thesis and give me feedback on
 it.
 What you are currently reading is a rough draft, in need of many revisions.
 You can always find the latest version at 
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.
 the PDF at this link is updated periodically with my latest revisions,
 but you can just download the current version and give me feedback on that.
 Don't worry about keeping up with the updates.
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As for what feedback I'm looking for, first of all, don't waste your time
 marking spelling mistakes and such.
 I haven't run a spell checker on it yet, so let me worry about that.
 Also, I'm aware that many abbreviations are not properly introduced the
 first time they are used, so don't worry about that either.
 However, if you see any glaring formatting issues, such as a figure being
 too large and getting cut off at the edge of the page, please note them.
 In addition, if any of the text in the figures is too small, please note
 that as well.
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Beyond that, what I'm mainly interested in is feedback on the content.
 For example: does the introduction flow logically, and does it provide
 enough background to understand the other chapters? Does each chapter make
 it clear what work and analyses I have done? Do the figures clearly communicate
 the results I'm trying to show? Do you feel that the claims in the results
 and discussion sections are well-supported? There's no need to suggest
 improvements; just note areas that you feel need improvement.
 Additionally, if you notice any un-cited claims in any chapter, please
 flag them for my attention.
 Similarly, if you discover any factual errors, please note them as well.
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You can provide your feedback in whatever way is most convenient to you.
 You could mark up this PDF with highlights and notes, then send it back
 to me.
 Or you could collect your comments in a separate text file and send that
 to me, or whatever else you like.
 However, if you send me your feedback in a separate document, please note
 a section/figure/table number for each comment, and 
\emph on
also
\emph default
 send me the exact PDF that you read so I can reference it while reading
 your comments, since as mentioned above, the current version I'm working
 on will have changed by that point (which might include shuffling sections
 and figures around, changing their numbers).
 One last thing: you'll see a bunch of text in orange boxes throughout the
 PDF.
 These are notes to myself about things that need to be fixed later, so
 if you see a problem noted in an orange box, that means I'm already aware
 of it, and there's no need to comment on it.
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My thesis is due Thursday, October 10th, so in order to be useful to me,
 I'll need your feedback at least several days before that, ideally by Monday,
 October 7th.
 If you have limited time and are unable to get through the whole thesis,
 please focus your efforts on Chapters 1 and 2, since those are the roughest
 and most in need of revision.
 Chapter 3 is fairly short and straightforward, and Chapter 4 is an adaptation
 of a paper that's already been through a few rounds of revision, so they
 should be a lot tighter.
 If you can't spare any time between now and then, or if something unexpected
 comes up, I understand.
 Just let me know.
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Thanks again for your help, and happy reading!
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Introduction
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Biological motivation
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Rejection is the major long-term threat to organ and tissue allografts
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Organ and tissue transplants are a life-saving treatment for people who
 have lost the function of an important organ.
 In some cases, it is possible to transplant a patient's own tissue from
 one area of their body to another, referred to as an autograft.
 This is common for tissues that are distributed throughout many areas of
 the body, such as skin and bone.
 However, in cases of organ failure, there is no functional self tissue
 remaining, and a transplant from another person – a donor – is required.
 This is referred to as an allograft 
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Because an allograft comes from a donor of the same species who is genetically
 distinct from the recipient (with rare exceptions), genetic variants in
 protein-coding regions affect the polypeptide sequences encoded by the
 affected genes, resulting in protein products in the allograft that differ
 from the equivalent proteins produced by the graft recipient's own tissue.
 As a result, without intervention, the recipient's immune system will eventuall
y identify the graft as foreign tissue and begin attacking it.
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 results in failure and death of the graft, a process referred to as transplant
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 Rejection is the primary obstacle to long-term health and survival of an
 allograft 
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 Like any adaptive immune response, an alloimmune response generally occurs
 via two broad mechanisms: cellular immunity, in which CD8
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  T-cells recognizing graft-specific antigens induce apoptosis in the graft
 cells; and humoral immunity, in which B-cells produce antibodies that bind
 to graft proteins and direct an immune response against the graft 
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 In either case, alloimmunity and rejection show most of the typical hallmarks
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 T-cells and formation of immune memory.
 
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Diagnosis and treatment of allograft rejection is a major challenge
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To prevent rejection, allograft recipients are treated with immune suppressive
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.
 The goal is to achieve sufficient suppression of the immune system to prevent
 rejection of the graft without compromising the ability of the immune system
 to raise a normal response against infection.
 As such, a delicate balance must be struck: insufficient immune suppression
 may lead to rejection and ultimately loss of the graft; excessive suppression
 leaves the patient vulnerable to life-threatening opportunistic infections
 
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.
 Because every patient's matabolism is different, achieving this delicate
 balance requires drug dosage to be tailored for each patient.
 Furthermore, dosage must be tuned over time, as the immune system's activity
 varies over time and in response to external stimuli with no fixed pattern.
 In order to properly adjust the dosage of immune suppression drugs, it
 is necessary to monitor the health of the transplant and increase the dosage
 if evidence of rejection or alloimmune activity is observed.
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However, diagnosis of rejection is a significant challenge.
 Early diagnosis is essential in order to step up immune suppression before
 the immune system damages the graft beyond recovery 
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.
 The current gold standard test for graft rejection is a tissue biopsy,
 examined for visible signs of rejection by a trained histologist 
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.
 When a patient shows symptoms of possible rejection, a 
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for cause
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 biopsy is performed to confirm the diagnosis, and immune suppression is
 adjusted as necessary.
 However, in many cases, the early stages of rejection are asymptomatic,
 known as 
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sub-clinical
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 rejection.
 In light of this, is is now common to perform 
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protocol biopsies
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 at specific times after transplantation of a graft, even if no symptoms
 of rejection are apparent, in addition to 
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for cause
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 biopsies 
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.
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However, biopsies have a number of downsides that limit their effectiveness
 as a diagnostic tool.
 First, the need for manual inspection by a histologist means that diagnosis
 is subject to the biases of the particular histologist examining the biopsy
 
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key "Kurian2014"
literal "false"

\end_inset

.
 In marginal cases, two different histologists may give two different diagnoses
 to the same biopsy.
 Second, a biopsy can only evaluate if rejection is occurring in the section
 of the graft from which the tissue was extracted.
 If rejection is localized to one section of the graft and the tissue is
 extracted from a different section, a false negative diagnosis may result.
 Most importantly, extraction of tissue from a graft is invasive and is
 treated as an injury by the body, which results in inflammation that in
 turn promotes increased immune system activity.
 Hence, the invasiveness of biopsies severely limits the frequency with
 which they can safely be performed 
\begin_inset CommandInset citation
LatexCommand cite
key "Patel2018"
literal "false"

\end_inset

.
 Typically, protocol biopsies are not scheduled more than about once per
 month 
\begin_inset CommandInset citation
LatexCommand cite
key "Wilkinson2006"
literal "false"

\end_inset

.
 A less invasive diagnostic test for rejection would bring manifold benefits.
 Such a test would enable more frequent testing and therefore earlier detection
 of rejection events.
 In addition, having a larger pool of historical data for a given patient
 would make it easier to evaluate when a given test is outside the normal
 parameters for that specific patient, rather than relying on normal ranges
 for the population as a whole.
 Lastly, the accumulated data from more frequent tests would be a boon to
 the transplant research community.
 Beyond simply providing more data overall, the better time granularity
 of the tests will enable studying the progression of a rejection event
 on the scale of days to weeks, rather than months.
\end_layout

\begin_layout Subsection
Memory cells are resistant to immune suppression
\end_layout

\begin_layout Standard
One of the defining features of the adaptive immune system is immune memory:
 the ability of the immune system to recognize a previously encountered
 foreign antigen and respond more quickly and more strongly to that antigen
 in subsequent encounters 
\begin_inset CommandInset citation
LatexCommand cite
key "Murphy2012"
literal "false"

\end_inset

.
 When the immune system first encounters a new antigen, the T-cells that
 respond are known as naïve cells – T-cells that have never detected their
 target antigens before.
 Once activated by their specific antigen presented by an antigen-presenting
 cell in the proper co-stimulatory context, naïve cells differentiate into
 effector cells that carry out their respective functions in targeting and
 destroying the source of the foreign antigen.
 The 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
TCR
\end_layout

\end_inset

 is cell-surface protein complex produced by T-cells that is responsible
 for recognizing the T-cell's specific antigen, presented on a 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
MHC
\end_layout

\end_inset

, the cell-surface protein complex used by an 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
APC
\end_layout

\end_inset

 to present antigens to the T-cell.
 However, a naïve T-cell that recognizes its antigen also requires a co-stimulat
ory signal, delivered through other interactions between 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
APC
\end_layout

\end_inset

 surface proteins and T-cell surface proteins such as CD28.
 Without proper co-stimulation, a T-cell that recognizes its antigen either
 dies or enters an unresponsive state known as anergy, in which the T-cell
 becomes much more resistant to subsequent activation even with proper co-stimul
ation.
 The dependency of activation on co-stimulation is an important feature
 of naïve lymphocytes that limits 
\begin_inset Quotes eld
\end_inset

false positive
\begin_inset Quotes erd
\end_inset

 immune responses against self antigens, because 
\begin_inset Flex Glossary Term (pl)
status open

\begin_layout Plain Layout
APC
\end_layout

\end_inset

 usually only express the proper co-stimulation after the innate immune
 system detects signs of an active infection, such as the presence of common
 bacterial cell components or inflamed tissue.
 
\end_layout

\begin_layout Standard
After the foreign antigen is cleared, most effector cells die since they
 are no longer needed, but some differentiate into memory cells and remain
 alive indefinitely.
 Like naïve cells, memory cells respond to detection of their specific antigen
 by differentiating into effector cells, ready to fight an infection 
\begin_inset CommandInset citation
LatexCommand cite
key "Murphy2012"
literal "false"

\end_inset

.
 However, the memory response to antigen is qualitatively different: memory
 cells are more sensitive to detection of their antigen, and a lower concentrati
on of antigen is suffiicient to activate them 
\begin_inset CommandInset citation
LatexCommand cite
key "Rogers2000,London2000,Berard2002"
literal "false"

\end_inset

.
 In addition, memory cells are much less dependent on co-stimulation for
 activation: they can activate without certain co-stimulatory signals that
 are required by naïve cells, and the signals they do require are only required
 at lower levels in order to cause activation 
\begin_inset CommandInset citation
LatexCommand cite
key "London2000"
literal "false"

\end_inset

.
 Furthermore, mechanisms that induce tolerance (non-response to antigen)
 in naïve cells are much less effective on memory cells 
\begin_inset CommandInset citation
LatexCommand cite
key "London2000"
literal "false"

\end_inset

.
 Lastly, once activated, memory cells proliferate and differentiate into
 effector cells more quickly than naïve cells do 
\begin_inset CommandInset citation
LatexCommand cite
key "Berard2002"
literal "false"

\end_inset

.
 In combination, these changes in lymphocyte behavior upon differentiation
 into memory cells account for the much quicker and stronger response of
 the immune system to subsequent exposure to a previously-encountered antigen.
\end_layout

\begin_layout Standard
In the context of a pathogenic infection, immune memory is a major advantage,
 allowing an organism to rapidly fight off a previously encountered pathogen
 much more quickly and effectively than the first time it was encountered
 
\begin_inset CommandInset citation
LatexCommand cite
key "Murphy2012"
literal "false"

\end_inset

.
 However, if effector cells that recognize an antigen from an allograft
 are allowed to differentiate into memory cells, preventing rejection of
 the graft becomes much more difficult.
 Many immune suppression drugs work by interfering with the co-stimulation
 that naïve cells require in order to mount an immune response.
 Since memory cells do not require the same degree of co-stimulation, these
 drugs are not effective at suppressing an immune response that is mediated
 by memory cells.
 Secondly, because memory cells are able to mount a stronger and faster
 response to an antigen, all else being equal stronger immune suppression
 is required to prevent an immune response mediated by memory cells.
\end_layout

\begin_layout Standard
However, immune suppression affects the entire immune system, not just cells
 recognizing a specific antigen, so increasing the dosage of immune suppression
 drugs also increases the risk of complications from a compromised immune
 system, such as opportunistic infections 
\begin_inset CommandInset citation
LatexCommand cite
key "Murphy2012"
literal "false"

\end_inset

.
 While the differences in cell surface markers between naïve and memory
 cells have been fairly well characterized, the internal regulatory mechanisms
 that allow memory cells to respond more quickly and without co-stimulation
 are still poorly understood.
 In order to develop methods of immune suppression that either prevent the
 formation of memory cells or work more effectively against memory cells,
 a more complete understanding of the mechanisms of immune memory formation
 and regulation is required.
\end_layout

\begin_layout Subsection
Infusion of allogenic mesenchymal stem cells modulates the alloimmune response
\end_layout

\begin_layout Standard
One promising experimental treatment for transplant rejection involves the
 infusion of allogenic 
\begin_inset Flex Glossary Term (pl)
status open

\begin_layout Plain Layout
MSC
\end_layout

\end_inset

.
 
\begin_inset Flex Glossary Term (pl)
status open

\begin_layout Plain Layout
MSC
\end_layout

\end_inset

 have been shown to have immune modulatory effects, both in general and
 specifically in the case of immune responses against allografts 
\begin_inset CommandInset citation
LatexCommand cite
key "LeBlanc2003,Aggarwal2005,Bartholomew2009,Berman2010"
literal "false"

\end_inset

.
 Furthermore, allogenic 
\begin_inset Flex Glossary Term (pl)
status open

\begin_layout Plain Layout
MSC
\end_layout

\end_inset

 themselves are immune-evasive and are rejected by the recipient's immune
 system more slowly than most allogenic tissues 
\begin_inset CommandInset citation
LatexCommand cite
key "Ankrum2014,Berglund2017"
literal "false"

\end_inset

.
 In addition, treating 
\begin_inset Flex Glossary Term (pl)
status open

\begin_layout Plain Layout
MSC
\end_layout

\end_inset

 in culture with 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
IFNg
\end_layout

\end_inset

 is shown to enhance their immunosuppressive properties and homogenize their
 cellulat phenotype, making them more amenable to development into a well-contro
lled treatment 
\begin_inset CommandInset citation
LatexCommand cite
key "Majumdar2003,Ryan2007"
literal "false"

\end_inset

.
 The mechanisms by which 
\begin_inset Flex Glossary Term (pl)
status open

\begin_layout Plain Layout
MSC
\end_layout

\end_inset

 modulate the immune system are still poorly understood.
 Despite this, there is signifcant interest in using 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
IFNg
\end_layout

\end_inset

-activated 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
MSC
\end_layout

\end_inset

 infusion as a supplementary immune suppressive treatment for allograft
 transplantation.
 
\end_layout

\begin_layout Standard
Note that despite the name, none of the above properties of 
\begin_inset Flex Glossary Term (pl)
status open

\begin_layout Plain Layout
MSC
\end_layout

\end_inset

 are believed to involve their ability as stem cells to differentiate into
 multiple different mature cell types, but rather the intercellular signals
 they produce 
\begin_inset CommandInset citation
LatexCommand cite
key "Ankrum2014"
literal "false"

\end_inset

.
\end_layout

\begin_layout Standard
\begin_inset Flex TODO Note (inline)
status open

\begin_layout Plain Layout
An overview of high-throughput assays would have been nice to have, but
 it's a bit late now.
\end_layout

\end_inset


\end_layout

\begin_layout Section
\begin_inset CommandInset label
LatexCommand label
name "sec:Overview-of-bioinformatic"

\end_inset

Overview of bioinformatic analysis methods
\end_layout

\begin_layout Standard
The studies presented in this work all involve the analysis of high-throughput
 genomic and epigenomic assay data.
 Assays like microarrays and 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
HTS
\end_layout

\end_inset

 are powerful methods for interrogating gene expression and epigenetic state
 across the entire genome.
 However, these data present many unique analysis challenges, and proper
 analysis requires identifying and exploiting genome-wide trends in the
 data to make up for the small sample sizes.
 A wide array of software tools is available to analyze these data.
 This section presents an overview of the most important methods and tools
 used throughout the following analyses, including what problems they solve,
 what assumptions they make, and a basic description of how they work.
\end_layout

\begin_layout Subsection
\begin_inset Flex Code
status open

\begin_layout Plain Layout
Limma
\end_layout

\end_inset

: The standard linear modeling framework for genomics
\end_layout

\begin_layout Standard
Linear models are a generalization of the 
\begin_inset Formula $t$
\end_inset

-test and ANOVA to arbitrarily complex experimental designs 
\begin_inset CommandInset citation
LatexCommand cite
key "chambers:1992"
literal "false"

\end_inset

.
 In a typical linear model, there is one dependent variable observation
 per sample and a large number of samples.
 For example, in a linear model of height as a function of age and sex,
 there is one height measurement per person.
 However, when analyzing genomic data, each sample consists of observations
 of thousands of dependent variables.
 For example, in a 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
RNA-seq
\end_layout

\end_inset

 experiment, the dependent variables may be the count of 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
RNA-seq
\end_layout

\end_inset

 reads for each annotated gene, and there are tens of thousands of genes
 in the human genome.
 Since many assays measure other things than gene expression, the abstract
 term 
\begin_inset Quotes eld
\end_inset

feature
\begin_inset Quotes erd
\end_inset

 is used to refer to each dependent variable being measured, which may include
 any genomic element, such as genes, promoters, peaks, enhancers, exons,
 etc.
 
\end_layout

\begin_layout Standard
The simplest approach to analyzing such data would be to fit the same model
 independently to each feature.
 However, this is undesirable for most genomics data sets.
 Genomics assays like 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
HTS
\end_layout

\end_inset

 are expensive, and often the process of generating the samples is also
 quite expensive and time-consuming.
 This expense limits the sample sizes typically employed in genomics experiments
, so a typical genomic data set has far more features being measured than
 observations (samples) per feature.
 As a result, the statistical power of the linear model for each individual
 feature is likewise limited by the small number of samples.
 However, because thousands of features from the same set of samples are
 analyzed together, there is an opportunity to improve the statistical power
 of the analysis by exploiting shared patterns of variation across features.
 This is the core feature of 
\begin_inset Flex Code
status open

\begin_layout Plain Layout
limma
\end_layout

\end_inset

, a linear modeling framework designed for genomic data.
 
\begin_inset Flex Code
status open

\begin_layout Plain Layout
Limma
\end_layout

\end_inset

 is typically used to analyze expression microarray data, and more recently
 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
RNA-seq
\end_layout

\end_inset

 data, but it can also be used to analyze any other data for which linear
 modeling is appropriate.
\end_layout

\begin_layout Standard
The central challenge when fitting a linear model is to estimate the variance
 of the data accurately.
 Out of all parameters required to evaluate statistical significance of
 an effect, the variance is the most difficult to estimate when sample sizes
 are small.
 A single shared variance could be estimated for all of the features together,
 and this estimate would be very stable, in contrast to the individual feature
 variance estimates.
 However, this would require the assumption that all features have equal
 variance, which is known to be false for most genomic data sets (for example,
 some genes' expression is known to be more variable than others').
 
\begin_inset Flex Code
status open

\begin_layout Plain Layout
Limma
\end_layout

\end_inset

 offers a compromise between these two extremes by using a method called
 empirical Bayes moderation to 
\begin_inset Quotes eld
\end_inset

squeeze
\begin_inset Quotes erd
\end_inset

 the distribution of estimated variances toward a single common value that
 represents the variance of an average feature in the data (Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:ebayes-example"
plural "false"
caps "false"
noprefix "false"

\end_inset

) 
\begin_inset CommandInset citation
LatexCommand cite
key "Smyth2004"
literal "false"

\end_inset

.
 While the individual feature variance estimates are not stable, the common
 variance estimate for the entire data set is quite stable, so using a combinati
on of the two yields a variance estimate for each feature with greater precision
 than the individual feature variances.
 The trade-off for this improvement is that squeezing each estimated variance
 toward the common value introduces some bias – the variance will be underestima
ted for features with high variance and overestimated for features with
 low variance.
 Essentially, 
\begin_inset Flex Code
status open

\begin_layout Plain Layout
limma
\end_layout

\end_inset

 assumes that extreme variances are less common than variances close to
 the common value.
 The squeezed variance estimates from this empirical Bayes procedure are
 shown empirically to yield greater statistical power than either the individual
 feature variances or the single common value.
\end_layout

\begin_layout Standard
\begin_inset Float figure
wide false
sideways false
status collapsed

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename graphics/Intro/eBayes-CROP-RASTER.png
	lyxscale 25
	width 100col%
	groupId colwidth-raster

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout
\begin_inset Argument 1
status collapsed

\begin_layout Plain Layout
Example of empirical Bayes squeezing of per-gene variances.
\end_layout

\end_inset


\begin_inset CommandInset label
LatexCommand label
name "fig:ebayes-example"

\end_inset


\series bold
Example of empirical Bayes squeezing of per-gene variances.

\series default
 A smooth trend line (red) is fitted to the individual gene variances (light
 blue) as a function of average gene abundance (logCPM).
 Then the individual gene variances are 
\begin_inset Quotes eld
\end_inset

squeezed
\begin_inset Quotes erd
\end_inset

 toward the trend (dark blue).
\end_layout

\end_inset


\end_layout

\begin_layout Plain Layout

\end_layout

\end_inset


\end_layout

\begin_layout Standard
On top of this core framework, 
\begin_inset Flex Code
status open

\begin_layout Plain Layout
limma
\end_layout

\end_inset

 also implements many other enhancements that, further relax the assumptions
 of the model and extend the scope of what kinds of data it can analyze.
 Instead of squeezing toward a single common variance value, 
\begin_inset Flex Code
status open

\begin_layout Plain Layout
limma
\end_layout

\end_inset

 can model the common variance as a function of a covariate, such as average
 expression 
\begin_inset CommandInset citation
LatexCommand cite
key "Law2014"
literal "false"

\end_inset

.
 This is essential for 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
RNA-seq
\end_layout

\end_inset

 data, where higher gene counts yield more precise expression measurements
 and therefore smaller variances than low-count genes.
 While linear models typically assume that all samples have equal variance,
 
\begin_inset Flex Code
status open

\begin_layout Plain Layout
limma
\end_layout

\end_inset

 is able to relax this assumption by identifying and down-weighting samples
 that diverge more strongly from the linear model across many features 
\begin_inset CommandInset citation
LatexCommand cite
key "Ritchie2006,Liu2015"
literal "false"

\end_inset

.
 In addition, 
\begin_inset Flex Code
status open

\begin_layout Plain Layout
limma
\end_layout

\end_inset

 is also able to fit simple mixed models incorporating one random effect
 in addition to the fixed effects represented by an ordinary linear model
 
\begin_inset CommandInset citation
LatexCommand cite
key "Smyth2005a"
literal "false"

\end_inset

.
 Once again, 
\begin_inset Flex Code
status open

\begin_layout Plain Layout
limma
\end_layout

\end_inset

 shares information between features to obtain a robust estimate for the
 random effect correlation.
\end_layout

\begin_layout Subsection
\begin_inset Flex Code
status open

\begin_layout Plain Layout
edgeR
\end_layout

\end_inset

 provides 
\begin_inset Flex Code
status open

\begin_layout Plain Layout
limma
\end_layout

\end_inset

-like analysis features for read count data
\end_layout

\begin_layout Standard
Although 
\begin_inset Flex Code
status open

\begin_layout Plain Layout
limma
\end_layout

\end_inset

 can be applied to read counts from 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
RNA-seq
\end_layout

\end_inset

 data, it is less suitable for counts from 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
ChIP-seq
\end_layout

\end_inset

 and other sources, which tend to be much smaller and therefore violate
 the assumption of a normal distribution more severely.
 For all count-based data, the 
\begin_inset Flex Code
status open

\begin_layout Plain Layout
edgeR
\end_layout

\end_inset

 package works similarly to 
\begin_inset Flex Code
status open

\begin_layout Plain Layout
limma
\end_layout

\end_inset

, but uses a 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
GLM
\end_layout

\end_inset

 instead of a linear model.
 Relative to a linear model, a 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
GLM
\end_layout

\end_inset

 gains flexibility by relaxing several assumptions, the most important of
 which is the assumption of normally distributed errors.
 This allows the 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
GLM
\end_layout

\end_inset

 in 
\begin_inset Flex Code
status open

\begin_layout Plain Layout
edgeR
\end_layout

\end_inset

 to model the counts directly using a 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
NB
\end_layout

\end_inset

 distribution rather than modeling the normalized log counts using a normal
 distribution as 
\begin_inset Flex Code
status open

\begin_layout Plain Layout
limma
\end_layout

\end_inset

 does 
\begin_inset CommandInset citation
LatexCommand cite
key "Chen2014,McCarthy2012,Robinson2010a"
literal "false"

\end_inset

.
\end_layout

\begin_layout Standard
The 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
NB
\end_layout

\end_inset

 distribution is a good fit for count data because it can be derived as
 a gamma-distributed mixture of Poisson distributions.
 The reads in an 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
RNA-seq
\end_layout

\end_inset

 sample are assumed to be sampled from a much larger population, such that
 the sampling process does not significantly affect the proportions.
 Under this assumption, a gene's read count in an 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
RNA-seq
\end_layout

\end_inset

 sample is distributed as 
\begin_inset Formula $\mathrm{Binomial}(n,p)$
\end_inset

, where 
\begin_inset Formula $n$
\end_inset

 is the total number of reads sequenced from the sample and 
\begin_inset Formula $p$
\end_inset

 is the proportion of total fragments in the sample derived from that gene.
 When 
\begin_inset Formula $n$
\end_inset

 is large and 
\begin_inset Formula $p$
\end_inset

 is small, a 
\begin_inset Formula $\mathrm{Binomial}(n,p)$
\end_inset

 distribution is well-approximated by 
\begin_inset Formula $\mathrm{Poisson}(np)$
\end_inset

.
 Hence, if multiple sequencing runs are performed on the same 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
RNA-seq
\end_layout

\end_inset

 sample (with the same gene mixing proportions each time), each gene's read
 count is expected to follow a Poisson distribution.
 If the abundance of a gene, 
\begin_inset Formula $p,$
\end_inset

 varies across biological replicates according to a gamma distribution,
 and 
\begin_inset Formula $n$
\end_inset

 is held constant, then the result is a gamma-distributed mixture of Poisson
 distributions, which is equivalent to the 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
NB
\end_layout

\end_inset

 distribution.
 The assumption of a gamma distribution for the mixing weights is arbitrary,
 motivated by the convenience of the numerically tractable 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
NB
\end_layout

\end_inset

 distribution and the need to select 
\emph on
some
\emph default
 distribution, since the true shape of the distribution of biological variance
 is unknown.
\end_layout

\begin_layout Standard
Thus, 
\begin_inset Flex Code
status open

\begin_layout Plain Layout
edgeR
\end_layout

\end_inset

's use of the 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
NB
\end_layout

\end_inset

 is equivalent to an 
\emph on
a priori 
\emph default
assumption that the variation in gene abundances between replicates follows
 a gamma distribution.
 The gamma shape parameter in the context of the 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
NB
\end_layout

\end_inset

 is called the dispersion, and the square root of this dispersion is referred
 to as the 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
BCV
\end_layout

\end_inset

, since it represents the variability in abundance that was present in the
 biological samples prior to the Poisson 
\begin_inset Quotes eld
\end_inset

noise
\begin_inset Quotes erd
\end_inset

 that was generated by the random sampling of reads in proportion to feature
 abundances.
 Like 
\begin_inset Flex Code
status open

\begin_layout Plain Layout
limma
\end_layout

\end_inset

, 
\begin_inset Flex Code
status open

\begin_layout Plain Layout
edgeR
\end_layout

\end_inset

 estimates the 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
BCV
\end_layout

\end_inset

 for each feature using an empirical Bayes procedure that represents a compromis
e between per-feature dispersions and a single pooled dispersion estimate
 shared across all features.
 For differential abundance testing, 
\begin_inset Flex Code
status open

\begin_layout Plain Layout
edgeR
\end_layout

\end_inset

 offers a likelihood ratio test based on the 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
NB
\end_layout

\end_inset

 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
GLM
\end_layout

\end_inset

.
 However, this test assumes the dispersion parameter is known exactly rather
 than estimated from the data, which can result in overstating the significance
 of differential abundance results.
 More recently, a quasi-likelihood test has been introduced that properly
 factors the uncertainty in dispersion estimation into the estimates of
 statistical significance, and this test is recommended over the likelihood
 ratio test in most cases 
\begin_inset CommandInset citation
LatexCommand cite
key "Lund2012"
literal "false"

\end_inset

.
\end_layout

\begin_layout Subsection
Calling consensus peaks from ChIP-seq data
\end_layout

\begin_layout Standard
Unlike 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
RNA-seq
\end_layout

\end_inset

 data, in which gene annotations provide a well-defined set of discrete
 genomic regions in which to count reads, 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
ChIP-seq
\end_layout

\end_inset

 reads can potentially occur anywhere in the genome.
 However, most genome regions will not contain significant 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
ChIP-seq
\end_layout

\end_inset

 read coverage, and analyzing every position in the entire genome is statistical
ly and computationally infeasible, so it is necessary to identify regions
 of interest inside which 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
ChIP-seq
\end_layout

\end_inset

 reads will be counted and analyzed.
 One option is to define a set of interesting regions
\emph on
 a priori
\emph default
, for example by defining a promoter region for each annotated gene.
 However, it is also possible to use the 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
ChIP-seq
\end_layout

\end_inset

 data itself to identify regions with 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
ChIP-seq
\end_layout

\end_inset

 read coverage significantly above the background level, known as peaks.
 
\end_layout

\begin_layout Standard
The challenge in peak calling is that the immunoprecipitation step is not
 100% selective, so some fraction of reads are 
\emph on
not 
\emph default
derived from DNA fragments that were bound by the immunoprecipitated protein.
 These are referred to as background reads.
 Biases in amplification and sequencing, as well as the aforementioned Poisson
 randomness of the sequencing itself, can cause fluctuations in the background
 level of reads that resemble peaks, and the true peaks must be distinguished
 from these.
 It is common to sequence the input DNA to the ChIP-seq reaction alongside
 the immunoprecipitated product in order to aid in estimating the fluctuations
 in background level across the genome.
\end_layout

\begin_layout Standard
There are generally two kinds of peaks that can be identified: narrow peaks
 and broadly enriched regions.
 Proteins that bind specific sites in the genome (such as many transcription
 factors) typically show most of their 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
ChIP-seq
\end_layout

\end_inset

 read coverage at these specific sites and very little coverage anywhere
 else.
 Because the footprint of the protein is consistent wherever it binds, each
 peak has a consistent width, typically tens to hundreds of base pairs,
 representing the length of DNA that it binds to.
 Algorithms like 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
MACS
\end_layout

\end_inset

 exploit this pattern to identify specific loci at which such 
\begin_inset Quotes eld
\end_inset

narrow peaks
\begin_inset Quotes erd
\end_inset

 occur by looking for the characteristic peak shape in the 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
ChIP-seq
\end_layout

\end_inset

 coverage rising above the surrounding background coverage 
\begin_inset CommandInset citation
LatexCommand cite
key "Zhang2008"
literal "false"

\end_inset

.
 In contrast, some proteins, chief among them histones, do not bind only
 at a small number of specific sites, but rather bind potentially almost
 everywhere in the entire genome.
 When looking at histone marks, adjacent histones tend to be similarly marked,
 and a given mark may be present on an arbitrary number of consecutive histones
 along the genome.
 Hence, there is no consistent 
\begin_inset Quotes eld
\end_inset

footprint size
\begin_inset Quotes erd
\end_inset

 for 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
ChIP-seq
\end_layout

\end_inset

 peaks based on histone marks, and peaks typically span many histones.
 Hence, typical peaks span many hundreds or even thousands of base pairs.
 Instead of identifying specific loci of strong enrichment, algorithms like
 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
SICER
\end_layout

\end_inset

 assume that peaks are represented in the 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
ChIP-seq
\end_layout

\end_inset

 data by modest enrichment above background occurring across broad regions,
 and they attempt to identify the extent of those regions 
\begin_inset CommandInset citation
LatexCommand cite
key "Zang2009"
literal "false"

\end_inset

.
\end_layout

\begin_layout Standard
Regardless of the type of peak identified, it is important to identify peaks
 that occur consistently across biological replicates.
 The 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
ENCODE
\end_layout

\end_inset

 project has developed a method called 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
IDR
\end_layout

\end_inset

 for this purpose 
\begin_inset CommandInset citation
LatexCommand cite
key "Li2011"
literal "false"

\end_inset

.
 The 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
IDR
\end_layout

\end_inset

 is defined as the probability that a peak identified in one biological
 replicate will 
\emph on
not
\emph default
 also be identified in a second replicate.
 Where the more familiar false discovery rate measures the degree of corresponde
nce between a data-derived ranked list and the (unknown) true list of significan
t features, 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
IDR
\end_layout

\end_inset

 instead measures the degree of correspondence between two ranked lists
 derived from different data.
 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
IDR
\end_layout

\end_inset

 assumes that the highest-ranked features are 
\begin_inset Quotes eld
\end_inset

signal
\begin_inset Quotes erd
\end_inset

 peaks that tend to be listed in the same order in both lists, while the
 lowest-ranked features are essentially noise peaks, listed in random order
 with no correspondence between the lists.
 
\begin_inset Flex Glossary Term (Capital)
status open

\begin_layout Plain Layout
IDR
\end_layout

\end_inset

 attempts to locate the 
\begin_inset Quotes eld
\end_inset

crossover point
\begin_inset Quotes erd
\end_inset

 between the signal and the noise by determining how far down the list the
 rank consistency breaks down into randomness (Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:Example-IDR"
plural "false"
caps "false"
noprefix "false"

\end_inset

).
\end_layout

\begin_layout Standard
\begin_inset Float figure
wide false
sideways false
status open

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename graphics/CD4-csaw/IDR/D4659vsD5053_epic-PAGE1-CROP-RASTER.png
	lyxscale 25
	width 100col%
	groupId colwidth-raster

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout
\begin_inset Argument 1
status collapsed

\begin_layout Plain Layout
Example IDR consistency plot.
\end_layout

\end_inset


\begin_inset CommandInset label
LatexCommand label
name "fig:Example-IDR"

\end_inset


\series bold
Example IDR consistency plot.

\series default
 Peak calls in two replicates are ranked from highest score (top and right)
 to lowest score (bottom and left).
 IDR identifies reproducible peaks, which rank highly in both replicates
 (light blue), separating them from 
\begin_inset Quotes eld
\end_inset

noise
\begin_inset Quotes erd
\end_inset

 peak calls whose ranking is not reproducible between replicates (dark blue).
\end_layout

\end_inset


\end_layout

\begin_layout Plain Layout

\end_layout

\end_inset


\end_layout

\begin_layout Standard
In addition to other considerations, if called peaks are to be used as regions
 of interest for differential abundance analysis, then care must be taken
 to call peaks in a way that is blind to differential abundance between
 experimental conditions, or else the statistical significance calculations
 for differential abundance will overstate their confidence in the results.
 The 
\begin_inset Flex Code
status open

\begin_layout Plain Layout
csaw
\end_layout

\end_inset

 package provides guidelines for calling peaks in this way: peaks are called
 based on a combination of all 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
ChIP-seq
\end_layout

\end_inset

 reads from all experimental conditions, so that the identified peaks are
 based on the average abundance across all conditions, which is independent
 of any differential abundance between conditions 
\begin_inset CommandInset citation
LatexCommand cite
key "Lun2015a"
literal "false"

\end_inset

.
\end_layout

\begin_layout Subsection
Normalization of high-throughput data is non-trivial and application-dependent
\end_layout

\begin_layout Standard
High-throughput data sets invariably require some kind of normalization
 before further analysis can be conducted.
 In general, the goal of normalization is to remove effects in the data
 that are caused by technical factors that have nothing to do with the biology
 being studied.
\end_layout

\begin_layout Standard
For Affymetrix expression arrays, the standard normalization algorithm used
 in most analyses is 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
RMA
\end_layout

\end_inset

 
\begin_inset CommandInset citation
LatexCommand cite
key "Irizarry2003a"
literal "false"

\end_inset

.
 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
RMA
\end_layout

\end_inset

 is designed with the assumption that some fraction of probes on each array
 will be artifactual and takes advantage of the fact that each gene is represent
ed by multiple probes by implementing normalization and summarization steps
 that are robust against outlier probes.
 However, 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
RMA
\end_layout

\end_inset

 uses the probe intensities of all arrays in the data set in the normalization
 of each individual array, meaning that the normalized expression values
 in each array depend on every array in the data set, and will necessarily
 change each time an array is added or removed from the data set.
 If this is undesirable, 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
fRMA
\end_layout

\end_inset

 implements a variant of 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
RMA
\end_layout

\end_inset

 where the relevant distributional parameters are learned from a large reference
 set of diverse public array data sets and then 
\begin_inset Quotes eld
\end_inset

frozen
\begin_inset Quotes erd
\end_inset

, so that each array is effectively normalized against this frozen reference
 set rather than the other arrays in the data set under study 
\begin_inset CommandInset citation
LatexCommand cite
key "McCall2010"
literal "false"

\end_inset

.
 Other available array normalization methods considered include dChip, 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
GRSN
\end_layout

\end_inset

, and 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
SCAN
\end_layout

\end_inset

 
\begin_inset CommandInset citation
LatexCommand cite
key "Li2001,Pelz2008,Piccolo2012"
literal "false"

\end_inset

.
\end_layout

\begin_layout Standard
In contrast, 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
HTS
\end_layout

\end_inset

 data present very different normalization challenges.
 The simplest case is 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
RNA-seq
\end_layout

\end_inset

 in which read counts are obtained for a set of gene annotations, yielding
 a matrix of counts with rows representing genes and columns representing
 samples.
 Because 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
RNA-seq
\end_layout

\end_inset

 approximates a process of sampling from a population with replacement,
 each gene's count is only interpretable as a fraction of the total reads
 for that sample.
 For that reason, 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
RNA-seq
\end_layout

\end_inset

 abundances are often reported as 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
CPM
\end_layout

\end_inset

.
 Furthermore, if the abundance of a single gene increases, then in order
 for its fraction of the total reads to increase, all other genes' fractions
 must decrease to accommodate it.
 This effect is known as composition bias, and it is an artifact of the
 read sampling process that has nothing to do with the biology of the samples
 and must therefore be normalized out.
 The most commonly used methods to normalize for composition bias in 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
RNA-seq
\end_layout

\end_inset

 data seek to equalize the average gene abundance across samples, under
 the assumption that the average gene is likely not changing 
\begin_inset CommandInset citation
LatexCommand cite
key "Robinson2010,Anders2010"
literal "false"

\end_inset

.
 The effect of such normalizations is to center the distribution of 
\begin_inset Flex Glossary Term (pl)
status open

\begin_layout Plain Layout
logFC
\end_layout

\end_inset

 at zero.
 Note that if a true global difference in gene expression is present in
 the data, this difference will be normalized out as well, since it is indisting
uishable from composition bias.
 In other words, 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
RNA-seq
\end_layout

\end_inset

 cannot measure absolute gene expression, only gene expression as a fraction
 of total reads.
\end_layout

\begin_layout Standard
In 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
ChIP-seq
\end_layout

\end_inset

 data, normalization is not as straightforward.
 The 
\begin_inset Flex Code
status open

\begin_layout Plain Layout
csaw
\end_layout

\end_inset

 package implements several different normalization strategies and provides
 guidance on when to use each one 
\begin_inset CommandInset citation
LatexCommand cite
key "Lun2015a"
literal "false"

\end_inset

.
 Briefly, a typical 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
ChIP-seq
\end_layout

\end_inset

 sample has a bimodal distribution of read counts: a low-abundance mode
 representing background regions and a high-abundance mode representing
 signal regions.
 This offers two mutually incompatible normalization strategies: equalizing
 background coverage or equalizing signal coverage (Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:chipseq-norm-example"
plural "false"
caps "false"
noprefix "false"

\end_inset

).
 If the experiment is well controlled and 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
ChIP
\end_layout

\end_inset

 efficiency is known to be consistent across all samples, then normalizing
 the background coverage to be equal across all samples is a reasonable
 strategy.
 If this is not a safe assumption, then the preferred strategy is to normalize
 the signal regions in a way similar to 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
RNA-seq
\end_layout

\end_inset

 data by assuming that the average signal region is not changing abundance
 between samples.
 Beyond this, if a 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
ChIP-seq
\end_layout

\end_inset

 experiment has a more complicated structure that doesn't show the typical
 bimodal count distribution, it may be necessary to implement a normalization
 as a smooth function of abundance.
 However, this strategy makes a much stronger assumption about the data:
 that the average 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
logFC
\end_layout

\end_inset

 is zero across all abundance levels.
 Hence, the simpler scaling normalization based on background or signal
 regions are generally preferred whenever possible.
\end_layout

\begin_layout Standard
\begin_inset Float figure
wide false
sideways false
status open

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename graphics/CD4-csaw/ChIP-seq/H3K4me2-sample-MAplot-bins-CROP.png
	lyxscale 25
	width 100col%
	groupId colwidth-raster

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout
\begin_inset Argument 1
status collapsed

\begin_layout Plain Layout
Example MA plot of ChIP-seq read counts in 10kb bins for two arbitrary samples.
\end_layout

\end_inset


\begin_inset CommandInset label
LatexCommand label
name "fig:chipseq-norm-example"

\end_inset


\series bold
Example MA plot of ChIP-seq read counts in 10kb bins for two arbitrary samples.
 
\series default
The distribution of bins is bimodal along the x axis (average abundance),
 with the left mode representing 
\begin_inset Quotes eld
\end_inset

background
\begin_inset Quotes erd
\end_inset

 regions with no protein binding and the right mode representing bound regions.
 The modes are also separated on the y axis (logFC), motivating two conflicting
 normalization strategies: background normalization (red) and signal normalizati
on (blue and green, two similar signal normalizations).
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Subsection
ComBat and SVA for correction of known and unknown batch effects
\end_layout

\begin_layout Standard
In addition to well-understood effects that can be easily normalized out,
 a data set often contains confounding biological effects that must be accounted
 for in the modeling step.
 For instance, in an experiment with pre-treatment and post-treatment samples
 of cells from several different donors, donor variability represents a
 known batch effect.
 The most straightforward correction for known batches is to estimate the
 mean for each batch independently and subtract out the differences, so
 that all batches have identical means for each feature.
 However, as with variance estimation, estimating the differences in batch
 means is not necessarily robust at the feature level, so the ComBat method
 adds empirical Bayes squeezing of the batch mean differences toward a common
 value, analogous to 
\begin_inset Flex Code
status open

\begin_layout Plain Layout
limma
\end_layout

\end_inset

's empirical Bayes squeezing of feature variance estimates 
\begin_inset CommandInset citation
LatexCommand cite
key "Johnson2007"
literal "false"

\end_inset

.
 Effectively, ComBat assumes that modest differences between batch means
 are real batch effects, but extreme differences between batch means are
 more likely to be the result of outlier observations that happen to line
 up with the batches rather than a genuine batch effect.
 The result is a batch correction that is more robust against outliers than
 simple subtraction of mean differences.
\end_layout

\begin_layout Standard
In some data sets, unknown batch effects may be present due to inherent
 variability in the data, either caused by technical or biological effects.
 Examples of unknown batch effects include variations in enrichment efficiency
 between 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
ChIP-seq
\end_layout

\end_inset

 samples, variations in populations of different cell types, and the effects
 of uncontrolled environmental factors on gene expression in humans or live
 animals.
 In an ordinary linear model context, unknown batch effects cannot be inferred
 and must be treated as random noise.
 However, in high-throughput experiments, once again information can be
 shared across features to identify patterns of un-modeled variation that
 are repeated in many features.
 One attractive strategy would be to perform 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
SVD
\end_layout

\end_inset

 on the matrix of linear model residuals (which contain all the un-modeled
 variation in the data) and take the first few singular vectors as batch
 effects.
 While this can be effective, it makes the unreasonable assumption that
 all batch effects are completely uncorrelated with any of the effects being
 modeled.
 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
SVA
\end_layout

\end_inset

 starts with this approach, but takes some additional steps to identify
 batch effects in the full data that are both highly correlated with the
 singular vectors in the residuals and least correlated with the effects
 of interest 
\begin_inset CommandInset citation
LatexCommand cite
key "Leek2007"
literal "false"

\end_inset

.
 Since the final batch effects are estimated from the full data, moderate
 correlations between the batch effects and effects of interest are allowed,
 which gives 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
SVA
\end_layout

\end_inset

 much more freedom to estimate the true extent of the batch effects compared
 to simple residual 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
SVD
\end_layout

\end_inset

.
 Once the surrogate variables are estimated, they can be included as coefficient
s in the linear model in a similar fashion to known batch effects in order
 to subtract out their effects on each feature's abundance.
\end_layout

\begin_layout Subsection
Interpreting p-value distributions and estimating false discovery rates
\end_layout

\begin_layout Standard
When testing thousands of genes for differential expression or performing
 thousands of statistical tests for other kinds of genomic data, the result
 is thousands of p-values.
 By construction, p-values have a 
\begin_inset Formula $\mathrm{Uniform}(0,1)$
\end_inset

 distribution under the null hypothesis.
 This means that if all null hypotheses are true in a large number 
\begin_inset Formula $N$
\end_inset

 of tests, then for any significance threshold 
\begin_inset Formula $T$
\end_inset

, approximately 
\begin_inset Formula $N*T$
\end_inset

 p-values would be called 
\begin_inset Quotes eld
\end_inset

significant
\begin_inset Quotes erd
\end_inset

 at that threshold even though the null hypotheses are all true.
 These are called false discoveries.
\end_layout

\begin_layout Standard
When only a fraction of null hypotheses are true, the p-value distribution
 will be a mixture of a uniform component representing the null hypotheses
 that are true and a non-uniform component representing the null hypotheses
 that are not true (Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:Example-pval-hist"
plural "false"
caps "false"
noprefix "false"

\end_inset

).
 The fraction belonging to the uniform component is referred to as 
\begin_inset Formula $\pi_{0}$
\end_inset

, which ranges from 1 (all null hypotheses true) to 0 (all null hypotheses
 false).
 Furthermore, the non-uniform component must be biased toward zero, since
 any evidence against the null hypothesis pushes the p-value for a test
 toward zero.
 We can exploit this fact to estimate the 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
FDR
\end_layout

\end_inset

 for any significance threshold by estimating the degree to which the density
 of p-values left of that threshold exceeds what would be expected for a
 uniform distribution.
 In genomics, the most commonly used 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
FDR
\end_layout

\end_inset

 estimation method, and the one used in this work, is that of 
\begin_inset ERT
status open

\begin_layout Plain Layout


\backslash
glsdisp{BH}{Benjamini and Hochberg}
\end_layout

\end_inset

 
\begin_inset CommandInset citation
LatexCommand cite
key "Benjamini1995"
literal "false"

\end_inset

.
 This is a conservative method that effectively assumes 
\begin_inset Formula $\pi_{0}=1$
\end_inset

.
 Hence it gives an estimated upper bound for the 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
FDR
\end_layout

\end_inset

 at any significance threshold, rather than a point estimate.
 
\end_layout

\begin_layout Standard
\begin_inset Float figure
wide false
sideways false
status collapsed

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename graphics/Intro/med-pval-hist-colored-CROP.pdf
	lyxscale 50
	width 100col%
	groupId colfullwidth

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout
\begin_inset Argument 1
status collapsed

\begin_layout Plain Layout
Example p-value histogram.
\end_layout

\end_inset


\begin_inset CommandInset label
LatexCommand label
name "fig:Example-pval-hist"

\end_inset


\series bold
Example p-value histogram.
 
\series default
The distribution of p-values from a large number of independent tests (such
 as differential expression tests for each gene in the genome) is a mixture
 of a uniform component representing the null hypotheses that are true (blue
 shading) and a zero-biased component representing the null hypotheses that
 are false (red shading).
 The FDR for any column in the histogram is the fraction of that column
 that is blue.
 The line 
\begin_inset Formula $y=\pi_{0}$
\end_inset

 represents the theoretical uniform component of this p-value distribution,
 while the line 
\begin_inset Formula $y=1$
\end_inset

 represents the uniform component when all null hypotheses are true.
 Note that in real data, the true status of each hypothesis is unknown,
 so only the overall shape of the distribution is known.
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Standard
We can also estimate 
\begin_inset Formula $\pi_{0}$
\end_inset

 for the entire distribution of p-values, which can give an idea of the
 overall signal size in the data without setting any significance threshold
 or making any decisions about which specific null hypotheses to reject.
 As 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
FDR
\end_layout

\end_inset

 estimation, there are many methods proposed for estimating 
\begin_inset Formula $\pi_{0}$
\end_inset

.
 The one used in this work is the Phipson method of averaging local 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
FDR
\end_layout

\end_inset

 values 
\begin_inset CommandInset citation
LatexCommand cite
key "Phipson2013Thesis"
literal "false"

\end_inset

.
 Once 
\begin_inset Formula $\pi_{0}$
\end_inset

 is estimated, the number of null hypotheses that are false can be estimated
 as 
\begin_inset Formula $(1-\pi_{0})*N$
\end_inset

.
\end_layout

\begin_layout Standard
Conversely, a p-value distribution that is neither uniform nor zero-biased
 is evidence of a modeling failure.
 Such a distribution would imply that there is less than zero evidence against
 the null hypothesis, which is not possible (in a frequentist setting).
 Attempting to estimate 
\begin_inset Formula $\pi_{0}$
\end_inset

 from such a distribution would yield an estimate greater than 1, a nonsensical
 result.
 The usual cause of a poorly-behaving p-value distribution is a model assumption
 that is violated by the data, such as assuming equal variance between groups
 (homoskedasticity) when the variance of each group is not equal (heteroskedasti
city) or failing to model a strong confounding batch effect.
 In particular, such a p-value distribution is 
\emph on
not 
\emph default
consistent with a simple lack of signal in the data, as this should result
 in a uniform distribution.
 Hence, observing such a p-value distribution should prompt a search for
 violated model assumptions.
\end_layout

\begin_layout Standard
\begin_inset Note Note
status open

\begin_layout Subsection
Factor analysis: PCA, PCoA, MOFA
\end_layout

\begin_layout Plain Layout
\begin_inset Flex TODO Note (inline)
status open

\begin_layout Plain Layout
Not sure if this merits a subsection here.
\end_layout

\end_inset


\end_layout

\begin_layout Itemize
Batch-corrected 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
PCA
\end_layout

\end_inset

 is informative, but careful application is required to avoid bias
\end_layout

\end_inset


\end_layout

\begin_layout Section
Structure of the thesis
\end_layout

\begin_layout Standard
This thesis presents 3 instances of using high-throughput genomic and epigenomic
 assays to investigate hypotheses or solve problems relating to the study
 of transplant rejection.
 In Chapter 
\begin_inset CommandInset ref
LatexCommand ref
reference "chap:CD4-ChIP-seq"
plural "false"
caps "false"
noprefix "false"

\end_inset

, 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
ChIP-seq
\end_layout

\end_inset

 and 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
RNA-seq
\end_layout

\end_inset

 are used to investigate the dynamics of promoter histone methylation as
 it relates to gene expression in T-cell activation and memory.
 Chapter 
\begin_inset CommandInset ref
LatexCommand ref
reference "chap:Improving-array-based-diagnostic"
plural "false"
caps "false"
noprefix "false"

\end_inset

 looks at several array-based assays with the potential to diagnose transplant
 rejection and shows that analyses of this array data are greatly improved
 by paying careful attention to normalization and preprocessing.
 Chapter 
\begin_inset CommandInset ref
LatexCommand ref
reference "chap:Globin-blocking-cyno"
plural "false"
caps "false"
noprefix "false"

\end_inset

 presents a custom method for improving 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
RNA-seq
\end_layout

\end_inset

 of non-human primate blood samples by preventing reverse transcription
 of unwanted globin transcripts.
 Finally, Chapter 
\begin_inset CommandInset ref
LatexCommand ref
reference "chap:Conclusions"
plural "false"
caps "false"
noprefix "false"

\end_inset

 summarizes the overarching lessons and strategies learned through these
 analyses that can be applied to all future analyses of high-throughput
 genomic assays.
\end_layout

\begin_layout Chapter
\begin_inset CommandInset label
LatexCommand label
name "chap:CD4-ChIP-seq"

\end_inset

Reproducible genome-wide epigenetic analysis of H3K4 and H3K27 methylation
 in naïve and memory CD4
\begin_inset Formula $^{+}$
\end_inset

 T-cell activation
\end_layout

\begin_layout Standard

\size large
Ryan C.
 Thompson, Sarah A.
 Lamere, Daniel R.
 Salomon
\end_layout

\begin_layout Standard
\begin_inset ERT
status collapsed

\begin_layout Plain Layout


\backslash
glsresetall
\end_layout

\end_inset


\begin_inset Note Note
status open

\begin_layout Plain Layout
This causes all abbreviations to be reintroduced.
\end_layout

\end_inset


\end_layout

\begin_layout Section
Introduction
\end_layout

\begin_layout Standard
CD4
\begin_inset Formula $^{+}$
\end_inset

 T-cells are central to all adaptive immune responses, as well as immune
 memory 
\begin_inset CommandInset citation
LatexCommand cite
key "Murphy2012"
literal "false"

\end_inset

.
 After an infection is cleared, a subset of the naïve CD4
\begin_inset Formula $^{+}$
\end_inset

 T-cells that responded to that infection differentiate into memory CD4
\begin_inset Formula $^{+}$
\end_inset

 T-cells, which are responsible for responding to the same pathogen in the
 future.
 Memory CD4
\begin_inset Formula $^{+}$
\end_inset

 T-cells are functionally distinct, able to respond to an infection more
 quickly and without the co-stimulation required by naïve CD4
\begin_inset Formula $^{+}$
\end_inset

 T-cells.
 However, the molecular mechanisms underlying this functional distinction
 are not well-understood.
 Epigenetic regulation via histone modification is thought to play an important
 role, but while many studies have looked at static snapshots of histone
 methylation in T-cells, few studies have looked at the dynamics of histone
 regulation after T-cell activation, nor the differences in histone methylation
 between naïve and memory T-cells.
 H3K4me2, H3K4me3 and H3K27me3 are three histone marks thought to be major
 epigenetic regulators of gene expression.
 The goal of the present study is to investigate the role of these histone
 marks in CD4
\begin_inset Formula $^{+}$
\end_inset

 T-cell activation kinetics and memory differentiation.
 In static snapshots, H3K4me2 and H3K4me3 are often observed in the promoters
 of highly transcribed genes, while H3K27me3 is more often observed in promoters
 of inactive genes with little to no transcription occurring.
 As a result, the two H3K4 marks have been characterized as 
\begin_inset Quotes eld
\end_inset

activating
\begin_inset Quotes erd
\end_inset

 marks, while H3K27me3 has been characterized as 
\begin_inset Quotes eld
\end_inset

deactivating
\begin_inset Quotes erd
\end_inset

.
 Despite these characterizations, the actual causal relationship between
 these histone modifications and gene transcription is complex and likely
 involves positive and negative feedback loops between the two.
\end_layout

\begin_layout Section
Approach
\end_layout

\begin_layout Standard
In order to investigate the relationship between gene expression and these
 histone modifications in the context of naïve and memory CD4
\begin_inset Formula $^{+}$
\end_inset

 T-cell activation, a previously published data set of 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
RNA-seq
\end_layout

\end_inset

 data and 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
ChIP-seq
\end_layout

\end_inset

 data was re-analyzed using up-to-date methods designed to address the specific
 analysis challenges posed by this data set.
 The data set contains naïve and memory CD4
\begin_inset Formula $^{+}$
\end_inset

 T-cell samples in a time course before and after activation.
 Like the original analysis, this analysis looks at the dynamics of these
 histone marks and compares them to gene expression dynamics at the same
 time points during activation, as well as compares them between naïve and
 memory cells, in hope of discovering evidence of new mechanistic details
 in the interplay between them.
 The original analysis of this data treated each gene promoter as a monolithic
 unit and mostly assumed that 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
ChIP-seq
\end_layout

\end_inset

 reads or peaks occurring anywhere within a promoter were equivalent, regardless
 of where they occurred relative to the gene structure.
 For an initial analysis of the data, this was a necessary simplifying assumptio
n.
 The current analysis aims to relax this assumption, first by directly analyzing
 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
ChIP-seq
\end_layout

\end_inset

 peaks for differential modification, and second by taking a more granular
 look at the 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
ChIP-seq
\end_layout

\end_inset

 read coverage within promoter regions to ask whether the location of histone
 modifications relative to the gene's 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
TSS
\end_layout

\end_inset

 is an important factor, as opposed to simple proximity.
\end_layout

\begin_layout Section
Methods
\end_layout

\begin_layout Standard
A reproducible workflow was written to analyze the raw 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
ChIP-seq
\end_layout

\end_inset

 and 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
RNA-seq
\end_layout

\end_inset

 data from previous studies (
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
GEO
\end_layout

\end_inset

 accession number 
\begin_inset CommandInset href
LatexCommand href
name "GSE73214"
target "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE73214"
literal "false"

\end_inset

) 
\begin_inset CommandInset citation
LatexCommand cite
key "gh-cd4-csaw,LaMere2015,LaMere2016,LaMere2017"
literal "true"

\end_inset

.
 Briefly, this data consists of 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
RNA-seq
\end_layout

\end_inset

 and 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
ChIP-seq
\end_layout

\end_inset

 from CD4
\begin_inset Formula $^{+}$
\end_inset

 T-cells from 4 donors.
 From each donor, naïve and memory CD4
\begin_inset Formula $^{+}$
\end_inset

 T-cells were isolated separately.
 Then cultures of both cells were activated with CD3/CD28 beads, and samples
 were taken at 4 time points: Day 0 (pre-activation), Day 1 (early activation),
 Day 5 (peak activation), and Day 14 (post-activation).
 For each combination of cell type and time point, RNA was isolated and
 sequenced, and 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
ChIP-seq
\end_layout

\end_inset

 was performed for each of 3 histone marks: H3K4me2, H3K4me3, and H3K27me3.
 The 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
ChIP-seq
\end_layout

\end_inset

 input DNA was also sequenced for each sample.
 The result was 32 samples for each assay.
\end_layout

\begin_layout Subsection
RNA-seq differential expression analysis
\end_layout

\begin_layout Standard
\begin_inset Note Note
status collapsed

\begin_layout Plain Layout
\begin_inset Float figure
wide false
sideways false
status open

\begin_layout Plain Layout
\align center
\begin_inset Float figure
wide false
sideways false
status collapsed

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename graphics/CD4-csaw/rnaseq-compare/ensmebl-vs-entrez-star-CROP.png
	lyxscale 25
	width 35col%
	groupId rna-comp-subfig

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout
STAR quantification, Entrez vs Ensembl gene annotation
\end_layout

\end_inset


\end_layout

\end_inset


\begin_inset space \qquad{}
\end_inset


\begin_inset Float figure
wide false
sideways false
status collapsed

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename graphics/CD4-csaw/rnaseq-compare/ensmebl-vs-entrez-shoal-CROP.png
	lyxscale 25
	width 35col%
	groupId rna-comp-subfig

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout
Salmon+Shoal quantification, Entrez vs Ensembl gene annotation
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Plain Layout
\align center
\begin_inset Float figure
wide false
sideways false
status collapsed

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename graphics/CD4-csaw/rnaseq-compare/star-vs-hisat2-CROP.png
	lyxscale 25
	width 35col%
	groupId rna-comp-subfig

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout
STAR vs HISAT2 quantification, Ensembl gene annotation
\end_layout

\end_inset


\end_layout

\end_inset


\begin_inset space \qquad{}
\end_inset


\begin_inset Float figure
wide false
sideways false
status collapsed

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename graphics/CD4-csaw/rnaseq-compare/star-vs-salmon-CROP.png
	lyxscale 25
	width 35col%
	groupId rna-comp-subfig

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout
Salmon vs STAR quantification, Ensembl gene annotation
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Plain Layout
\align center
\begin_inset Float figure
wide false
sideways false
status collapsed

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename graphics/CD4-csaw/rnaseq-compare/salmon-vs-kallisto-CROP.png
	lyxscale 25
	width 35col%
	groupId rna-comp-subfig

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout
Salmon vs Kallisto quantification, Ensembl gene annotation
\end_layout

\end_inset


\end_layout

\end_inset


\begin_inset space \qquad{}
\end_inset


\begin_inset Float figure
wide false
sideways false
status collapsed

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename graphics/CD4-csaw/rnaseq-compare/salmon-vs-shoal-CROP.png
	lyxscale 25
	width 35col%
	groupId rna-comp-subfig

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout
Salmon+Shoal vs Salmon alone, Ensembl gene annotation
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout
\begin_inset CommandInset label
LatexCommand label
name "fig:RNA-norm-comp"

\end_inset

RNA-seq comparisons
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Standard
Sequence reads were retrieved from the 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
SRA
\end_layout

\end_inset

 
\begin_inset CommandInset citation
LatexCommand cite
key "Leinonen2011"
literal "false"

\end_inset

.
 Five different alignment and quantification methods were tested for the
 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
RNA-seq
\end_layout

\end_inset

 data 
\begin_inset CommandInset citation
LatexCommand cite
key "Dobin2012,Kim2019,Liao2014,Pimentel2016,Patro2017,gh-shoal,gh-hg38-ref"
literal "false"

\end_inset

.
 Each quantification was tested with both Ensembl transcripts and GENCODE
 known gene annotations 
\begin_inset CommandInset citation
LatexCommand cite
key "Zerbino2018,Harrow2012"
literal "false"

\end_inset

.
 Comparisons of downstream results from each combination of quantification
 method and reference revealed that all quantifications gave broadly similar
 results for most genes, with non being obviously superior.
 Salmon quantification with regularization by shoal with the Ensembl annotation
 was chosen as the method theoretically most likely to partially mitigate
 some of the batch effect in the data 
\begin_inset CommandInset citation
LatexCommand cite
key "Patro2017,gh-shoal"
literal "false"

\end_inset

.
\end_layout

\begin_layout Standard
Due to an error in sample preparation, the RNA from the samples for days
 0 and 5 were sequenced using a different kit than those for days 1 and
 14.
 This induced a substantial batch effect in the data due to differences
 in sequencing biases between the two kits, and this batch effect is unfortunate
ly confounded with the time point variable (Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:RNA-PCA-no-batchsub"
plural "false"
caps "false"
noprefix "false"

\end_inset

).
 To do the best possible analysis with this data, this batch effect was
 subtracted out from the data using ComBat 
\begin_inset CommandInset citation
LatexCommand cite
key "Johnson2007"
literal "false"

\end_inset

, ignoring the time point variable due to the confounding with the batch
 variable.
 The result is a marked improvement, but the unavoidable confounding with
 time point means that certain real patterns of gene expression will be
 indistinguishable from the batch effect and subtracted out as a result.
 Specifically, any 
\begin_inset Quotes eld
\end_inset

zig-zag
\begin_inset Quotes erd
\end_inset

 pattern, such as a gene whose expression goes up on day 1, down on day
 5, and back up again on day 14, will be attenuated or eliminated entirely.
 In the context of a T-cell activation time course, it is unlikely that
 many genes of interest will follow such an expression pattern, so this
 loss was deemed an acceptable cost for correcting the batch effect.
\end_layout

\begin_layout Standard
\begin_inset Float figure
wide false
sideways false
status collapsed

\begin_layout Plain Layout
\align center
\begin_inset Float figure
wide false
sideways false
status open

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename graphics/CD4-csaw/RNA-seq/PCA-no-batchsub-CROP.png
	lyxscale 25
	width 75col%
	groupId rna-pca-subfig

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout
\begin_inset CommandInset label
LatexCommand label
name "fig:RNA-PCA-no-batchsub"

\end_inset

Before batch correction
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Plain Layout
\align center
\begin_inset Float figure
wide false
sideways false
status open

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename graphics/CD4-csaw/RNA-seq/PCA-combat-batchsub-CROP.png
	lyxscale 25
	width 75col%
	groupId rna-pca-subfig

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout
\begin_inset CommandInset label
LatexCommand label
name "fig:RNA-PCA-ComBat-batchsub"

\end_inset

After batch correction with ComBat
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout
\begin_inset Argument 1
status collapsed

\begin_layout Plain Layout
PCoA plots of RNA-seq data showing effect of batch correction.
\end_layout

\end_inset


\begin_inset CommandInset label
LatexCommand label
name "fig:RNA-PCA"

\end_inset


\series bold
PCoA plots of RNA-seq data showing effect of batch correction.
 
\series default
The uncorrected data (a) shows a clear separation between samples from the
 two batches (red and blue) dominating the first principal coordinate.
 After correction with ComBat (b), the two batches now have approximately
 the same center, and the first two principal coordinates both show separation
 between experimental conditions rather than batches.
 (Note that time points are shown in hours rather than days in these plots.)
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Standard
However, removing the systematic component of the batch effect still leaves
 the noise component.
 The gene quantifications from the first batch are substantially noisier
 than those in the second batch.
 This analysis corrected for this by using 
\begin_inset Flex Code
status open

\begin_layout Plain Layout
limma
\end_layout

\end_inset

's sample weighting method to assign lower weights to the noisy samples
 of batch 1 (Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:RNA-seq-weights-vs-covars"
plural "false"
caps "false"
noprefix "false"

\end_inset

) 
\begin_inset CommandInset citation
LatexCommand cite
key "Ritchie2006,Liu2015"
literal "false"

\end_inset

.
 The resulting analysis gives an accurate assessment of statistical significance
 for all comparisons, which unfortunately means a loss of statistical power
 for comparisons involving samples in batch 1.
\end_layout

\begin_layout Standard
In any case, the 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
RNA-seq
\end_layout

\end_inset

 counts were first normalized using 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
TMM
\end_layout

\end_inset

 
\begin_inset CommandInset citation
LatexCommand cite
key "Robinson2010"
literal "false"

\end_inset

, converted to normalized 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
logCPM
\end_layout

\end_inset

 with quality weights using 
\begin_inset Flex Code
status open

\begin_layout Plain Layout
voomWithQualityWeights
\end_layout

\end_inset

 
\begin_inset CommandInset citation
LatexCommand cite
key "Law2014,Liu2015"
literal "false"

\end_inset

, and batch-corrected at this point using ComBat.
 A linear model was fit to the batch-corrected, quality-weighted data for
 each gene using 
\begin_inset Flex Code
status open

\begin_layout Plain Layout
limma
\end_layout

\end_inset

, and each gene was tested for differential expression using 
\begin_inset Flex Code
status open

\begin_layout Plain Layout
limma
\end_layout

\end_inset

's empirical Bayes moderated 
\begin_inset Formula $t$
\end_inset

-test 
\begin_inset CommandInset citation
LatexCommand cite
key "Smyth2005,Law2014,Phipson2016"
literal "false"

\end_inset

.
 P-values were corrected for multiple testing using the 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
BH
\end_layout

\end_inset

 procedure for 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
FDR
\end_layout

\end_inset

 control 
\begin_inset CommandInset citation
LatexCommand cite
key "Benjamini1995"
literal "false"

\end_inset

.
\end_layout

\begin_layout Standard
\begin_inset Float figure
wide false
sideways false
status open

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename graphics/CD4-csaw/RNA-seq/weights-vs-covars-nobcv-CROP.png
	lyxscale 25
	width 100col%
	groupId colwidth-raster

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout
\begin_inset Argument 1
status collapsed

\begin_layout Plain Layout
RNA-seq sample weights, grouped by experimental and technical covariates.
\end_layout

\end_inset


\begin_inset CommandInset label
LatexCommand label
name "fig:RNA-seq-weights-vs-covars"

\end_inset


\series bold
RNA-seq sample weights, grouped by experimental and technical covariates.
 
\series default
Inverse variance weights were estimated for each sample using 
\begin_inset Flex Code
status open

\begin_layout Plain Layout
limma
\end_layout

\end_inset

's 
\begin_inset Flex Code
status open

\begin_layout Plain Layout
arrayWeights
\end_layout

\end_inset

 function (part of 
\begin_inset Flex Code
status open

\begin_layout Plain Layout
voomWithQualityWeights
\end_layout

\end_inset

).
 The samples were grouped by each known covariate and the distribution of
 weights was plotted for each group.
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Subsection
ChIP-seq analyses
\end_layout

\begin_layout Standard
Sequence reads were retrieved from 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
SRA
\end_layout

\end_inset

 
\begin_inset CommandInset citation
LatexCommand cite
key "Leinonen2011"
literal "false"

\end_inset

.
 
\begin_inset Flex Glossary Term (Capital)
status open

\begin_layout Plain Layout
ChIP-seq
\end_layout

\end_inset

 (and input) reads were aligned to the 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
GRCh38
\end_layout

\end_inset

 genome assembly using Bowtie 2 
\begin_inset CommandInset citation
LatexCommand cite
key "Langmead2012,Schneider2017,gh-hg38-ref"
literal "false"

\end_inset

.
 Artifact regions were annotated using a custom implementation of the 
\begin_inset Flex Code
status open

\begin_layout Plain Layout
GreyListChIP
\end_layout

\end_inset

 algorithm, and these 
\begin_inset Quotes eld
\end_inset

greylists
\begin_inset Quotes erd
\end_inset

 were merged with the published 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
ENCODE
\end_layout

\end_inset

 blacklists 
\begin_inset CommandInset citation
LatexCommand cite
key "greylistchip,Dunham2012,Amemiya2019,gh-cd4-csaw"
literal "false"

\end_inset

.
 Any read or called peak overlapping one of these regions was regarded as
 artifactual and excluded from downstream analyses.
 Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:CCF-master"
plural "false"
caps "false"
noprefix "false"

\end_inset

 shows the improvement after blacklisting in the strand cross-correlation
 plots, a common quality control plot for 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
ChIP-seq
\end_layout

\end_inset

 data 
\begin_inset CommandInset citation
LatexCommand cite
key "Kharchenko2008,Lun2015a"
literal "false"

\end_inset

.
 Peaks were called using 
\begin_inset Flex Code
status open

\begin_layout Plain Layout
epic
\end_layout

\end_inset

, an implementation of the 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
SICER
\end_layout

\end_inset

 algorithm 
\begin_inset CommandInset citation
LatexCommand cite
key "Zang2009,gh-epic"
literal "false"

\end_inset

.
 Peaks were also called separately using 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
MACS
\end_layout

\end_inset

, but 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
MACS
\end_layout

\end_inset

 was determined to be a poor fit for the data, and these peak calls are
 not used in any further analyses 
\begin_inset CommandInset citation
LatexCommand cite
key "Zhang2008"
literal "false"

\end_inset

.
 Consensus peaks were determined by applying the 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
IDR
\end_layout

\end_inset

 framework 
\begin_inset CommandInset citation
LatexCommand cite
key "Li2011,gh-idr"
literal "false"

\end_inset

 to find peaks consistently called in the same locations across all 4 donors.
\end_layout

\begin_layout Standard
\begin_inset ERT
status open

\begin_layout Plain Layout


\backslash
afterpage{
\end_layout

\begin_layout Plain Layout


\backslash
begin{landscape}
\end_layout

\end_inset


\end_layout

\begin_layout Standard
\begin_inset Float figure
wide false
sideways false
status collapsed

\begin_layout Plain Layout
\align center
\begin_inset Float figure
wide false
sideways false
status open

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename graphics/CD4-csaw/csaw/CCF-plots-noBL-PAGE2-CROP.pdf
	lyxscale 75
	width 47col%
	groupId ccf-subfig

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout

\series bold
\begin_inset CommandInset label
LatexCommand label
name "fig:CCF-without-blacklist"

\end_inset

Cross-correlation plots without removing blacklisted reads.
 
\series default
Without blacklisting, many artifactual peaks are visible in the cross-correlatio
ns of the ChIP-seq samples, and the peak at the true fragment size (147
\begin_inset space ~
\end_inset

bp) is frequently overshadowed by the artifactual peak at the read length
 (100
\begin_inset space ~
\end_inset

bp).
\end_layout

\end_inset


\end_layout

\end_inset


\begin_inset space \hfill{}
\end_inset


\begin_inset Float figure
wide false
sideways false
status collapsed

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename graphics/CD4-csaw/csaw/CCF-plots-PAGE2-CROP.pdf
	lyxscale 75
	width 47col%
	groupId ccf-subfig

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout

\series bold
\begin_inset CommandInset label
LatexCommand label
name "fig:CCF-with-blacklist"

\end_inset

Cross-correlation plots with blacklisted reads removed.

\series default
 After blacklisting, most ChIP-seq samples have clean-looking periodic cross-cor
relation plots, with the largest peak around 147
\begin_inset space ~
\end_inset

bp, the expected size for a fragment of DNA from a single nucleosome, and
 little to no peak at the read length, 100
\begin_inset space ~
\end_inset

bp.
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Flex TODO Note (inline)
status open

\begin_layout Plain Layout
Figure font too small
\end_layout

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout
\begin_inset Argument 1
status collapsed

\begin_layout Plain Layout
Strand cross-correlation plots for ChIP-seq data, before and after blacklisting.
\end_layout

\end_inset


\begin_inset CommandInset label
LatexCommand label
name "fig:CCF-master"

\end_inset


\series bold
Strand cross-correlation plots for ChIP-seq data, before and after blacklisting.
 
\series default
The number of reads starting at each position in the genome was counted
 separately for the plus and minus strands, and then the correlation coefficient
 between the read start counts for both strands (cross-correlation) was
 computed after shifting the plus strand counts forward by a specified interval
 (the delay).
 This was repeated for every delay value from 0 to 1000, and the cross-correlati
on values were plotted as a function of the delay.
 In good quality samples, cross-correlation is maximized when the delay
 equals the fragment size; in poor quality samples, cross-correlation is
 often maximized when the delay equals the read length, an artifactual peak
 whose cause is not fully understood.
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Standard
\begin_inset ERT
status open

\begin_layout Plain Layout


\backslash
end{landscape}
\end_layout

\begin_layout Plain Layout

}
\end_layout

\end_inset


\end_layout

\begin_layout Standard
Promoters were defined by computing the distance from each annotated 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
TSS
\end_layout

\end_inset

 to the nearest called peak and examining the distribution of distances,
 observing that peaks for each histone mark were enriched within a certain
 distance of the 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
TSS
\end_layout

\end_inset

.
 (Note: this analysis was performed using the original peak calls and expression
 values from 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
GEO
\end_layout

\end_inset

 
\begin_inset CommandInset citation
LatexCommand cite
key "LaMere2016"
literal "false"

\end_inset

.) For H3K4me2 and H3K4me3, this distance was about 1
\begin_inset space ~
\end_inset

kbp, while for H3K27me3 it was 2.5
\begin_inset space ~
\end_inset

kbp.
 These distances were used as an 
\begin_inset Quotes eld
\end_inset

effective promoter radius
\begin_inset Quotes erd
\end_inset

 for each mark.
 The promoter region for each gene was defined as the region of the genome
 within this distance upstream or downstream of the gene's annotated 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
TSS
\end_layout

\end_inset

.
 For genes with multiple annotated 
\begin_inset Flex Glossary Term (pl)
status open

\begin_layout Plain Layout
TSS
\end_layout

\end_inset

, a promoter region was defined for each 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
TSS
\end_layout

\end_inset

 individually, and any promoters that overlapped (due to multiple 
\begin_inset Flex Glossary Term (pl)
status open

\begin_layout Plain Layout
TSS
\end_layout

\end_inset

 being closer than 2 times the radius) were merged into one large promoter.
 Thus, some genes had multiple promoters defined, which were each analyzed
 separately for differential modification.
\end_layout

\begin_layout Standard
Reads in promoters, peaks, and sliding windows across the genome were counted
 and normalized using 
\begin_inset Flex Code
status open

\begin_layout Plain Layout
csaw
\end_layout

\end_inset

 and analyzed for differential modification using 
\begin_inset Flex Code
status open

\begin_layout Plain Layout
edgeR
\end_layout

\end_inset

 
\begin_inset CommandInset citation
LatexCommand cite
key "Lun2014,Lun2015a,Lund2012,Phipson2016"
literal "false"

\end_inset

.
 Unobserved confounding factors in the 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
ChIP-seq
\end_layout

\end_inset

 data were corrected using 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
SVA
\end_layout

\end_inset

 
\begin_inset CommandInset citation
LatexCommand cite
key "Leek2007,Leek2014"
literal "false"

\end_inset

.
 Principal coordinate plots of the promoter count data for each histone
 mark before and after subtracting surrogate variable effects are shown
 in Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:PCoA-ChIP"
plural "false"
caps "false"
noprefix "false"

\end_inset

.
\end_layout

\begin_layout Standard
\begin_inset Float figure
wide false
sideways false
status collapsed

\begin_layout Plain Layout
\begin_inset Float figure
wide false
sideways false
status open

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename graphics/CD4-csaw/ChIP-seq/H3K4me2-PCA-raw-CROP.png
	lyxscale 25
	width 45col%
	groupId pcoa-subfig

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout

\series bold
\begin_inset CommandInset label
LatexCommand label
name "fig:PCoA-H3K4me2-bad"

\end_inset

H3K4me2, no correction
\end_layout

\end_inset


\end_layout

\end_inset


\begin_inset space \hfill{}
\end_inset


\begin_inset Float figure
wide false
sideways false
status open

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename graphics/CD4-csaw/ChIP-seq/H3K4me2-PCA-SVsub-CROP.png
	lyxscale 25
	width 45col%
	groupId pcoa-subfig

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout

\series bold
\begin_inset CommandInset label
LatexCommand label
name "fig:PCoA-H3K4me2-good"

\end_inset

H3K4me2, SVs subtracted
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Float figure
wide false
sideways false
status collapsed

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename graphics/CD4-csaw/ChIP-seq/H3K4me3-PCA-raw-CROP.png
	lyxscale 25
	width 45col%
	groupId pcoa-subfig

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout

\series bold
\begin_inset CommandInset label
LatexCommand label
name "fig:PCoA-H3K4me3-bad"

\end_inset

H3K4me3, no correction
\end_layout

\end_inset


\end_layout

\end_inset


\begin_inset space \hfill{}
\end_inset


\begin_inset Float figure
wide false
sideways false
status collapsed

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename graphics/CD4-csaw/ChIP-seq/H3K4me3-PCA-SVsub-CROP.png
	lyxscale 25
	width 45col%
	groupId pcoa-subfig

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout

\series bold
\begin_inset CommandInset label
LatexCommand label
name "fig:PCoA-H3K4me3-good"

\end_inset

H3K4me3, SVs subtracted
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Float figure
wide false
sideways false
status collapsed

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename graphics/CD4-csaw/ChIP-seq/H3K27me3-PCA-raw-CROP.png
	lyxscale 25
	width 45col%
	groupId pcoa-subfig

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout

\series bold
\begin_inset CommandInset label
LatexCommand label
name "fig:PCoA-H3K27me3-bad"

\end_inset

H3K27me3, no correction
\end_layout

\end_inset


\end_layout

\end_inset


\begin_inset space \hfill{}
\end_inset


\begin_inset Float figure
wide false
sideways false
status collapsed

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename graphics/CD4-csaw/ChIP-seq/H3K27me3-PCA-SVsub-CROP.png
	lyxscale 25
	width 45col%
	groupId pcoa-subfig

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout

\series bold
\begin_inset CommandInset label
LatexCommand label
name "fig:PCoA-H3K27me3-good"

\end_inset

H3K27me3, SVs subtracted
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Flex TODO Note (inline)
status collapsed

\begin_layout Plain Layout
Figure font too small
\end_layout

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout
\begin_inset Argument 1
status collapsed

\begin_layout Plain Layout
PCoA plots of ChIP-seq sliding window data, before and after subtracting
 surrogate variables.
\end_layout

\end_inset


\begin_inset CommandInset label
LatexCommand label
name "fig:PCoA-ChIP"

\end_inset


\series bold
PCoA plots of ChIP-seq sliding window data, before and after subtracting
 surrogate variables (SVs).
 
\series default
For each histone mark, a PCoA plot of the first 2 principal coordinates
 was created before and after subtraction of SV effects.
 Time points are shown by color and cell type by shape, and samples from
 the same time point and cell type are enclosed in a shaded area to aid
 in visial recognition (this shaded area has no meaning on the plot).
 Samples of the same cell type from the same donor are connected with a
 line in time point order, showing the 
\begin_inset Quotes eld
\end_inset

trajectory
\begin_inset Quotes erd
\end_inset

 of each donor's samples over time.
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Standard
To investigate whether the location of a peak within the promoter region
 was important, 
\begin_inset Quotes eld
\end_inset

relative coverage profiles
\begin_inset Quotes erd
\end_inset

 were generated.
 First, 500-bp sliding windows were tiled around each annotated 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
TSS
\end_layout

\end_inset

: one window centered on the 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
TSS
\end_layout

\end_inset

 itself, and 10 windows each upstream and downstream, thus covering a 10.5-kb
 region centered on the 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
TSS
\end_layout

\end_inset

 with a total of 21 windows.
 Reads in each window for each 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
TSS
\end_layout

\end_inset

 were counted in each sample, and the counts were normalized and converted
 to 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
logCPM
\end_layout

\end_inset

 as in the differential modification analysis.
 An abundance threshold was chosen such that 99% of peak-containing promoters
 have an average 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
logCPM
\end_layout

\end_inset

 above this threshold (Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:Promoter-abundance-filtering"
plural "false"
caps "false"
noprefix "false"

\end_inset

).
 Then 
\emph on
all 
\emph default
promoters with an average 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
logCPM
\end_layout

\end_inset

 above this threshold were included, and all below that thereshold were
 filtered out, regardless of whether they actually contained a called peak.
 This ensures that even promoters containing undetected peaks will be included,
 at the cost of likely including many promoters that do not contain any
 true peak.
 Then, the 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
logCPM
\end_layout

\end_inset

 values of the bins within each promoter were normalized to an average of
 zero, such that each window's normalized abundance now represents the relative
 read depth of that window compared to all other windows in the same promoter.
 The normalized abundance values for each window in a promoter are collectively
 referred to as that promoter's 
\begin_inset Quotes eld
\end_inset

relative coverage profile
\begin_inset Quotes erd
\end_inset

.
\end_layout

\begin_layout Standard
\begin_inset ERT
status open

\begin_layout Plain Layout


\backslash
afterpage{
\end_layout

\begin_layout Plain Layout


\backslash
begin{landscape}
\end_layout

\end_inset


\end_layout

\begin_layout Standard
\begin_inset Float figure
wide false
sideways false
status open

\begin_layout Plain Layout
\align center
\begin_inset Float figure
wide false
sideways false
status collapsed

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename graphics/CD4-csaw/ChIP-seq/H3K4me2-neighborhood-logCPM-filter.png
	lyxscale 25
	width 30col%
	groupId nhood-filter-subfig

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout
H3K4me2
\end_layout

\end_inset


\end_layout

\end_inset


\begin_inset space \hfill{}
\end_inset


\begin_inset Float figure
wide false
sideways false
status collapsed

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename graphics/CD4-csaw/ChIP-seq/H3K4me3-neighborhood-logCPM-filter.png
	lyxscale 25
	width 30col%
	groupId nhood-filter-subfig

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout
H3K4me3
\end_layout

\end_inset


\end_layout

\end_inset


\begin_inset space \hfill{}
\end_inset


\begin_inset Float figure
wide false
sideways false
status collapsed

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename graphics/CD4-csaw/ChIP-seq/H3K27me3-neighborhood-logCPM-filter.png
	lyxscale 25
	width 30col%
	groupId nhood-filter-subfig

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout
H3K27me3
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout
\begin_inset Argument 1
status collapsed

\begin_layout Plain Layout
Promoter abundance filtering for relative coverage profiles.
\end_layout

\end_inset


\begin_inset CommandInset label
LatexCommand label
name "fig:Promoter-abundance-filtering"

\end_inset


\series bold
Promoter abundance filtering for relative coverage profiles.

\series default
 For each histone mark, a histogram of promoter logCPM values was plotted,
 colored by whether each promoter contains a called peak.
 The abundance filter for each histone mark (dotted vertical line) was set
 such that 99% of peak-containing promoters (blue) are above the threshold,
 and then all promoters above this threshold were included in downstream
 analyses.
\end_layout

\end_inset


\end_layout

\begin_layout Plain Layout

\end_layout

\end_inset


\end_layout

\begin_layout Standard
\begin_inset ERT
status open

\begin_layout Plain Layout


\backslash
end{landscape}
\end_layout

\begin_layout Plain Layout

}
\end_layout

\end_inset


\end_layout

\begin_layout Subsection
MOFA analysis of cross-dataset variation patterns
\end_layout

\begin_layout Standard
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
MOFA
\end_layout

\end_inset

 was run on all the 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
ChIP-seq
\end_layout

\end_inset

 windows overlapping consensus peaks for each histone mark, as well as the
 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
RNA-seq
\end_layout

\end_inset

 data, in order to identify patterns of coordinated variation across all
 data sets 
\begin_inset CommandInset citation
LatexCommand cite
key "Argelaguet2018"
literal "false"

\end_inset

.
 The results are summarized in Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:MOFA-master"
plural "false"
caps "false"
noprefix "false"

\end_inset

.
 
\begin_inset Flex Glossary Term (Capital, pl)
status open

\begin_layout Plain Layout
LF
\end_layout

\end_inset

 1, 4, and 5 were determined to explain the most variation consistently
 across all data sets (Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:mofa-varexplained"
plural "false"
caps "false"
noprefix "false"

\end_inset

), and scatter plots of these factors show that they also correlate best
 with the experimental factors (Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:mofa-lf-scatter"
plural "false"
caps "false"
noprefix "false"

\end_inset

).
 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
LF
\end_layout

\end_inset

2 captures the batch effect in the 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
RNA-seq
\end_layout

\end_inset

 data.
 Removing the effect of 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
LF
\end_layout

\end_inset

2 using 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
MOFA
\end_layout

\end_inset

 theoretically yields a batch correction that does not depend on knowing
 the experimental factors.
 When this was attempted, the resulting batch correction was comparable
 to ComBat (see Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:RNA-PCA-ComBat-batchsub"
plural "false"
caps "false"
noprefix "false"

\end_inset

), indicating that the ComBat-based batch correction has little room for
 improvement given the problems with the data set.
\end_layout

\begin_layout Standard
\begin_inset ERT
status open

\begin_layout Plain Layout


\backslash
afterpage{
\end_layout

\begin_layout Plain Layout


\backslash
begin{landscape}
\end_layout

\end_inset


\end_layout

\begin_layout Standard
\begin_inset Float figure
wide false
sideways false
status open

\begin_layout Plain Layout
\begin_inset Float figure
wide false
sideways false
status collapsed

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename graphics/CD4-csaw/MOFA-varExplained-matrix-CROP.png
	lyxscale 25
	height 70theight%
	groupId mofa-subfig

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout

\series bold
\begin_inset CommandInset label
LatexCommand label
name "fig:mofa-varexplained"

\end_inset

Variance explained in each data set by each latent factor estimated by MOFA.

\series default
 For each LF learned by MOFA, the variance explained by that factor in each
 data set (
\begin_inset Quotes eld
\end_inset

view
\begin_inset Quotes erd
\end_inset

) is shown by the shading of the cells in the lower section.
 The upper section shows the total fraction of each data set's variance
 that is explained by all LFs combined.
\end_layout

\end_inset


\end_layout

\end_inset


\begin_inset space \hfill{}
\end_inset


\begin_inset Float figure
wide false
sideways false
status collapsed

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename graphics/CD4-csaw/MOFA-LF-scatter-small.png
	lyxscale 25
	height 70theight%
	groupId mofa-subfig

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout

\series bold
\begin_inset CommandInset label
LatexCommand label
name "fig:mofa-lf-scatter"

\end_inset

Scatter plots of specific pairs of MOFA latent factors.

\series default
 LFs 1, 4, and 5 explain substantial variation in all data sets, so they
 were plotted against each other in order to reveal patterns of variation
 that are shared across all data sets.
 These plots can be interpreted similarly to PCA and PCoA plots.
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout
\begin_inset Argument 1
status collapsed

\begin_layout Plain Layout
MOFA latent factors identify shared patterns of variation.
\end_layout

\end_inset


\begin_inset CommandInset label
LatexCommand label
name "fig:MOFA-master"

\end_inset


\series bold
MOFA latent factors identify shared patterns of variation.
 
\series default
MOFA was used to estimate latent factors (LFs) that explain substantial
 variation in the RNA-seq data and the ChIP-seq data (a).
 Then specific LFs of interest were selected and plotted (b).
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Standard
\begin_inset ERT
status open

\begin_layout Plain Layout


\backslash
end{landscape}
\end_layout

\begin_layout Plain Layout

}
\end_layout

\end_inset


\end_layout

\begin_layout Standard
\begin_inset Note Note
status collapsed

\begin_layout Plain Layout
\begin_inset Float figure
wide false
sideways false
status open

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename graphics/CD4-csaw/MOFA-batch-correct-CROP.png
	lyxscale 25
	width 100col%
	groupId colwidth-raster

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout

\series bold
\begin_inset CommandInset label
LatexCommand label
name "fig:mofa-batchsub"

\end_inset

Result of RNA-seq batch-correction using MOFA latent factors
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Section
Results
\end_layout

\begin_layout Subsection
Interpretation of RNA-seq analysis is limited by a major confounding factor
\end_layout

\begin_layout Standard
Genes called as present in the 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
RNA-seq
\end_layout

\end_inset

 data were tested for differential expression between all time points and
 cell types.
 The counts of differentially expressed genes are shown in Table 
\begin_inset CommandInset ref
LatexCommand ref
reference "tab:Estimated-and-detected-rnaseq"
plural "false"
caps "false"
noprefix "false"

\end_inset

.
 Notably, all the results for Day 0 and Day 5 have substantially fewer genes
 called differentially expressed than any of the results for other time
 points.
 This is an unfortunate result of the difference in sample quality between
 the two batches of 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
RNA-seq
\end_layout

\end_inset

 data.
 All the samples in Batch 1, which includes all the samples from Days 0
 and 5, have substantially more variability than the samples in Batch 2,
 which includes the other time points.
 This is reflected in the substantially higher weights assigned to Batch
 2 (Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:RNA-seq-weights-vs-covars"
plural "false"
caps "false"
noprefix "false"

\end_inset

).
 The batch effect has both a systematic component and a random noise component.
 While the systematic component was subtracted out using ComBat (Figure
 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:RNA-PCA"
plural "false"
caps "false"
noprefix "false"

\end_inset

), no such correction is possible for the noise component: Batch 1 simply
 has substantially more random noise in it, which reduces the statistical
 power for any differential expression tests involving samples in that batch.
 
\begin_inset Float table
wide false
sideways false
status collapsed

\begin_layout Plain Layout
\align center
\begin_inset Tabular
<lyxtabular version="3" rows="11" columns="3">
<features tabularvalignment="middle">
<column alignment="center" valignment="top">
<column alignment="center" valignment="top">
<column alignment="center" valignment="top">
<row>
<cell alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
Test
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
Est.
 non-null
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" rightline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
\begin_inset Formula $\mathrm{FDR}\le10\%$
\end_inset


\end_layout

\end_inset
</cell>
</row>
<row>
<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
Naïve Day 0 vs Day 1
\end_layout

\end_inset
</cell>
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\begin_layout Plain Layout
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\begin_layout Plain Layout
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\end_layout

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</cell>
</row>
<row>
<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
Naïve Day 0 vs Day 5
\end_layout

\end_inset
</cell>
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\begin_layout Plain Layout
3038
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\begin_inset Text

\begin_layout Plain Layout
Naïve Day 0 vs Day 14
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\begin_inset Text

\begin_layout Plain Layout
Memory Day 0 vs Day 1
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\end_inset
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\begin_layout Plain Layout
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\begin_inset Text

\begin_layout Plain Layout
Memory Day 0 vs Day 5
\end_layout

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\begin_layout Plain Layout
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\begin_layout Plain Layout
Memory Day 0 vs Day 14
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<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
Day 0 Naïve vs Memory
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<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
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\begin_layout Plain Layout
Day 1 Naïve vs Memory
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\begin_layout Plain Layout
9167
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\begin_layout Plain Layout
5532
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<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
Day 5 Naïve vs Memory
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
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\begin_layout Plain Layout
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<cell alignment="center" valignment="top" topline="true" leftline="true" rightline="true" usebox="none">
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\begin_layout Plain Layout
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\end_layout

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</cell>
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<row>
<cell alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
Day 14 Naïve vs Memory
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
6446
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" rightline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
2319
\end_layout

\end_inset
</cell>
</row>
</lyxtabular>

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout
\begin_inset Argument 1
status collapsed

\begin_layout Plain Layout
Estimated and detected differentially expressed genes.
\end_layout

\end_inset


\begin_inset CommandInset label
LatexCommand label
name "tab:Estimated-and-detected-rnaseq"

\end_inset


\series bold
Estimated and detected differentially expressed genes.

\series default
 
\begin_inset Quotes eld
\end_inset

Test
\begin_inset Quotes erd
\end_inset

: Which sample groups were compared; 
\begin_inset Quotes eld
\end_inset

Est non-null
\begin_inset Quotes erd
\end_inset

: Estimated number of differentially expressed genes, using the method of
 averaging local FDR values 
\begin_inset CommandInset citation
LatexCommand cite
key "Phipson2013Thesis"
literal "false"

\end_inset

; 
\begin_inset Quotes eld
\end_inset


\begin_inset Formula $\mathrm{FDR}\le10\%$
\end_inset


\begin_inset Quotes erd
\end_inset

: Number of significantly differentially expressed genes at an FDR threshold
 of 10%.
 The total number of genes tested was 16707.
\end_layout

\end_inset


\end_layout

\end_inset


\begin_inset Note Note
status collapsed

\begin_layout Plain Layout
If float lost issues, reposition randomly until success.
\end_layout

\end_inset

 
\end_layout

\begin_layout Standard
Despite the difficulty in detecting specific differentially expressed genes,
 there is still evidence that differential expression is present for these
 time points.
 In Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:rna-pca-final"
plural "false"
caps "false"
noprefix "false"

\end_inset

, there is a clear separation between naïve and memory samples at Day 0,
 despite the fact that only 2 genes were significantly differentially expressed
 for this comparison.
 Similarly, the small numbers of genes detected for the Day 0 vs Day 5 compariso
ns do not reflect the large separation between these time points in Figure
 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:rna-pca-final"
plural "false"
caps "false"
noprefix "false"

\end_inset

.
 In addition, the 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
MOFA
\end_layout

\end_inset

 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
LF
\end_layout

\end_inset

 plots in Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:mofa-lf-scatter"
plural "false"
caps "false"
noprefix "false"

\end_inset

.
 This suggests that there is indeed a differential expression signal present
 in the data for these comparisons, but the large variability in the Batch
 1 samples obfuscates this signal at the individual gene level.
 As a result, it is impossible to make any meaningful statements about the
 
\begin_inset Quotes eld
\end_inset

size
\begin_inset Quotes erd
\end_inset

 of the gene signature for any time point, since the number of significant
 genes as well as the estimated number of differentially expressed genes
 depends so strongly on the variations in sample quality in addition to
 the size of the differential expression signal in the data.
 Gene-set enrichment analyses are similarly impractical.
 However, analyses looking at genome-wide patterns of expression are still
 practical.
\end_layout

\begin_layout Standard
\begin_inset Float figure
wide false
sideways false
status collapsed

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename graphics/CD4-csaw/RNA-seq/PCA-final-12-CROP.png
	lyxscale 25
	width 100col%
	groupId colwidth-raster

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout
\begin_inset Argument 1
status collapsed

\begin_layout Plain Layout
PCoA plot of RNA-seq samples after ComBat batch correction.
\end_layout

\end_inset


\begin_inset CommandInset label
LatexCommand label
name "fig:rna-pca-final"

\end_inset


\series bold
PCoA plot of RNA-seq samples after ComBat batch correction.
 
\series default
Each point represents an individual sample.
 Samples with the same combination of cell type and time point are encircled
 with a shaded region to aid in visual identification of the sample groups.
 Samples of the same cell type from the same donor are connected by lines
 to indicate the 
\begin_inset Quotes eld
\end_inset

trajectory
\begin_inset Quotes erd
\end_inset

 of each donor's cells over time in PCoA space.
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Subsection
H3K4 and H3K27 methylation occur in broad regions and are enriched near
 promoters
\end_layout

\begin_layout Standard
\begin_inset Float table
wide false
sideways false
status collapsed

\begin_layout Plain Layout
\align center
\begin_inset Flex TODO Note (inline)
status open

\begin_layout Plain Layout
Also get 
\emph on
median
\emph default
 peak width and maybe other quantiles (25%, 75%)
\end_layout

\end_inset


\end_layout

\begin_layout Plain Layout
\align center
\begin_inset Tabular
<lyxtabular version="3" rows="4" columns="5">
<features tabularvalignment="middle">
<column alignment="center" valignment="top">
<column alignment="center" valignment="top">
<column alignment="center" valignment="top">
<column alignment="center" valignment="top">
<column alignment="center" valignment="top">
<row>
<cell alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
Histone Mark
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
# Peaks
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
Mean peak width
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
genome coverage
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" rightline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
FRiP
\end_layout

\end_inset
</cell>
</row>
<row>
<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
H3K4me2
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
14,965
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
3,970
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
1.92%
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" leftline="true" rightline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
14.2%
\end_layout

\end_inset
</cell>
</row>
<row>
<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
H3K4me3
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
6,163
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
2,946
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
0.588%
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" leftline="true" rightline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
6.57%
\end_layout

\end_inset
</cell>
</row>
<row>
<cell alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
H3K27me3
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
18,139
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
18,967
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
11.1%
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" rightline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
22.5%
\end_layout

\end_inset
</cell>
</row>
</lyxtabular>

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout
\begin_inset Argument 1
status collapsed

\begin_layout Plain Layout
Summary of peak-calling statistics.
\end_layout

\end_inset


\begin_inset CommandInset label
LatexCommand label
name "tab:peak-calling-summary"

\end_inset


\series bold
Summary of peak-calling statistics.
 
\series default
For each histone mark, the number of peaks called using SICER at an IDR
 threshold of 0.05, the mean width of those peaks, the fraction of the genome
 covered by peaks, and the fraction of reads in peaks (FRiP).
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Standard
Table 
\begin_inset CommandInset ref
LatexCommand ref
reference "tab:peak-calling-summary"
plural "false"
caps "false"
noprefix "false"

\end_inset

 gives a summary of the peak calling statistics for each histone mark.
 Consistent with previous observations, all 3 histone marks occur in broad
 regions spanning many consecutive nucleosomes, rather than in sharp peaks
 as would be expected for a transcription factor or other molecule that
 binds to specific sites.
 This conclusion is further supported by Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:CCF-with-blacklist"
plural "false"
caps "false"
noprefix "false"

\end_inset

, in which a clear nucleosome-sized periodicity is visible in the cross-correlat
ion value for each sample, indicating that each time a given mark is present
 on one histone, it is also likely to be found on adjacent histones as well.
 H3K27me3 enrichment in particular is substantially more broad than either
 H3K4 mark, with a mean peak width of almost 19,000 bp.
 This is also reflected in the periodicity observed in Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:CCF-with-blacklist"
plural "false"
caps "false"
noprefix "false"

\end_inset

, which remains strong much farther out for H3K27me3 than the other marks,
 showing H3K27me3 especially tends to be found on long runs of consecutive
 histones.
\end_layout

\begin_layout Standard
All 3 histone marks tend to occur more often near promoter regions, as shown
 in Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:near-promoter-peak-enrich"
plural "false"
caps "false"
noprefix "false"

\end_inset

.
 The majority of each density distribution is flat, representing the background
 density of peaks genome-wide.
 Each distribution has a peak near zero, representing an enrichment of peaks
 close to 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
TSS
\end_layout

\end_inset

 positions relative to the remainder of the genome.
 Interestingly, the 
\begin_inset Quotes eld
\end_inset

radius
\begin_inset Quotes erd
\end_inset

 within which this enrichment occurs is not the same for every histone mark
 (Table 
\begin_inset CommandInset ref
LatexCommand ref
reference "tab:effective-promoter-radius"
plural "false"
caps "false"
noprefix "false"

\end_inset

).
 For H3K4me2 and H3K4me3, peaks are most enriched within 1
\begin_inset space ~
\end_inset

kbp of 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
TSS
\end_layout

\end_inset

 positions, while for H3K27me3, enrichment is broader, extending to 2.5
\begin_inset space ~
\end_inset

kbp.
 These 
\begin_inset Quotes eld
\end_inset

effective promoter radii
\begin_inset Quotes erd
\end_inset

 remain approximately the same across all combinations of experimental condition
 (cell type, time point, and donor), so they appear to be a property of
 the histone mark itself.
 Hence, these radii were used to define the promoter regions for each histone
 mark in all further analyses.
\end_layout

\begin_layout Standard
\begin_inset Float figure
wide false
sideways false
status open

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename graphics/CD4-csaw/Promoter-Peak-Distance-Profile-PAGE1-CROP.pdf
	lyxscale 50
	width 80col%

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Flex TODO Note (inline)
status open

\begin_layout Plain Layout
Future direction idea: Need a control: shuffle all peaks and repeat, N times.
\end_layout

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout
\begin_inset Argument 1
status collapsed

\begin_layout Plain Layout
Enrichment of peaks in promoter neighborhoods.
\end_layout

\end_inset


\begin_inset CommandInset label
LatexCommand label
name "fig:near-promoter-peak-enrich"

\end_inset


\series bold
Enrichment of peaks in promoter neighborhoods.
 
\series default
This plot shows the distribution of distances from each annotated transcription
 start site in the genome to the nearest called peak.
 Each line represents one combination of histone mark, cell type, and time
 point.
 Distributions are smoothed using kernel density estimation.
 TSSs that occur 
\emph on
within
\emph default
 peaks were excluded from this plot to avoid a large spike at zero that
 would overshadow the rest of the distribution.
 (Note: this figure was generated using the original peak calls and expression
 values from 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
GEO
\end_layout

\end_inset

 
\begin_inset CommandInset citation
LatexCommand cite
key "LaMere2016"
literal "false"

\end_inset

.)
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Standard
\begin_inset Float table
wide false
sideways false
status collapsed

\begin_layout Plain Layout
\align center
\begin_inset Tabular
<lyxtabular version="3" rows="4" columns="2">
<features tabularvalignment="middle">
<column alignment="center" valignment="top">
<column alignment="center" valignment="top">
<row>
<cell alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
Histone mark
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" rightline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
Effective promoter radius
\end_layout

\end_inset
</cell>
</row>
<row>
<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
H3K4me2
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" leftline="true" rightline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
1 kbp
\end_layout

\end_inset
</cell>
</row>
<row>
<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
H3K4me3
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" leftline="true" rightline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
1 kbp
\end_layout

\end_inset
</cell>
</row>
<row>
<cell alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
H3K27me3
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" rightline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
2.5 kbp
\end_layout

\end_inset
</cell>
</row>
</lyxtabular>

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout
\begin_inset Argument 1
status collapsed

\begin_layout Plain Layout
Effective promoter radius for each histone mark.
\end_layout

\end_inset


\begin_inset CommandInset label
LatexCommand label
name "tab:effective-promoter-radius"

\end_inset


\series bold
Effective promoter radius for each histone mark.

\series default
 These values represent the approximate distance from transcription start
 site positions within which an excess of peaks are found, as shown in Figure
 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:near-promoter-peak-enrich"
plural "false"
caps "false"
noprefix "false"

\end_inset

.
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Standard
\begin_inset Flex TODO Note (inline)
status open

\begin_layout Plain Layout
Consider also showing figure for distance to nearest peak center, and reference
 median peak size once that is known.
\end_layout

\end_inset


\end_layout

\begin_layout Subsection
Correlations between gene expression and promoter methylation follow expected
 genome-wide trends
\end_layout

\begin_layout Standard
H3K4me2 and H3K4me2 have previously been reported as activating marks whose
 presence in a gene's promoter is associated with higher gene expression,
 while H3K27me3 has been reported as inactivating 
\begin_inset CommandInset citation
LatexCommand cite
key "LaMere2016,LaMere2017"
literal "false"

\end_inset

.
 The data are consistent with this characterization: genes whose promoters
 (as defined by the radii for each histone mark listed in 
\begin_inset CommandInset ref
LatexCommand ref
reference "tab:effective-promoter-radius"
plural "false"
caps "false"
noprefix "false"

\end_inset

) overlap with a H3K4me2 or H3K4me3 peak tend to have higher expression
 than those that don't, while H3K27me3 is likewise associated with lower
 gene expression, as shown in 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:fpkm-by-peak"
plural "false"
caps "false"
noprefix "false"

\end_inset

.
 This pattern holds across all combinations of cell type and time point
 (Welch's 
\emph on
t
\emph default
-test, all 
\begin_inset Formula $p\textrm{-values}\ll2.2\times10^{-16}$
\end_inset

).
 The difference in average 
\begin_inset Formula $\log_{2}$
\end_inset

 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
FPKM
\end_layout

\end_inset

 values when a peak overlaps the promoter is about 
\begin_inset Formula $+5.67$
\end_inset

 for H3K4me2, 
\begin_inset Formula $+5.76$
\end_inset

 for H3K4me2, and 
\begin_inset Formula $-4.00$
\end_inset

 for H3K27me3.
\end_layout

\begin_layout Standard
\begin_inset ERT
status open

\begin_layout Plain Layout


\backslash
afterpage{
\end_layout

\begin_layout Plain Layout


\backslash
begin{landscape}
\end_layout

\end_inset


\end_layout

\begin_layout Standard
\begin_inset Float figure
wide false
sideways false
status collapsed

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename graphics/CD4-csaw/FPKM-by-Peak-Violin-Plots-CROP.pdf
	lyxscale 50
	height 80theight%

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout
\begin_inset Argument 1
status collapsed

\begin_layout Plain Layout
Expression distributions of genes with and without promoter peaks.
\end_layout

\end_inset


\begin_inset CommandInset label
LatexCommand label
name "fig:fpkm-by-peak"

\end_inset


\series bold
Expression distributions of genes with and without promoter peaks.
 
\series default
For each histone mark in each experimental condition, the average RNA-seq
 abundance (
\begin_inset Formula $\log_{2}$
\end_inset

 FPKM) of each gene across all 4 donors was calculated.
 Genes were grouped based on whether or not a peak was called in their promoters
 in that condition, and the distribution of abundance values was plotted
 for the no-peak and peak groups.
 (Note: this figure was generated using the original peak calls and expression
 values from 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
GEO
\end_layout

\end_inset

 
\begin_inset CommandInset citation
LatexCommand cite
key "LaMere2016"
literal "false"

\end_inset

.)
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Standard
\begin_inset ERT
status open

\begin_layout Plain Layout


\backslash
end{landscape}
\end_layout

\begin_layout Plain Layout

}
\end_layout

\end_inset


\end_layout

\begin_layout Subsection
Gene expression and promoter histone methylation patterns show convergence
 between naïve and memory cells at day 14
\end_layout

\begin_layout Standard
We hypothesized that if naïve cells had differentiated into memory cells
 by Day 14, then their patterns of expression and histone modification should
 converge with those of memory cells at Day 14.
 Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:PCoA-promoters"
plural "false"
caps "false"
noprefix "false"

\end_inset

 shows the patterns of variation in all 3 histone marks in the promoter
 regions of the genome using 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
PCoA
\end_layout

\end_inset

.
 All 3 marks show a noticeable convergence between the naïve and memory
 samples at day 14, visible as an overlapping of the day 14 groups on each
 plot.
 This is consistent with the counts of significantly differentially modified
 promoters and estimates of the total numbers of differentially modified
 promoters shown in Table 
\begin_inset CommandInset ref
LatexCommand ref
reference "tab:Number-signif-promoters"
plural "false"
caps "false"
noprefix "false"

\end_inset

.
 For all histone marks, evidence of differential modification between naïve
 and memory samples was detected at every time point except day 14.
 The day 14 convergence pattern is also present in the 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
RNA-seq
\end_layout

\end_inset

 data (Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:RNA-PCA-group"
plural "false"
caps "false"
noprefix "false"

\end_inset

), albeit in the 2nd and 3rd principal coordinates, indicating that it is
 not the most dominant pattern driving gene expression.
 Taken together, the data show that promoter histone methylation for these
 3 histone marks and RNA expression for naïve and memory cells are most
 similar at day 14, the furthest time point after activation.
 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
MOFA
\end_layout

\end_inset

 was also able to capture this day 14 convergence pattern in 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
LF
\end_layout

\end_inset

5 (Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:mofa-lf-scatter"
plural "false"
caps "false"
noprefix "false"

\end_inset

), which accounts for shared variation across all 3 histone marks and the
 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
RNA-seq
\end_layout

\end_inset

 data, confirming that this convergence is a coordinated pattern across
 all 4 data sets.
 While this observation does not prove that the naïve cells have differentiated
 into memory cells at Day 14, it is consistent with that hypothesis.
\end_layout

\begin_layout Standard
\begin_inset Float figure
placement p
wide false
sideways false
status collapsed

\begin_layout Plain Layout
\align center
\begin_inset Float figure
wide false
sideways false
status open

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename graphics/CD4-csaw/ChIP-seq/H3K4me2-promoter-PCA-group-CROP.png
	lyxscale 25
	width 45col%
	groupId pcoa-prom-subfig

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout
\begin_inset CommandInset label
LatexCommand label
name "fig:PCoA-H3K4me2-prom"

\end_inset

PCoA plot of H3K4me2 promoters, after subtracting surrogate variables.
\end_layout

\end_inset


\end_layout

\end_inset


\begin_inset space \hfill{}
\end_inset


\begin_inset Float figure
wide false
sideways false
status open

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename graphics/CD4-csaw/ChIP-seq/H3K4me3-promoter-PCA-group-CROP.png
	lyxscale 25
	width 45col%
	groupId pcoa-prom-subfig

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout
\begin_inset CommandInset label
LatexCommand label
name "fig:PCoA-H3K4me3-prom"

\end_inset

PCoA plot of H3K4me3 promoters, after subtracting surrogate variables.
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Plain Layout
\align center
\begin_inset Float figure
wide false
sideways false
status open

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename graphics/CD4-csaw/ChIP-seq/H3K27me3-promoter-PCA-group-CROP.png
	lyxscale 25
	width 45col%
	groupId pcoa-prom-subfig

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout
\begin_inset CommandInset label
LatexCommand label
name "fig:PCoA-H3K27me3-prom"

\end_inset

PCoA plot of H3K27me3 promoters, after subtracting surrogate variables.
\end_layout

\end_inset


\end_layout

\end_inset


\begin_inset space \hfill{}
\end_inset


\begin_inset Float figure
wide false
sideways false
status open

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename graphics/CD4-csaw/RNA-seq/PCA-final-23-CROP.png
	lyxscale 25
	width 45col%
	groupId pcoa-prom-subfig

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout
\begin_inset CommandInset label
LatexCommand label
name "fig:RNA-PCA-group"

\end_inset

RNA-seq PCoA, after ComBat batch correction, showing principal coordinates
 2 and 3.
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Flex TODO Note (inline)
status open

\begin_layout Plain Layout
Figure font too small
\end_layout

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout
\begin_inset Argument 1
status collapsed

\begin_layout Plain Layout
PCoA plots for promoter ChIP-seq and expression RNA-seq data
\end_layout

\end_inset


\begin_inset CommandInset label
LatexCommand label
name "fig:PCoA-promoters"

\end_inset


\series bold
PCoA plots for promoter ChIP-seq and expression RNA-seq data.
 
\series default
Each point represents an individual sample.
 Samples with the same combination of cell type and time point are encircled
 with a shaded region to aid in visual identification of the sample groups.
 Samples of the same cell type from the same donor are connected by lines
 to indicate the 
\begin_inset Quotes eld
\end_inset

trajectory
\begin_inset Quotes erd
\end_inset

 of each donor's cells over time in PCoA space.
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Standard
\begin_inset ERT
status open

\begin_layout Plain Layout


\backslash
afterpage{
\end_layout

\begin_layout Plain Layout


\backslash
begin{landscape}
\end_layout

\end_inset


\end_layout

\begin_layout Standard
\begin_inset Float table
wide false
sideways false
status collapsed

\begin_layout Plain Layout
\align center
\begin_inset Tabular
<lyxtabular version="3" rows="6" columns="7">
<features tabularvalignment="middle">
<column alignment="center" valignment="top">
<column alignment="center" valignment="top">
<column alignment="center" valignment="top">
<column alignment="center" valignment="top">
<column alignment="center" valignment="top">
<column alignment="center" valignment="top">
<column alignment="center" valignment="top">
<row>
<cell alignment="center" valignment="top" usebox="none">
\begin_inset Text

\begin_layout Plain Layout

\end_layout

\end_inset
</cell>
<cell multicolumn="1" alignment="center" valignment="top" topline="true" leftline="true" rightline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
Number of significant promoters
\end_layout

\end_inset
</cell>
<cell multicolumn="2" alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout

\end_layout

\end_inset
</cell>
<cell multicolumn="2" alignment="center" valignment="top" topline="true" leftline="true" rightline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout

\end_layout

\end_inset
</cell>
<cell multicolumn="1" alignment="center" valignment="top" topline="true" leftline="true" rightline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
Est.
 differentially modified promoters
\end_layout

\end_inset
</cell>
<cell multicolumn="2" alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout

\end_layout

\end_inset
</cell>
<cell multicolumn="2" alignment="center" valignment="top" topline="true" leftline="true" rightline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout

\end_layout

\end_inset
</cell>
</row>
<row>
<cell alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
Time Point
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
H3K4me2
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
H3K4me3
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" rightline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
H3K27me3
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
H3K4me2
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
H3K4me3
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" rightline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
H3K27me3
\end_layout

\end_inset
</cell>
</row>
<row>
<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
Day 0
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
4553
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
927
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" leftline="true" rightline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
6
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
9967
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
4149
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" leftline="true" rightline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
2404
\end_layout

\end_inset
</cell>
</row>
<row>
<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
Day 1
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
567
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
278
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" leftline="true" rightline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
1570
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
4370
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
2145
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" leftline="true" rightline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
6598
\end_layout

\end_inset
</cell>
</row>
<row>
<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
Day 5
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
2313
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
139
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" leftline="true" rightline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
490
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
9450
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
1148
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" leftline="true" rightline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
4141
\end_layout

\end_inset
</cell>
</row>
<row>
<cell alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
Day 14
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
0
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
0
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" rightline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
0
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
0
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
0
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" rightline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
0
\end_layout

\end_inset
</cell>
</row>
</lyxtabular>

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout
\begin_inset Argument 1
status collapsed

\begin_layout Plain Layout
Number of differentially modified promoters between naïve and memory cells
 at each time point after activation.
\end_layout

\end_inset


\begin_inset CommandInset label
LatexCommand label
name "tab:Number-signif-promoters"

\end_inset


\series bold
Number of differentially modified promoters between naïve and memory cells
 at each time point after activation.
 
\series default
This table shows both the number of differentially modified promoters detected
 at a 10% FDR threshold (left half), and the total number of differentially
 modified promoters estimated using the method of averaging local FDR estimates
 
\begin_inset CommandInset citation
LatexCommand cite
key "Phipson2016"
literal "false"

\end_inset

 (right half).
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Standard
\begin_inset ERT
status open

\begin_layout Plain Layout


\backslash
end{landscape}
\end_layout

\begin_layout Plain Layout

}
\end_layout

\end_inset


\end_layout

\begin_layout Subsection
Location of H3K4me2 and H3K4me3 promoter coverage associates with gene expressio
n
\end_layout

\begin_layout Standard
\begin_inset Flex TODO Note (inline)
status open

\begin_layout Plain Layout
Make sure use of coverage/abundance/whatever is consistent.
\end_layout

\end_inset


\end_layout

\begin_layout Standard
\begin_inset Flex TODO Note (inline)
status open

\begin_layout Plain Layout
For the figures in this section and the next, the group labels are arbitrary,
 so if time allows, it would be good to manually reorder them in a logical
 way, e.g.
 most upstream to most downstream.
 If this is done, make sure to update the text with the correct group labels.
\end_layout

\end_inset


\end_layout

\begin_layout Standard
To test whether the position of a histone mark relative to a gene's 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
TSS
\end_layout

\end_inset

 was important, we looked at the 
\begin_inset Quotes eld
\end_inset

landscape
\begin_inset Quotes erd
\end_inset

 of 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
ChIP-seq
\end_layout

\end_inset

 read coverage in naïve Day 0 samples within 5 kbp of each gene's 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
TSS
\end_layout

\end_inset

 by binning reads into 500-bp windows tiled across each promoter 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
logCPM
\end_layout

\end_inset

 values were calculated for the bins in each promoter and then the average
 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
logCPM
\end_layout

\end_inset

 for each promoter's bins was normalized to zero, such that the values represent
 coverage relative to other regions of the same promoter rather than being
 proportional to absolute read count.
 The promoters were then clustered based on the normalized bin abundances
 using 
\begin_inset Formula $k$
\end_inset

-means clustering with 
\begin_inset Formula $K=6$
\end_inset

.
 Different values of 
\begin_inset Formula $K$
\end_inset

 were also tested, but did not substantially change the interpretation of
 the data.
\end_layout

\begin_layout Standard
For H3K4me2, plotting the average bin abundances for each cluster reveals
 a simple pattern (Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:H3K4me2-neighborhood-clusters"
plural "false"
caps "false"
noprefix "false"

\end_inset

): Cluster 5 represents a completely flat promoter coverage profile, likely
 consisting of genes with no H3K4me2 methylation in the promoter.
 All the other clusters represent a continuum of peak positions relative
 to the 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
TSS
\end_layout

\end_inset

.
 In order from most upstream to most downstream, they are Clusters 6, 4,
 3, 1, and 2.
 There do not appear to be any clusters representing coverage patterns other
 than lone peaks, such as coverage troughs or double peaks.
 Next, all promoters were plotted in a 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
PCA
\end_layout

\end_inset

 plot based on the same relative bin abundance data, and colored based on
 cluster membership (Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:H3K4me2-neighborhood-pca"
plural "false"
caps "false"
noprefix "false"

\end_inset

).
 The 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
PCA
\end_layout

\end_inset

 plot shows Cluster 5 (the 
\begin_inset Quotes eld
\end_inset

no peak
\begin_inset Quotes erd
\end_inset

 cluster) at the center, with the other clusters arranged in a counter-clockwise
 arc around it in the order noted above, from most upstream peak to most
 downstream.
 Notably, the 
\begin_inset Quotes eld
\end_inset

clusters
\begin_inset Quotes erd
\end_inset

 form a single large 
\begin_inset Quotes eld
\end_inset

cloud
\begin_inset Quotes erd
\end_inset

 with no apparent separation between them, further supporting the conclusion
 that these clusters represent an arbitrary partitioning of a continuous
 distribution of promoter coverage landscapes.
 While the clusters are a useful abstraction that aids in visualization,
 they are ultimately not an accurate representation of the data.
 The continuous nature of the distribution also explains why different values
 of 
\begin_inset Formula $K$
\end_inset

 led to similar conclusions.
\end_layout

\begin_layout Standard
\begin_inset ERT
status open

\begin_layout Plain Layout


\backslash
afterpage{
\end_layout

\begin_layout Plain Layout


\backslash
begin{landscape}
\end_layout

\end_inset


\end_layout

\begin_layout Standard
\begin_inset Float figure
wide false
sideways false
status collapsed

\begin_layout Plain Layout
\align center
\begin_inset Float figure
wide false
sideways false
status open

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename graphics/CD4-csaw/ChIP-seq/H3K4me2-neighborhood-clusters-CROP.png
	lyxscale 25
	width 30col%
	groupId covprof-subfig

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout

\series bold
\begin_inset CommandInset label
LatexCommand label
name "fig:H3K4me2-neighborhood-clusters"

\end_inset

Average relative coverage for each bin in each cluster.
\end_layout

\end_inset


\end_layout

\end_inset


\begin_inset space \hfill{}
\end_inset


\begin_inset Float figure
wide false
sideways false
status open

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename graphics/CD4-csaw/ChIP-seq/H3K4me2-neighborhood-PCA-CROP.png
	lyxscale 25
	width 30col%
	groupId covprof-subfig

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout
\begin_inset CommandInset label
LatexCommand label
name "fig:H3K4me2-neighborhood-pca"

\end_inset

PCA of relative coverage depth, colored by K-means cluster membership.
\end_layout

\end_inset


\end_layout

\end_inset


\begin_inset space \hfill{}
\end_inset


\begin_inset Float figure
wide false
sideways false
status open

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename graphics/CD4-csaw/ChIP-seq/H3K4me2-neighborhood-expression-CROP.png
	lyxscale 25
	width 30col%
	groupId covprof-subfig

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout
\begin_inset CommandInset label
LatexCommand label
name "fig:H3K4me2-neighborhood-expression"

\end_inset

Gene expression grouped by promoter coverage clusters.
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Flex TODO Note (inline)
status open

\begin_layout Plain Layout
Figure font too small
\end_layout

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout
\begin_inset Argument 1
status collapsed

\begin_layout Plain Layout
K-means clustering of promoter H3K4me2 relative coverage depth in naïve
 day 0 samples.
\end_layout

\end_inset


\begin_inset CommandInset label
LatexCommand label
name "fig:H3K4me2-neighborhood"

\end_inset


\series bold
K-means clustering of promoter H3K4me2 relative coverage depth in naïve
 day 0 samples.
 
\series default
H3K4me2 ChIP-seq reads were binned into 500-bp windows tiled across each
 promoter from 5
\begin_inset space ~
\end_inset

kbp upstream to 5
\begin_inset space ~
\end_inset

kbp downstream, and the logCPM values were normalized within each promoter
 to an average of 0, yielding relative coverage depths.
 These were then grouped using K-means clustering with 
\begin_inset Formula $K=6$
\end_inset

,
\series bold
 
\series default
and the average bin values were plotted for each cluster (a).
 The 
\begin_inset Formula $x$
\end_inset

-axis is the genomic coordinate of each bin relative to the the transcription
 start site, and the 
\begin_inset Formula $y$
\end_inset

-axis is the mean relative coverage depth of that bin across all promoters
 in the cluster.
 Each line represents the average 
\begin_inset Quotes eld
\end_inset

shape
\begin_inset Quotes erd
\end_inset

 of the promoter coverage for promoters in that cluster.
 PCA was performed on the same data, and the first two PCs were plotted,
 coloring each point by its K-means cluster identity (b).
 For each cluster, the distribution of gene expression values was plotted
 (c).
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Standard
\begin_inset ERT
status open

\begin_layout Plain Layout


\backslash
end{landscape}
\end_layout

\begin_layout Plain Layout

}
\end_layout

\end_inset


\end_layout

\begin_layout Standard
\begin_inset Flex TODO Note (inline)
status open

\begin_layout Plain Layout
Should have a table of p-values on difference of means between Cluster 5
 and the others.
\end_layout

\end_inset


\end_layout

\begin_layout Standard
To investigate the association between relative peak position and gene expressio
n, we plotted the Naïve Day 0 expression for the genes in each cluster (Figure
 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:H3K4me2-neighborhood-expression"
plural "false"
caps "false"
noprefix "false"

\end_inset

).
 Most genes in Cluster 5, the 
\begin_inset Quotes eld
\end_inset

no peak
\begin_inset Quotes erd
\end_inset

 cluster, have low expression values.
 Taking this as the 
\begin_inset Quotes eld
\end_inset

baseline
\begin_inset Quotes erd
\end_inset

 distribution when no H3K4me2 methylation is present, we can compare the
 other clusters' distributions to determine which peak positions are associated
 with elevated expression.
 As might be expected, the 3 clusters representing peaks closest to the
 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
TSS
\end_layout

\end_inset

, Clusters 1, 3, and 4, show the highest average expression distributions.
 Specifically, these clusters all have their highest 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
ChIP-seq
\end_layout

\end_inset

 abundance within 1kb of the 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
TSS
\end_layout

\end_inset

, consistent with the previously determined promoter radius.
 In contrast, cluster 6, which represents peaks several kbp upstream of
 the 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
TSS
\end_layout

\end_inset

, shows a slightly higher average expression than baseline, while Cluster
 2, which represents peaks several kbp downstream, doesn't appear to show
 any appreciable difference.
 Interestingly, the cluster with the highest average expression is Cluster
 1, which represents peaks about 1 kbp downstream of the 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
TSS
\end_layout

\end_inset

, rather than Cluster 3, which represents peaks centered directly at the
 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
TSS
\end_layout

\end_inset

.
 This suggests that conceptualizing the promoter as a region centered on
 the 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
TSS
\end_layout

\end_inset

 with a certain 
\begin_inset Quotes eld
\end_inset

radius
\begin_inset Quotes erd
\end_inset

 may be an oversimplification – a peak that is a specific distance from
 the 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
TSS
\end_layout

\end_inset

 may have a different degree of influence depending on whether it is upstream
 or downstream of the 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
TSS
\end_layout

\end_inset

.
\end_layout

\begin_layout Standard
All observations described above for H3K4me2 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
ChIP-seq
\end_layout

\end_inset

 also appear to hold for H3K4me3 as well (Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:H3K4me3-neighborhood"
plural "false"
caps "false"
noprefix "false"

\end_inset

).
 This is expected, since there is a high correlation between the positions
 where both histone marks occur.
\end_layout

\begin_layout Standard
\begin_inset ERT
status open

\begin_layout Plain Layout


\backslash
afterpage{
\end_layout

\begin_layout Plain Layout


\backslash
begin{landscape}
\end_layout

\end_inset


\end_layout

\begin_layout Standard
\begin_inset Float figure
wide false
sideways false
status collapsed

\begin_layout Plain Layout
\align center
\begin_inset Float figure
wide false
sideways false
status open

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename graphics/CD4-csaw/ChIP-seq/H3K4me3-neighborhood-clusters-CROP.png
	lyxscale 25
	width 30col%
	groupId covprof-subfig

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout
\begin_inset CommandInset label
LatexCommand label
name "fig:H3K4me3-neighborhood-clusters"

\end_inset

Average relative coverage for each bin in each cluster.
\end_layout

\end_inset


\end_layout

\end_inset


\begin_inset space \hfill{}
\end_inset


\begin_inset Float figure
wide false
sideways false
status open

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename graphics/CD4-csaw/ChIP-seq/H3K4me3-neighborhood-PCA-CROP.png
	lyxscale 25
	width 30col%
	groupId covprof-subfig

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout
\begin_inset CommandInset label
LatexCommand label
name "fig:H3K4me3-neighborhood-pca"

\end_inset

PCA of relative coverage depth, colored by K-means cluster membership.
\end_layout

\end_inset


\end_layout

\end_inset


\begin_inset space \hfill{}
\end_inset


\begin_inset Float figure
wide false
sideways false
status open

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename graphics/CD4-csaw/ChIP-seq/H3K4me3-neighborhood-expression-CROP.png
	lyxscale 25
	width 30col%
	groupId covprof-subfig

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout
\begin_inset CommandInset label
LatexCommand label
name "fig:H3K4me3-neighborhood-expression"

\end_inset

Gene expression grouped by promoter coverage clusters.
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout
\begin_inset Argument 1
status collapsed

\begin_layout Plain Layout
K-means clustering of promoter H3K4me3 relative coverage depth in naïve
 day 0 samples.
\end_layout

\end_inset


\begin_inset CommandInset label
LatexCommand label
name "fig:H3K4me3-neighborhood"

\end_inset


\series bold
K-means clustering of promoter H3K4me3 relative coverage depth in naïve
 day 0 samples.
 
\series default
H3K4me3 ChIP-seq reads were binned into 500-bp windows tiled across each
 promoter from 5
\begin_inset space ~
\end_inset

kbp upstream to 5
\begin_inset space ~
\end_inset

kbp downstream, and the logCPM values were normalized within each promoter
 to an average of 0, yielding relative coverage depths.
 These were then grouped using K-means clustering with 
\begin_inset Formula $K=6$
\end_inset

,
\series bold
 
\series default
and the average bin values were plotted for each cluster (a).
 The 
\begin_inset Formula $x$
\end_inset

-axis is the genomic coordinate of each bin relative to the the transcription
 start site, and the 
\begin_inset Formula $y$
\end_inset

-axis is the mean relative coverage depth of that bin across all promoters
 in the cluster.
 Each line represents the average 
\begin_inset Quotes eld
\end_inset

shape
\begin_inset Quotes erd
\end_inset

 of the promoter coverage for promoters in that cluster.
 PCA was performed on the same data, and the first two PCs were plotted,
 coloring each point by its K-means cluster identity (b).
 For each cluster, the distribution of gene expression values was plotted
 (c).
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Standard
\begin_inset ERT
status open

\begin_layout Plain Layout


\backslash
end{landscape}
\end_layout

\begin_layout Plain Layout

}
\end_layout

\end_inset


\end_layout

\begin_layout Subsection
Patterns of H3K27me3 promoter coverage associate with gene expression
\end_layout

\begin_layout Standard
Unlike both H3K4 marks, whose main patterns of variation appear directly
 related to the size and position of a single peak within the promoter,
 the patterns of H3K27me3 methylation in promoters are more complex (Figure
 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:H3K27me3-neighborhood"
plural "false"
caps "false"
noprefix "false"

\end_inset

).
 Once again looking at the relative coverage in a 500-bp wide bins in a
 5kb radius around each 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
TSS
\end_layout

\end_inset

, promoters were clustered based on the normalized relative coverage values
 in each bin using 
\begin_inset Formula $k$
\end_inset

-means clustering with 
\begin_inset Formula $K=6$
\end_inset

 (Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:H3K27me3-neighborhood-clusters"
plural "false"
caps "false"
noprefix "false"

\end_inset

).
 This time, 3 
\begin_inset Quotes eld
\end_inset

axes
\begin_inset Quotes erd
\end_inset

 of variation can be observed, each represented by 2 clusters with opposing
 patterns.
 The first axis is greater upstream coverage (Cluster 1) vs.
 greater downstream coverage (Cluster 3); the second axis is the coverage
 at the 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
TSS
\end_layout

\end_inset

 itself: peak (Cluster 4) or trough (Cluster 2); lastly, the third axis
 represents a trough upstream of the 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
TSS
\end_layout

\end_inset

 (Cluster 5) vs.
 downstream of the 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
TSS
\end_layout

\end_inset

 (Cluster 6).
 Referring to these opposing pairs of clusters as axes of variation is justified
, because they correspond precisely to the first 3 
\begin_inset Flex Glossary Term (pl)
status open

\begin_layout Plain Layout
PC
\end_layout

\end_inset

 in the 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
PCA
\end_layout

\end_inset

 plot of the relative coverage values (Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:H3K27me3-neighborhood-pca"
plural "false"
caps "false"
noprefix "false"

\end_inset

).
 The 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
PCA
\end_layout

\end_inset

 plot reveals that as in the case of H3K4me2, all the 
\begin_inset Quotes eld
\end_inset

clusters
\begin_inset Quotes erd
\end_inset

 are really just sections of a single connected cloud rather than discrete
 clusters.
 The cloud is approximately ellipsoid-shaped, with each PC being an axis
 of the ellipse, and each cluster consisting of a pyramidal section of the
 ellipsoid.
\end_layout

\begin_layout Standard
\begin_inset ERT
status open

\begin_layout Plain Layout


\backslash
afterpage{
\end_layout

\begin_layout Plain Layout


\backslash
begin{landscape}
\end_layout

\end_inset


\end_layout

\begin_layout Standard
\begin_inset Float figure
wide false
sideways false
status open

\begin_layout Plain Layout
\align center
\begin_inset Float figure
wide false
sideways false
status open

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename graphics/CD4-csaw/ChIP-seq/H3K27me3-neighborhood-clusters-CROP.png
	lyxscale 25
	width 30col%
	groupId covprof-subfig

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout
\begin_inset CommandInset label
LatexCommand label
name "fig:H3K27me3-neighborhood-clusters"

\end_inset

Average relative coverage for each bin in each cluster.
\end_layout

\end_inset


\end_layout

\end_inset


\begin_inset space \hfill{}
\end_inset


\begin_inset Float figure
wide false
sideways false
status open

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename graphics/CD4-csaw/ChIP-seq/H3K27me3-neighborhood-PCA-CROP.png
	lyxscale 25
	width 30col%
	groupId covprof-subfig

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout
\begin_inset CommandInset label
LatexCommand label
name "fig:H3K27me3-neighborhood-pca"

\end_inset

PCA of relative coverage depth, colored by K-means cluster membership.
\end_layout

\end_inset


\end_layout

\end_inset


\begin_inset space \hfill{}
\end_inset


\begin_inset Float figure
wide false
sideways false
status open

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename graphics/CD4-csaw/ChIP-seq/H3K27me3-neighborhood-expression-CROP.png
	lyxscale 25
	width 30col%
	groupId covprof-subfig

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout
\begin_inset CommandInset label
LatexCommand label
name "fig:H3K27me3-neighborhood-expression"

\end_inset

Gene expression grouped by promoter coverage clusters.
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout
\begin_inset Argument 1
status collapsed

\begin_layout Plain Layout
K-means clustering of promoter H3K27me3 relative coverage depth in naïve
 day 0 samples.
\end_layout

\end_inset


\begin_inset CommandInset label
LatexCommand label
name "fig:H3K27me3-neighborhood"

\end_inset


\series bold
K-means clustering of promoter H3K27me3 relative coverage depth in naïve
 day 0 samples.
 
\series default
H3K27me3 ChIP-seq reads were binned into 500-bp windows tiled across each
 promoter from 5
\begin_inset space ~
\end_inset

kbp upstream to 5
\begin_inset space ~
\end_inset

kbp downstream, and the logCPM values were normalized within each promoter
 to an average of 0, yielding relative coverage depths.
 These were then grouped using 
\begin_inset Formula $k$
\end_inset

-means clustering with 
\begin_inset Formula $K=6$
\end_inset

,
\series bold
 
\series default
and the average bin values were plotted for each cluster (a).
 The 
\begin_inset Formula $x$
\end_inset

-axis is the genomic coordinate of each bin relative to the the transcription
 start site, and the 
\begin_inset Formula $y$
\end_inset

-axis is the mean relative coverage depth of that bin across all promoters
 in the cluster.
 Each line represents the average 
\begin_inset Quotes eld
\end_inset

shape
\begin_inset Quotes erd
\end_inset

 of the promoter coverage for promoters in that cluster.
 PCA was performed on the same data, and the first two PCs were plotted,
 coloring each point by its K-means cluster identity (b).
 (Note: In (b), Cluster 6 is hidden behind all the other clusters.) For each
 cluster, the distribution of gene expression values was plotted (c).
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Standard
\begin_inset ERT
status open

\begin_layout Plain Layout


\backslash
end{landscape}
\end_layout

\begin_layout Plain Layout

}
\end_layout

\end_inset


\end_layout

\begin_layout Standard
In Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:H3K27me3-neighborhood-expression"
plural "false"
caps "false"
noprefix "false"

\end_inset

, we can see that Clusters 1 and 2 are the only clusters with higher gene
 expression than the others.
 For Cluster 2, this is expected, since this cluster represents genes with
 depletion of H3K27me3 near the promoter.
 Hence, elevated expression in cluster 2 is consistent with the conventional
 view of H3K27me3 as a deactivating mark.
 However, Cluster 1, the cluster with the most elevated gene expression,
 represents genes with elevated coverage upstream of the 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
TSS
\end_layout

\end_inset

, or equivalently, decreased coverage downstream, inside the gene body.
 The opposite pattern, in which H3K27me3 is more abundant within the gene
 body and less abundance in the upstream promoter region, does not show
 any elevation in gene expression.
 As with H3K4me2, this shows that the location of H3K27 trimethylation relative
 to the 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
TSS
\end_layout

\end_inset

 is potentially an important factor beyond simple proximity.
\end_layout

\begin_layout Standard
\begin_inset Note Note
status open

\begin_layout Plain Layout
\begin_inset Flex TODO Note (inline)
status open

\begin_layout Plain Layout
Show the figures where the negative result ended this line of inquiry.
 I need to debug some errors resulting from an R upgrade to do this.
\end_layout

\end_inset


\end_layout

\begin_layout Subsection
Defined pattern analysis
\end_layout

\begin_layout Plain Layout
\begin_inset Flex TODO Note (inline)
status open

\begin_layout Plain Layout
This was where I defined interesting expression patterns and then looked
 at initial relative promoter coverage for each expression pattern.
 Negative result.
 I forgot about this until recently.
 Worth including? Remember to also write methods.
\end_layout

\end_inset


\end_layout

\begin_layout Subsection
Promoter CpG islands?
\end_layout

\begin_layout Plain Layout
\begin_inset Flex TODO Note (inline)
status open

\begin_layout Plain Layout
I forgot until recently about the work I did on this.
 Worth including? Remember to also write methods.
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Section
Discussion
\end_layout

\begin_layout Subsection
Each histone mark's 
\begin_inset Quotes eld
\end_inset

effective promoter extent
\begin_inset Quotes erd
\end_inset

 must be determined empirically
\end_layout

\begin_layout Standard
Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:near-promoter-peak-enrich"
plural "false"
caps "false"
noprefix "false"

\end_inset

 shows that H3K4me2, H3K4me3, and H3K27me3 are all enriched near promoters,
 relative to the rest of the genome, consistent with their conventionally
 understood role in regulating gene transcription.
 Interestingly, the radius within this enrichment occurs is not the same
 for each histone mark.
 H3K4me2 and H3K4me3 are enriched within a 1
\begin_inset space ~
\end_inset

kbp radius, while H3K27me3 is enriched within 2.5
\begin_inset space ~
\end_inset

kbp.
 Notably, the determined promoter radius was consistent across all experimental
 conditions, varying only between different histone marks.
 This suggests that the conventional 
\begin_inset Quotes eld
\end_inset

one size fits all
\begin_inset Quotes erd
\end_inset

 approach of defining a single promoter region for each gene (or each 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
TSS
\end_layout

\end_inset

) and using that same promoter region for analyzing all types of genomic
 data within an experiment may not be appropriate, and a better approach
 may be to use a separate promoter radius for each kind of data, with each
 radius being derived from the data itself.
 Furthermore, the apparent asymmetry of upstream and downstream promoter
 histone modification with respect to gene expression, seen in Figures 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:H3K4me2-neighborhood"
plural "false"
caps "false"
noprefix "false"

\end_inset

, 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:H3K4me3-neighborhood"
plural "false"
caps "false"
noprefix "false"

\end_inset

, and 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:H3K27me3-neighborhood"
plural "false"
caps "false"
noprefix "false"

\end_inset

, shows that even the concept of a promoter 
\begin_inset Quotes eld
\end_inset

radius
\begin_inset Quotes erd
\end_inset

 is likely an oversimplification.
 At a minimum, nearby enrichment of peaks should be evaluated separately
 for both upstream and downstream peaks, and an appropriate 
\begin_inset Quotes eld
\end_inset

radius
\begin_inset Quotes erd
\end_inset

 should be selected for each direction.
\end_layout

\begin_layout Standard
\begin_inset Flex TODO Note (inline)
status open

\begin_layout Plain Layout
Sarah: I would have to search the literature, but I believe this has been
 observed before.
 The position relative to the TSS likely has to do with recruitment of the
 transcriptional machinery and the space required for that.
\end_layout

\end_inset


\end_layout

\begin_layout Standard
Figures 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:H3K4me2-neighborhood"
plural "false"
caps "false"
noprefix "false"

\end_inset

 and 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:H3K4me3-neighborhood"
plural "false"
caps "false"
noprefix "false"

\end_inset

 show that the determined promoter radius of 1
\begin_inset space ~
\end_inset

kbp is approximately consistent with the distance from the 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
TSS
\end_layout

\end_inset

 at which enrichment of H3K4 methylation correlates with increased expression,
 showing that this radius, which was determined by a simple analysis of
 measuring the distance from each 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
TSS
\end_layout

\end_inset

 to the nearest peak, also has functional significance.
 For H3K27me3, the correlation between histone modification near the promoter
 and gene expression is more complex, involving non-peak variations such
 as troughs in coverage at the 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
TSS
\end_layout

\end_inset

 and asymmetric coverage upstream and downstream, so it is difficult in
 this case to evaluate whether the 2.5
\begin_inset space ~
\end_inset

kbp radius determined from TSS-to-peak distances is functionally significant.
 However, the two patterns of coverage associated with elevated expression
 levels both have interesting features within this radius.
\end_layout

\begin_layout Subsection
Day 14 convergence is consistent with naïve-to-memory differentiation
\end_layout

\begin_layout Standard
\begin_inset Flex TODO Note (inline)
status open

\begin_layout Plain Layout
Look up some more references for these histone marks being involved in memory
 differentiation.
 (Ask Sarah)
\end_layout

\end_inset


\end_layout

\begin_layout Standard
We observed that all 3 histone marks and the gene expression data all exhibit
 evidence of convergence in abundance between naïve and memory cells by
 day 14 after activation (Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:PCoA-promoters"
plural "false"
caps "false"
noprefix "false"

\end_inset

, Table 
\begin_inset CommandInset ref
LatexCommand ref
reference "tab:Number-signif-promoters"
plural "false"
caps "false"
noprefix "false"

\end_inset

).
 The 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
MOFA
\end_layout

\end_inset

 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
LF
\end_layout

\end_inset

 scatter plots (Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:mofa-lf-scatter"
plural "false"
caps "false"
noprefix "false"

\end_inset

) show that this pattern of convergence is captured in 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
LF
\end_layout

\end_inset

5.
 Like all the 
\begin_inset Flex Glossary Term (pl)
status open

\begin_layout Plain Layout
LF
\end_layout

\end_inset

 in this plot, this factor explains a substantial portion of the variance
 in all 4 data sets, indicating a coordinated pattern of variation shared
 across all histone marks and gene expression.
 This is consistent with the expectation that any naïve CD4
\begin_inset Formula $^{+}$
\end_inset

 T-cells remaining at day 14 should have differentiated into memory cells
 by that time, and should therefore have a genomic and epigenomic state
 similar to memory cells.
 This convergence is evidence that these histone marks all play an important
 role in the naïve-to-memory differentiation process.
 A histone mark that was not involved in naïve-to-memory differentiation
 would not be expected to converge in this way after activation.
\end_layout

\begin_layout Standard
In H3K4me2, H3K4me3, and 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
RNA-seq
\end_layout

\end_inset

, this convergence appears to be in progress already by Day 5, shown by
 the smaller distance between naïve and memory cells at day 5 along the
 
\begin_inset Formula $y$
\end_inset

-axes in Figures 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:PCoA-H3K4me2-prom"
plural "false"
caps "false"
noprefix "false"

\end_inset

, 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:PCoA-H3K4me3-prom"
plural "false"
caps "false"
noprefix "false"

\end_inset

, and 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:RNA-PCA-group"
plural "false"
caps "false"
noprefix "false"

\end_inset

.
 This agrees with the model proposed by Sarah Lamere based on an prior analysis
 of the same data, shown in Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:Lamere2016-Fig8"
plural "false"
caps "false"
noprefix "false"

\end_inset

, which shows the pattern of H3K4 methylation and expression for naïve cells
 and memory cells converging at day 5.
 This model was developed without the benefit of the 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
PCoA
\end_layout

\end_inset

 plots in Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:PCoA-promoters"
plural "false"
caps "false"
noprefix "false"

\end_inset

, which have been corrected for confounding factors by ComBat and 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
SVA
\end_layout

\end_inset

.
 This shows that proper batch correction assists in extracting meaningful
 patterns in the data while eliminating systematic sources of irrelevant
 variation in the data, allowing simple automated procedures like 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
PCoA
\end_layout

\end_inset

 to reveal interesting behaviors in the data that were previously only detectabl
e by a detailed manual analysis.
 While the ideal comparison to demonstrate this convergence would be naïve
 cells at day 14 to memory cells at day 0, this is not feasible in this
 experimental system, since neither naïve nor memory cells are able to fully
 return to their pre-activation state, as shown by the lack of overlap between
 days 0 and 14 for either naïve or memory cells in Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:PCoA-promoters"
plural "false"
caps "false"
noprefix "false"

\end_inset

.
\end_layout

\begin_layout Standard
\begin_inset Float figure
wide false
sideways false
status collapsed

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename graphics/CD4-csaw/LaMere2016_fig8.pdf
	lyxscale 50
	width 100col%
	groupId colfullwidth

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout
\begin_inset Argument 1
status collapsed

\begin_layout Plain Layout
Lamere 2016 Figure 8 “Model for the role of H3K4 methylation during CD4
\begin_inset Formula $^{+}$
\end_inset

 T-cell activation.
\begin_inset Quotes erd
\end_inset


\end_layout

\end_inset


\begin_inset CommandInset label
LatexCommand label
name "fig:Lamere2016-Fig8"

\end_inset


\series bold
Lamere 2016 Figure 8 
\begin_inset CommandInset citation
LatexCommand cite
key "LaMere2016"
literal "false"

\end_inset

, 
\begin_inset Quotes eld
\end_inset

Model for the role of H3K4 methylation during CD4
\begin_inset Formula $\mathbf{^{+}}$
\end_inset

 T-cell activation.
\begin_inset Quotes erd
\end_inset

 
\series default
(Reproduced with permission.)
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Subsection
The location of histone modifications within the promoter is important
\end_layout

\begin_layout Standard
When looking at patterns in the relative coverage of each histone mark near
 the 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
TSS
\end_layout

\end_inset

 of each gene, several interesting patterns were apparent.
 For H3K4me2 and H3K4me3, the pattern was straightforward: the consistent
 pattern across all promoters was a single peak a few kbp wide, with the
 main axis of variation being the position of this peak relative to the
 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
TSS
\end_layout

\end_inset

 (Figures 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:H3K4me2-neighborhood"
plural "false"
caps "false"
noprefix "false"

\end_inset

 & 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:H3K4me3-neighborhood"
plural "false"
caps "false"
noprefix "false"

\end_inset

).
 There were no obvious 
\begin_inset Quotes eld
\end_inset

preferred
\begin_inset Quotes erd
\end_inset

 positions, but rather a continuous distribution of relative positions ranging
 all across the promoter region.
 The association with gene expression was also straightforward: peaks closer
 to the 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
TSS
\end_layout

\end_inset

 were more strongly associated with elevated gene expression.
 Coverage downstream of the 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
TSS
\end_layout

\end_inset

 appears to be more strongly associated with elevated expression than coverage
 at the same distance upstream, indicating that the 
\begin_inset Quotes eld
\end_inset

effective promoter region
\begin_inset Quotes erd
\end_inset

 for H3K4me2 and H3K4me3 may be centered downstream of the 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
TSS
\end_layout

\end_inset

.
\end_layout

\begin_layout Standard
The relative promoter coverage for H3K27me3 had a more complex pattern,
 with two specific patterns of promoter coverage associated with elevated
 expression: a sharp depletion of H3K27me3 around the 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
TSS
\end_layout

\end_inset

 relative to the surrounding area, and a depletion of H3K27me3 downstream
 of the 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
TSS
\end_layout

\end_inset

 relative to upstream (Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:H3K27me3-neighborhood"
plural "false"
caps "false"
noprefix "false"

\end_inset

).
 A previous study found that H3K27me3 depletion within the gene body was
 associated with elevated gene expression in 4 different cell types in mice
 
\begin_inset CommandInset citation
LatexCommand cite
key "Young2011"
literal "false"

\end_inset

.
 This is consistent with the second pattern described here.
 This study also reported that a spike in coverage at the 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
TSS
\end_layout

\end_inset

 was associated with 
\emph on
lower
\emph default
 expression, which is indirectly consistent with the first pattern described
 here, in the sense that it associates lower H3K27me3 levels near the 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
TSS
\end_layout

\end_inset

 with higher expression.
\end_layout

\begin_layout Subsection
A reproducible workflow aids in analysis
\end_layout

\begin_layout Standard
The analyses described in this chapter were organized into a reproducible
 workflow using the Snakemake workflow management system 
\begin_inset CommandInset citation
LatexCommand cite
key "Koster2012"
literal "false"

\end_inset

.
 As shown in Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:rulegraph"
plural "false"
caps "false"
noprefix "false"

\end_inset

, the workflow includes many steps with complex dependencies between them.
 For example, the step that counts the number of 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
ChIP-seq
\end_layout

\end_inset

 reads in 500
\begin_inset space ~
\end_inset

bp windows in each promoter (the starting point for Figures 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:H3K4me2-neighborhood"
plural "false"
caps "false"
noprefix "false"

\end_inset

, 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:H3K4me3-neighborhood"
plural "false"
caps "false"
noprefix "false"

\end_inset

, and 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:H3K27me3-neighborhood"
plural "false"
caps "false"
noprefix "false"

\end_inset

), named 
\begin_inset Flex Code
status open

\begin_layout Plain Layout
chipseq_count_tss_neighborhoods
\end_layout

\end_inset

, depends on the 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
RNA-seq
\end_layout

\end_inset

 abundance estimates in order to select the most-used 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
TSS
\end_layout

\end_inset

 for each gene, the aligned 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
ChIP-seq
\end_layout

\end_inset

 reads, the index for those reads, and the blacklist of regions to be excluded
 from 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
ChIP-seq
\end_layout

\end_inset

 analysis.
 Each step declares its inputs and outputs, and Snakemake uses these to
 determine the dependencies between steps.
 Each step is marked as depending on all the steps whose outputs match its
 inputs, generating the workflow graph in Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:rulegraph"
plural "false"
caps "false"
noprefix "false"

\end_inset

, which Snakemake uses to determine order in which to execute each step
 so that each step is executed only after all of the steps it depends on
 have completed, thereby automating the entire workflow from start to finish.
\end_layout

\begin_layout Standard
\begin_inset ERT
status open

\begin_layout Plain Layout


\backslash
afterpage{
\end_layout

\begin_layout Plain Layout


\backslash
begin{landscape}
\end_layout

\end_inset


\end_layout

\begin_layout Standard
\begin_inset Float figure
wide false
sideways false
status collapsed

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename graphics/CD4-csaw/rulegraphs/rulegraph-all.pdf
	lyxscale 50
	width 100col%
	height 95theight%

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout
\begin_inset Argument 1
status collapsed

\begin_layout Plain Layout
Dependency graph of steps in reproducible workflow.
\end_layout

\end_inset


\begin_inset CommandInset label
LatexCommand label
name "fig:rulegraph"

\end_inset


\series bold
Dependency graph of steps in reproducible workflow.
 
\series default
The analysis flows from left to right.
 Arrows indicate which analysis steps depend on the output of other steps.
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Standard
\begin_inset ERT
status open

\begin_layout Plain Layout


\backslash
end{landscape}
\end_layout

\begin_layout Plain Layout

}
\end_layout

\end_inset


\end_layout

\begin_layout Standard
In addition to simply making it easier to organize the steps in the analysis,
 structuring the analysis as a workflow allowed for some analysis strategies
 that would not have been practical otherwise.
 For example, 5 different 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
RNA-seq
\end_layout

\end_inset

 quantification methods were tested against two different reference transcriptom
e annotations for a total of 10 different quantifications of the same 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
RNA-seq
\end_layout

\end_inset

 data.
 These were then compared against each other in the exploratory data analysis
 step, to determine that the results were not very sensitive to either the
 choice of quantification method or the choice of annotation.
 This was possible with a single script for the exploratory data analysis,
 because Snakemake was able to automate running this script for every combinatio
n of method and reference.
 In a similar manner, two different peak calling methods were tested against
 each other, and in this case it was determined that 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
SICER
\end_layout

\end_inset

 was unambiguously superior to 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
MACS
\end_layout

\end_inset

 for all histone marks studied.
 By enabling these types of comparisons, structuring the analysis as an
 automated workflow allowed important analysis decisions to be made in a
 data-driven way, by running every reasonable option through the downstream
 steps, seeing the consequences of choosing each option, and deciding accordingl
y.
\end_layout

\begin_layout Section
Future Directions
\end_layout

\begin_layout Standard
The analysis of 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
RNA-seq
\end_layout

\end_inset

 and 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
ChIP-seq
\end_layout

\end_inset

 in CD4
\begin_inset Formula $^{+}$
\end_inset

  T-cells in Chapter 2 is in many ways a preliminary study that suggests
 a multitude of new avenues of investigation.
 Here we consider a selection of such avenues.
\end_layout

\begin_layout Subsection
Previous negative results
\end_layout

\begin_layout Standard
Two additional analyses were conducted beyond those reported in the results.
 First, we searched for evidence that the presence or absence of a 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
CpGi
\end_layout

\end_inset

 in the promoter was correlated with increases or decreases in gene expression
 or any histone mark in any of the tested contrasts.
 Second, we searched for evidence that the relative 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
ChIP-seq
\end_layout

\end_inset

 coverage profiles prior to activations could predict the change in expression
 of a gene after activation.
 Neither analysis turned up any clear positive results.
\end_layout

\begin_layout Subsection
Improve on the idea of an effective promoter radius
\end_layout

\begin_layout Standard
This study introduced the concept of an 
\begin_inset Quotes eld
\end_inset

effective promoter radius
\begin_inset Quotes erd
\end_inset

 specific to each histone mark based on distance from the 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
TSS
\end_layout

\end_inset

 within which an excess of peaks was called for that mark.
 This concept was then used to guide further analyses throughout the study.
 However, while the effective promoter radius was useful in those analyses,
 it is both limited in theory and shown in practice to be a possible oversimplif
ication.
 First, the effective promoter radii used in this study were chosen based
 on manual inspection of the TSS-to-peak distance distributions in Figure
 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:near-promoter-peak-enrich"
plural "false"
caps "false"
noprefix "false"

\end_inset

, selecting round numbers of analyst convenience (Table 
\begin_inset CommandInset ref
LatexCommand ref
reference "tab:effective-promoter-radius"
plural "false"
caps "false"
noprefix "false"

\end_inset

).
 It would be better to define an algorithm that selects a more precise radius
 based on the features of the graph.
 One possible way to do this would be to randomly rearrange the called peaks
 throughout the genome many (while preserving the distribution of peak widths)
 and re-generate the same plot as in Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:near-promoter-peak-enrich"
plural "false"
caps "false"
noprefix "false"

\end_inset

.
 This would yield a better 
\begin_inset Quotes eld
\end_inset

background
\begin_inset Quotes erd
\end_inset

 distribution that demonstrates the degree of near-TSS enrichment that would
 be expected by random chance.
 The effective promoter radius could be defined as the point where the true
 distribution diverges from the randomized background distribution.
 
\end_layout

\begin_layout Standard
Furthermore, the above definition of effective promoter radius has the significa
nt limitation of being based on the peak calling method.
 It is thus very sensitive to the choice of peak caller and significance
 threshold for calling peaks, as well as the degree of saturation in the
 sequencing.
 Calling peaks from 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
ChIP-seq
\end_layout

\end_inset

 samples with insufficient coverage depth, with the wrong peak caller, or
 with a different significance threshold could give a drastically different
 number of called peaks, and hence a drastically different distribution
 of peak-to-TSS distances.
 To address this, it is desirable to develop a better method of determining
 the effective promoter radius that relies only on the distribution of read
 coverage around the 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
TSS
\end_layout

\end_inset

, independent of the peak calling.
 Furthermore, as demonstrated by the upstream-downstream asymmetries observed
 in Figures 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:H3K4me2-neighborhood"
plural "false"
caps "false"
noprefix "false"

\end_inset

, 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:H3K4me3-neighborhood"
plural "false"
caps "false"
noprefix "false"

\end_inset

, and 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:H3K27me3-neighborhood"
plural "false"
caps "false"
noprefix "false"

\end_inset

, this definition should determine a different radius for the upstream and
 downstream directions.
 At this point, it may be better to rename this concept 
\begin_inset Quotes eld
\end_inset

effective promoter extent
\begin_inset Quotes erd
\end_inset

 and avoid the word 
\begin_inset Quotes eld
\end_inset

radius
\begin_inset Quotes erd
\end_inset

, since a radius implies a symmetry about the 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
TSS
\end_layout

\end_inset

 that is not supported by the data.
\end_layout

\begin_layout Standard
Beyond improving the definition of effective promoter extent, functional
 validation is necessary to show that this measure of near-TSS enrichment
 has biological meaning.
 Figures 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:H3K4me2-neighborhood"
plural "false"
caps "false"
noprefix "false"

\end_inset

 and 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:H3K4me3-neighborhood"
plural "false"
caps "false"
noprefix "false"

\end_inset

 already provide a very limited functional validation of the chosen promoter
 extents for H3K4me2 and H3K4me3 by showing that spikes in coverage within
 this region are most strongly correlated with elevated gene expression.
 However, there are other ways to show functional relevance of the promoter
 extent.
 For example, correlations could be computed between read counts in peaks
 nearby gene promoters and the expression level of those genes, and these
 correlations could be plotted against the distance of the peak upstream
 or downstream of the gene's 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
TSS
\end_layout

\end_inset

.
 If the promoter extent truly defines a 
\begin_inset Quotes eld
\end_inset

sphere of influence
\begin_inset Quotes erd
\end_inset

 within which a histone mark is involved with the regulation of a gene,
 then the correlations for peaks within this extent should be significantly
 higher than those further upstream or downstream.
 Peaks within these extents may also be more likely to show differential
 modification than those outside genic regions of the genome.
\end_layout

\begin_layout Subsection
Design experiments to focus on post-activation convergence of naïve & memory
 cells
\end_layout

\begin_layout Standard
In this study, a convergence between naïve and memory cells was observed
 in both the pattern of gene expression and in epigenetic state of the 3
 histone marks studied, consistent with the hypothesis that any naïve cells
 remaining 14 days after activation have differentiated into memory cells,
 and that both gene expression and these histone marks are involved in this
 differentiation.
 However, the current study was not designed with this specific hypothesis
 in mind, and it therefore has some deficiencies with regard to testing
 it.
 The memory CD4
\begin_inset Formula $^{+}$
\end_inset

 samples at day 14 do not resemble the memory samples at day 0, indicating
 that in the specific model of activation used for this experiment, the
 cells are not guaranteed to return to their original pre-activation state,
 or perhaps this process takes substantially longer than 14 days.
 This difference is expected, as the cell cultures in this experiment were
 treated with IL2 from day 5 onward 
\begin_inset CommandInset citation
LatexCommand cite
key "LaMere2016"
literal "false"

\end_inset

, so the signalling environments in which the cells are cultured are different
 at day 0 and day 14.
 This is a challenge for testing the convergence hypothesis because the
 ideal comparison to prove that naïve cells are converging to a resting
 memory state would be to compare the final naïve time point to the Day
 0 memory samples, but this comparison is only meaningful if memory cells
 generally return to the same 
\begin_inset Quotes eld
\end_inset

resting
\begin_inset Quotes erd
\end_inset

 state that they started at.
\end_layout

\begin_layout Standard
Because pre-culture and post-culture cells will probably never behave identicall
y even if they both nominally have a 
\begin_inset Quotes eld
\end_inset

resting
\begin_inset Quotes erd
\end_inset

 phenotype, a different experiment should be designed in which post-activation
 naive cells are compared to memory cells that were cultured for the same
 amount of time but never activated, in addition to post-activation memory
 cells.
 If the convergence hypothesis is correct, both post-activation cultures
 should converge on the culture of never-activated memory cells.
\end_layout

\begin_layout Standard
In addition, if naïve-to-memory convergence is a general pattern, it should
 also be detectable in other epigenetic marks, including other histone marks
 and DNA methylation.
 An experiment should be designed studying a large number of epigenetic
 marks known or suspected to be involved in regulation of gene expression,
 assaying all of these at the same pre- and post-activation time points.
 Multi-dataset factor analysis methods like 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
MOFA
\end_layout

\end_inset

 can then be used to identify coordinated patterns of regulation shared
 across many epigenetic marks.
 Of course, CD4
\begin_inset Formula $^{+}$
\end_inset

 T-cells are not the only adaptive immune cells that exhibit memory formation.
 A similar study could be designed for CD8
\begin_inset Formula $^{+}$
\end_inset

 T-cells, B-cells, and even specific subsets of CD4
\begin_inset Formula $^{+}$
\end_inset

 T-cells, such as Th1, Th2, Treg, and Th17 cells, to determine whether these
 also show convergence.
\end_layout

\begin_layout Subsection
Follow up on hints of interesting patterns in promoter relative coverage
 profiles
\end_layout

\begin_layout Standard
The analysis of promoter coverage landscapes in resting naive CD4
\begin_inset Formula $^{+}$
\end_inset

 T-cells and their correlations with gene expression raises many interesting
 questions.
 The chosen analysis strategy used a clustering approach, but this approach
 was subsequently shown to be a poor fit for the data.
 In light of this, a better means of dimension reduction for promoter landscape
 data is required.
 In the case of H3K4me2 and H3K4me3, one option is to define the first 3
 principal componets as orthogonal promoter 
\begin_inset Quotes eld
\end_inset

state variables
\begin_inset Quotes erd
\end_inset

: upstream vs downstream coverage, TSS-centered peak vs trough, and proximal
 upstream trough vs proximal downstream trough.
 Gene expression could then be modeled as a function of these three variables,
 or possibly as a function of the first 
\begin_inset Formula $N$
\end_inset

 principal components for 
\begin_inset Formula $N$
\end_inset

 larger than 3.
 For H3K4me2 and H3K4me3, a better representation might be obtained by transform
ing the first 2 principal coordinates into a polar coordinate system 
\begin_inset Formula $(r,\theta)$
\end_inset

 with the origin at the center of the 
\begin_inset Quotes eld
\end_inset

no peak
\begin_inset Quotes erd
\end_inset

 cluster, where the radius 
\begin_inset Formula $r$
\end_inset

 represents the peak height above the background and the angle 
\begin_inset Formula $\theta$
\end_inset

 represents the peak's position upstream or downstream of the 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
TSS
\end_layout

\end_inset

.
 
\end_layout

\begin_layout Standard
Another weakness in the current analysis is the normalization of the average
 abundance of each promoter to an average of zero.
 This allows the abundance value in each window to represent the relative
 abundance of that window compared to all the other windows in the interrogated
 area.
 However, while using the remainder of the windows to set the 
\begin_inset Quotes eld
\end_inset

background
\begin_inset Quotes erd
\end_inset

 level against which each window is normalized is convenient, it is far
 from optimal.
 As shown in Table 
\begin_inset CommandInset ref
LatexCommand ref
reference "tab:peak-calling-summary"
plural "false"
caps "false"
noprefix "false"

\end_inset

, many enriched regions are larger than the 5
\begin_inset space ~
\end_inset

kbp radius., which means there may not be any 
\begin_inset Quotes eld
\end_inset

background
\begin_inset Quotes erd
\end_inset

 regions within 5
\begin_inset space ~
\end_inset

kbp of the 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
TSS
\end_layout

\end_inset

 to normalize against.
 For example, this normalization strategy fails to distinguish between a
 trough in coverage at the 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
TSS
\end_layout

\end_inset

 and a pair of wide peaks upstream and downstream of the 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
TSS
\end_layout

\end_inset

.
 Both cases would present as lower coverage in the windows immediately adjacent
 to the 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
TSS
\end_layout

\end_inset

 and higher coverage in windows further away, but the functional implications
 of these two cases might be completely different.
 To improve the normalization, the background estimation method used by
 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
SICER
\end_layout

\end_inset

, which is specifically designed for finding broad regions of enrichment,
 should be adapted to estimate the background sequencing depth in each window
 from the 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
ChIP-seq
\end_layout

\end_inset

 input samples, and each window's read count should be normalized against
 the background and reported as a 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
logFC
\end_layout

\end_inset

 relative to that background.
\end_layout

\begin_layout Standard
Lastly, the analysis of promoter coverage landscapes presented in this work
 only looked at promoter coverage of resting naive CD4
\begin_inset Formula $^{+}$
\end_inset

 T-cells, with the goal of determining whether this initial promoter state
 was predictive of post-activation changes in gene expression.
 Changes in the promoter coverage landscape over time have not yet been
 considered.
 This represents a significant analysis challenge, by adding yet another
 dimension (genomic coordinate) in to the data.
\end_layout

\begin_layout Subsection
Investigate causes of high correlation between mutually exclusive histone
 marks
\end_layout

\begin_layout Standard
The high correlation between coverage depth observed between H3K4me2 and
 H3K4me3 is both expected and unexpected.
 Since both marks are associated with elevated gene transcription, a positive
 correlation between them is not surprising.
 However, these two marks represent different post-translational modifications
 of the 
\emph on
same
\emph default
 lysine residue on the histone H3 polypeptide, which means that they cannot
 both be present on the same H3 subunit.
 Thus, the high correlation between them has several potential explanations.
 One possible reason is cell population heterogeneity: perhaps some genomic
 loci are frequently marked with H3K4me2 in some cells, while in other cells
 the same loci are marked with H3K4me3.
 Another possibility is allele-specific modifications: the loci are marked
 in each diploid cell with H3K4me2 on one allele and H3K4me3 on the other
 allele.
 Lastly, since each histone octamer contains 2 H3 subunits, it is possible
 that having one H3K4me2 mark and one H3K4me3 mark on a given histone octamer
 represents a distinct epigenetic state with a different function than either
 double H3K4me2 or double H3K4me3.
 
\end_layout

\begin_layout Standard
The hypothesis of allele-specific histone modification can easily be tested
 with existing data by locating all heterozygous loci occurring within both
 H3K4me3 and H3K4me2 peaks and checking for opposite allelic imbalance between
 H3K4me3 and H3K4me2 read at each locus.
 If the allele fractions in the reads from the two histone marks for each
 locus are plotted against each other, there should be a negative correlation.
 If no such negative correlation is found, then allele-specific histone
 modification is unlikely to be the reason for the high correlation between
 these histone marks.
\end_layout

\begin_layout Standard
To test the hypothesis that H3K4me2 and H3K4me3 marks are occurring on the
 same histones.
 A double 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
ChIP
\end_layout

\end_inset

 experiment can be performed 
\begin_inset CommandInset citation
LatexCommand cite
key "Jin2007"
literal "false"

\end_inset

.
 In this assay, the input DNA goes through two sequential immunoprecipitations
 with different antibodies: first the anti-H3K4me2 antibody, then the anti-H3K4m
e3 antibody.
 Only bearing both histone marks, and the DNA associated with them, should
 be isolated.
 This can be followed by 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
HTS
\end_layout

\end_inset

 to form a 
\begin_inset Quotes eld
\end_inset

double 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
ChIP-seq
\end_layout

\end_inset


\begin_inset Quotes erd
\end_inset

 assay that can be used to identify DNA regions bound by the isolated histones
 
\begin_inset CommandInset citation
LatexCommand cite
key "Jin2009"
literal "false"

\end_inset

.
 If peaks called from this double 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
ChIP-seq
\end_layout

\end_inset

 assay are highly correlated with both H3K4me2 and H3K4me3 peaks, then this
 is strong evidence that the correlation between the two marks is actually
 caused by physical co-location on the same histone.
\end_layout

\begin_layout Chapter
\begin_inset CommandInset label
LatexCommand label
name "chap:Improving-array-based-diagnostic"

\end_inset

Improving array-based diagnostics for transplant rejection by optimizing
 data preprocessing
\end_layout

\begin_layout Standard

\size large
Ryan C.
 Thompson, Sunil M.
 Kurian, Thomas Whisnant, Padmaja Natarajan, Daniel R.
 Salomon
\end_layout

\begin_layout Standard
\begin_inset ERT
status collapsed

\begin_layout Plain Layout


\backslash
glsresetall
\end_layout

\end_inset


\begin_inset Note Note
status collapsed

\begin_layout Plain Layout
Reintroduce all abbreviations
\end_layout

\end_inset


\end_layout

\begin_layout Section
Introduction
\end_layout

\begin_layout Subsection
Proper pre-processing is essential for array data
\end_layout

\begin_layout Standard
Microarrays, bead arrays, and similar assays produce raw data in the form
 of fluorescence intensity measurements, with each intensity measurement
 proportional to the abundance of some fluorescently labelled target DNA
 or RNA sequence that base pairs to a specific probe sequence.
 However, the fluorescence measurements for each probe are also affected
 my many technical confounding factors, such as the concentration of target
 material, strength of off-target binding, the sensitivity of the imaging
 sensor, and visual artifacts in the image.
 Some array designs also use multiple probe sequences for each target.
 Hence, extensive pre-processing of array data is necessary to normalize
 out the effects of these technical factors and summarize the information
 from multiple probes to arrive at a single usable estimate of abundance
 or other relevant quantity, such as a ratio of two abundances, for each
 target 
\begin_inset CommandInset citation
LatexCommand cite
key "Gentleman2005"
literal "false"

\end_inset

.
\end_layout

\begin_layout Standard
The choice of pre-processing algorithms used in the analysis of an array
 data set can have a large effect on the results of that analysis.
 However, despite their importance, these steps are often neglected or rushed
 in order to get to the more scientifically interesting analysis steps involving
 the actual biology of the system under study.
 Hence, it is often possible to achieve substantial gains in statistical
 power, model goodness-of-fit, or other relevant performance measures, by
 checking the assumptions made by each preprocessing step and choosing specific
 normalization methods tailored to the specific goals of the current analysis.
\end_layout

\begin_layout Section
Approach
\end_layout

\begin_layout Subsection
Clinical diagnostic applications for microarrays require single-channel
 normalization
\end_layout

\begin_layout Standard
As the cost of performing microarray assays falls, there is increasing interest
 in using genomic assays for diagnostic purposes, such as distinguishing
 
\begin_inset ERT
status collapsed

\begin_layout Plain Layout


\backslash
glsdisp*{TX}{healthy transplants (TX)}
\end_layout

\end_inset

 from transplants undergoing 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
AR
\end_layout

\end_inset

 or 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
ADNR
\end_layout

\end_inset

.
 However, the the standard normalization algorithm used for microarray data,
 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
RMA
\end_layout

\end_inset

 
\begin_inset CommandInset citation
LatexCommand cite
key "Irizarry2003a"
literal "false"

\end_inset

, is not applicable in a clinical setting.
 Two of the steps in 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
RMA
\end_layout

\end_inset

, quantile normalization and probe summarization by median polish, depend
 on every array in the data set being normalized.
 This means that adding or removing any arrays from a data set changes the
 normalized values for all arrays, and data sets that have been normalized
 separately cannot be compared to each other.
 Hence, when using 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
RMA
\end_layout

\end_inset

, any arrays to be analyzed together must also be normalized together, and
 the set of arrays included in the data set must be held constant throughout
 an analysis.
\end_layout

\begin_layout Standard
These limitations present serious impediments to the use of arrays as a
 diagnostic tool.
 When training a classifier, the samples to be classified must not be involved
 in any step of the training process, lest their inclusion bias the training
 process.
 Once a classifier is deployed in a clinical setting, the samples to be
 classified will not even 
\emph on
exist
\emph default
 at the time of training, so including them would be impossible even if
 it were statistically justifiable.
 Therefore, any machine learning application for microarrays demands that
 the normalized expression values computed for an array must depend only
 on information contained within that array.
 This would ensure that each array's normalization is independent of every
 other array, and that arrays normalized separately can still be compared
 to each other without bias.
 Such a normalization is commonly referred to as 
\begin_inset Quotes eld
\end_inset

single-channel normalization
\begin_inset Quotes erd
\end_inset

.
\end_layout

\begin_layout Standard
\begin_inset Flex Glossary Term (Capital)
status open

\begin_layout Plain Layout
fRMA
\end_layout

\end_inset

 addresses these concerns by replacing the quantile normalization and median
 polish with alternatives that do not introduce inter-array dependence,
 allowing each array to be normalized independently of all others 
\begin_inset CommandInset citation
LatexCommand cite
key "McCall2010"
literal "false"

\end_inset

.
 Quantile normalization is performed against a pre-generated set of quantiles
 learned from a collection of 850 publicly available arrays sampled from
 a wide variety of tissues in 
\begin_inset ERT
status collapsed

\begin_layout Plain Layout


\backslash
glsdisp*{GEO}{the Gene Expression Omnibus (GEO)}
\end_layout

\end_inset

.
 Each array's probe intensity distribution is normalized against these pre-gener
ated quantiles.
 The median polish step is replaced with a robust weighted average of probe
 intensities, using inverse variance weights learned from the same public
 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
GEO
\end_layout

\end_inset

 data.
 The result is a normalization that satisfies the requirements mentioned
 above: each array is normalized independently of all others, and any two
 normalized arrays can be compared directly to each other.
\end_layout

\begin_layout Standard
One important limitation of 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
fRMA
\end_layout

\end_inset

 is that it requires a separate reference data set from which to learn the
 parameters (reference quantiles and probe weights) that will be used to
 normalize each array.
 These parameters are specific to a given array platform, and pre-generated
 parameters are only provided for the most common platforms, such as Affymetrix
 hgu133plus2.
 For a less common platform, such as hthgu133pluspm, is is necessary to
 learn custom parameters from in-house data before 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
fRMA
\end_layout

\end_inset

 can be used to normalize samples on that platform 
\begin_inset CommandInset citation
LatexCommand cite
key "McCall2011"
literal "false"

\end_inset

.
\end_layout

\begin_layout Standard
One other option is the aptly-named 
\begin_inset ERT
status collapsed

\begin_layout Plain Layout


\backslash
glsdisp*{SCAN}{Single Channel Array Normalization (SCAN)}
\end_layout

\end_inset

, which adapts a normalization method originally designed for tiling arrays
 
\begin_inset CommandInset citation
LatexCommand cite
key "Piccolo2012"
literal "false"

\end_inset

.
 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
SCAN
\end_layout

\end_inset

 is truly single-channel in that it does not require a set of normalization
 parameters estimated from an external set of reference samples like 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
fRMA
\end_layout

\end_inset

 does.
\end_layout

\begin_layout Subsection
Heteroskedasticity must be accounted for in methylation array data 
\end_layout

\begin_layout Standard
DNA methylation arrays are a relatively new kind of assay that uses microarrays
 to measure the degree of methylation on cytosines in specific regions arrayed
 across the genome.
 First, bisulfite treatment converts all unmethylated cytosines to uracil
 (which are read as thymine during amplification and sequencing) while leaving
 methylated cytosines unaffected.
 Then, each target region is interrogated with two probes: one binds to
 the original genomic sequence and interrogates the level of methylated
 DNA, and the other binds to the same sequence with all cytosines replaced
 by thymidines and interrogates the level of unmethylated DNA.
\end_layout

\begin_layout Standard
After normalization, these two probe intensities are summarized in one of
 two ways, each with advantages and disadvantages.
 β
\series bold
 
\series default
values, interpreted as fraction of DNA copies methylated, range from 0 to
 1.
 β
\series bold
 
\series default
values are conceptually easy to interpret, but the constrained range makes
 them unsuitable for linear modeling, and their error distributions are
 highly non-normal, which also frustrates linear modeling.
 
\begin_inset ERT
status collapsed

\begin_layout Plain Layout


\backslash
glsdisp*{M-value}{M-values}
\end_layout

\end_inset

, interpreted as the log ratios of methylated to unmethylated copies for
 each probe region, are computed by mapping the beta values from 
\begin_inset Formula $[0,1]$
\end_inset

 onto 
\begin_inset Formula $(-\infty,+\infty)$
\end_inset

 using a sigmoid curve (Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:Sigmoid-beta-m-mapping"
plural "false"
caps "false"
noprefix "false"

\end_inset

).
 This transformation results in values with better statistical properties:
 the unconstrained range is suitable for linear modeling, and the error
 distributions are more normal.
 Hence, most linear modeling and other statistical testing on methylation
 arrays is performed using 
\begin_inset Flex Glossary Term (pl)
status open

\begin_layout Plain Layout
M-value
\end_layout

\end_inset

.
\end_layout

\begin_layout Standard
\begin_inset Float figure
wide false
sideways false
status collapsed

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename graphics/methylvoom/sigmoid.pdf
	lyxscale 50
	width 60col%
	groupId colwidth

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout
\begin_inset Argument 1
status collapsed

\begin_layout Plain Layout
Sigmoid shape of the mapping between β and M values.
\end_layout

\end_inset


\begin_inset CommandInset label
LatexCommand label
name "fig:Sigmoid-beta-m-mapping"

\end_inset


\series bold
Sigmoid shape of the mapping between β and M values.
 
\series default
This mapping is monotonic and non-linear, but it is approximately linear
 in the neighborhood of 
\begin_inset Formula $(\beta=0.5,M=0)$
\end_inset

.
 
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Standard
However, the steep slope of the sigmoid transformation near 0 and 1 tends
 to over-exaggerate small differences in β values near those extremes, which
 in turn amplifies the error in those values, leading to a U-shaped trend
 in the mean-variance curve: extreme values have higher variances than values
 near the middle.
 This mean-variance dependency must be accounted for when fitting the linear
 model for differential methylation, or else the variance will be systematically
 overestimated for probes with moderate 
\begin_inset Flex Glossary Term (pl)
status open

\begin_layout Plain Layout
M-value
\end_layout

\end_inset

 and underestimated for probes with extreme 
\begin_inset Flex Glossary Term (pl)
status open

\begin_layout Plain Layout
M-value
\end_layout

\end_inset

.
 This is particularly undesirable for methylation data because the intermediate
 
\begin_inset Flex Glossary Term (pl)
status open

\begin_layout Plain Layout
M-value
\end_layout

\end_inset

 are the ones of most interest, since they are more likely to represent
 areas of varying methylation, whereas extreme 
\begin_inset Flex Glossary Term (pl)
status open

\begin_layout Plain Layout
M-value
\end_layout

\end_inset

 typically represent complete methylation or complete lack of methylation.
\end_layout

\begin_layout Standard
\begin_inset Flex Glossary Term (Capital)
status open

\begin_layout Plain Layout
RNA-seq
\end_layout

\end_inset

 read count data are also known to show heteroskedasticity, and the voom
 method was introduced for modeling this heteroskedasticity by estimating
 the mean-variance trend in the data and using this trend to assign precision
 weights to each observation 
\begin_inset CommandInset citation
LatexCommand cite
key "Law2014"
literal "false"

\end_inset

.
 While methylation array data are not derived from counts and have a very
 different mean-variance relationship from that of typical 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
RNA-seq
\end_layout

\end_inset

 data, the voom method makes no specific assumptions on the shape of the
 mean-variance relationship – it only assumes that the relationship can
 be modeled as a smooth curve.
 Hence, the method is sufficiently general to model the mean-variance relationsh
ip in methylation array data.
 However, while the method does not require count data as input, the standard
 implementation of voom assumes that the input is given in raw read counts,
 and it must be adapted to run on methylation 
\begin_inset Flex Glossary Term (pl)
status open

\begin_layout Plain Layout
M-value
\end_layout

\end_inset

.
\end_layout

\begin_layout Section
Methods
\end_layout

\begin_layout Subsection
Evaluation of classifier performance with different normalization methods
\end_layout

\begin_layout Standard
For testing different expression microarray normalizations, a data set of
 157 hgu133plus2 arrays was used, consisting of blood samples from kidney
 transplant patients whose grafts had been graded as 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
TX
\end_layout

\end_inset

, 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
AR
\end_layout

\end_inset

, or 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
ADNR
\end_layout

\end_inset

 via biopsy and histology (46 TX, 69 AR, 42 ADNR) 
\begin_inset CommandInset citation
LatexCommand cite
key "Kurian2014"
literal "true"

\end_inset

.
 Additionally, an external validation set of 75 samples was gathered from
 public 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
GEO
\end_layout

\end_inset

 data (37 TX, 38 AR, no ADNR).
 
\end_layout

\begin_layout Standard
\begin_inset Flex TODO Note (inline)
status open

\begin_layout Plain Layout
Find appropriate GEO identifiers if possible.
 Kurian 2014 says GSE15296, but this seems to be different data.
 I also need to look up the GEO accession for the external validation set.
\end_layout

\end_inset


\end_layout

\begin_layout Standard
To evaluate the effect of each normalization on classifier performance,
 the same classifier training and validation procedure was used after each
 normalization method.
 The 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
PAM
\end_layout

\end_inset

 algorithm was used to train a nearest shrunken centroid classifier on the
 training set and select the appropriate threshold for centroid shrinking
 
\begin_inset CommandInset citation
LatexCommand cite
key "Tibshirani2002"
literal "false"

\end_inset

.
 Then the trained classifier was used to predict the class probabilities
 of each validation sample.
 From these class probabilities, 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
ROC
\end_layout

\end_inset

 curves and 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
AUC
\end_layout

\end_inset

 values were generated 
\begin_inset CommandInset citation
LatexCommand cite
key "Turck2011"
literal "false"

\end_inset

.
 Each normalization was tested on two different sets of training and validation
 samples.
 For internal validation, the 115 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
TX
\end_layout

\end_inset

 and 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
AR
\end_layout

\end_inset

 arrays in the internal set were split at random into two equal sized sets,
 one for training and one for validation, each containing the same numbers
 of 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
TX
\end_layout

\end_inset

 and 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
AR
\end_layout

\end_inset

 samples as the other set.
 For external validation, the full set of 115 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
TX
\end_layout

\end_inset

 and 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
AR
\end_layout

\end_inset

 samples were used as a training set, and the 75 external 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
TX
\end_layout

\end_inset

 and 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
AR
\end_layout

\end_inset

 samples were used as the validation set.
 Thus, 2 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
ROC
\end_layout

\end_inset

 curves and 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
AUC
\end_layout

\end_inset

 values were generated for each normalization method: one internal and one
 external.
 Because the external validation set contains no 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
ADNR
\end_layout

\end_inset

 samples, only classification of 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
TX
\end_layout

\end_inset

 and 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
AR
\end_layout

\end_inset

 samples was considered.
 The 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
ADNR
\end_layout

\end_inset

 samples were included during normalization but excluded from all classifier
 training and validation.
 This ensures that the performance on internal and external validation sets
 is directly comparable, since both are performing the same task: distinguishing
 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
TX
\end_layout

\end_inset

 from 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
AR
\end_layout

\end_inset

.
\end_layout

\begin_layout Standard
\begin_inset Flex TODO Note (inline)
status open

\begin_layout Plain Layout
Summarize the get.best.threshold algorithm for PAM threshold selection, or
 just put the code online?
\end_layout

\end_inset


\end_layout

\begin_layout Standard
Six different normalization strategies were evaluated.
 First, 2 well-known non-single-channel normalization methods were considered:
 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
RMA
\end_layout

\end_inset

 and dChip 
\begin_inset CommandInset citation
LatexCommand cite
key "Li2001,Irizarry2003a"
literal "false"

\end_inset

.
 Since 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
RMA
\end_layout

\end_inset

 produces expression values on a 
\begin_inset Formula $\log_{2}$
\end_inset

 scale and dChip does not, the values from dChip were 
\begin_inset Formula $\log_{2}$
\end_inset

 transformed after normalization.
 Next, 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
RMA
\end_layout

\end_inset

 and dChip followed by 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
GRSN
\end_layout

\end_inset

 were tested 
\begin_inset CommandInset citation
LatexCommand cite
key "Pelz2008"
literal "false"

\end_inset

.
 Post-processing with 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
GRSN
\end_layout

\end_inset

 does not turn 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
RMA
\end_layout

\end_inset

 or dChip into single-channel methods, but it may help mitigate batch effects
 and is therefore useful as a benchmark.
 Lastly, the two single-channel normalization methods, 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
fRMA
\end_layout

\end_inset

 and 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
SCAN
\end_layout

\end_inset

, were tested 
\begin_inset CommandInset citation
LatexCommand cite
key "McCall2010,Piccolo2012"
literal "false"

\end_inset

.
 When evaluating internal validation performance, only the 157 internal
 samples were normalized; when evaluating external validation performance,
 all 157 internal samples and 75 external samples were normalized together.
\end_layout

\begin_layout Standard
For demonstrating the problem with separate normalization of training and
 validation data, one additional normalization was performed: the internal
 and external sets were each normalized separately using 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
RMA
\end_layout

\end_inset

, and the normalized data for each set were combined into a single set with
 no further attempts at normalizing between the two sets.
 This represents approximately how 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
RMA
\end_layout

\end_inset

 would have to be used in a clinical setting, where the samples to be classified
 are not available at the time the classifier is trained.
\end_layout

\begin_layout Subsection
Generating custom fRMA vectors for hthgu133pluspm array platform
\end_layout

\begin_layout Standard
In order to enable 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
fRMA
\end_layout

\end_inset

 normalization for the hthgu133pluspm array platform, custom 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
fRMA
\end_layout

\end_inset

 normalization vectors were trained using the 
\begin_inset Flex Code
status open

\begin_layout Plain Layout
frmaTools
\end_layout

\end_inset

 package 
\begin_inset CommandInset citation
LatexCommand cite
key "McCall2011"
literal "false"

\end_inset

.
 Separate vectors were created for two types of samples: kidney graft biopsy
 samples and blood samples from graft recipients.
 For training, 341 kidney biopsy samples from 2 data sets and 965 blood
 samples from 5 data sets were used as the reference set.
 Arrays were groups into batches based on unique combinations of sample
 type (blood or biopsy), diagnosis (TX, AR, etc.), data set, and scan date.
 Thus, each batch represents arrays of the same kind that were run together
 on the same day.
 For estimating the probe inverse variance weights, frmaTools requires equal-siz
ed batches, which means a batch size must be chosen, and then batches smaller
 than that size must be ignored, while batches larger than the chosen size
 must be downsampled.
 This downsampling is performed randomly, so the sampling process is repeated
 5 times and the resulting normalizations are compared to each other.
\end_layout

\begin_layout Standard
To evaluate the consistency of the generated normalization vectors, the
 5 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
fRMA
\end_layout

\end_inset

 vector sets generated from 5 random batch samplings were each used to normalize
 the same 20 randomly selected samples from each tissue.
 Then the normalized expression values for each probe on each array were
 compared across all normalizations.
 Each 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
fRMA
\end_layout

\end_inset

 normalization was also compared against the normalized expression values
 obtained by normalizing the same 20 samples with ordinary 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
RMA
\end_layout

\end_inset

.
\end_layout

\begin_layout Subsection
Modeling methylation array M-value heteroskedasticity with a modified voom
 implementation
\end_layout

\begin_layout Standard
\begin_inset Flex TODO Note (inline)
status open

\begin_layout Plain Layout
Put code on Github and reference it.
\end_layout

\end_inset


\end_layout

\begin_layout Standard
To investigate the whether DNA methylation could be used to distinguish
 between healthy and dysfunctional transplants, a data set of 78 Illumina
 450k methylation arrays from human kidney graft biopsies was analyzed for
 differential methylation between 4 transplant statuses: 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
TX
\end_layout

\end_inset

, transplants undergoing 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
AR
\end_layout

\end_inset

, 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
ADNR
\end_layout

\end_inset

, and 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
CAN
\end_layout

\end_inset

.
 The data consisted of 33 TX, 9 AR, 8 ADNR, and 28 CAN samples.
 The uneven group sizes are a result of taking the biopsy samples before
 the eventual fate of the transplant was known.
 Each sample was additionally annotated with a donor 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
ID
\end_layout

\end_inset

 (anonymized), sex, age, ethnicity, creatinine level, and diabetes diagnosis
 (all samples in this data set came from patients with either 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
T1D
\end_layout

\end_inset

 or 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
T2D
\end_layout

\end_inset

).
 
\end_layout

\begin_layout Standard
The intensity data were first normalized using 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
SWAN
\end_layout

\end_inset

 
\begin_inset CommandInset citation
LatexCommand cite
key "Maksimovic2012"
literal "false"

\end_inset

, then converted to intensity ratios (beta values) 
\begin_inset CommandInset citation
LatexCommand cite
key "Aryee2014"
literal "false"

\end_inset

.
 Any probes binding to loci that overlapped annotated SNPs were dropped,
 and the annotated sex of each sample was verified against the sex inferred
 from the ratio of median probe intensities for the X and Y chromosomes.
 Then, the ratios were transformed to 
\begin_inset Flex Glossary Term (pl)
status open

\begin_layout Plain Layout
M-value
\end_layout

\end_inset

.
\end_layout

\begin_layout Standard
\begin_inset Float table
wide false
sideways false
status collapsed

\begin_layout Plain Layout
\align center
\begin_inset Tabular
<lyxtabular version="3" rows="4" columns="6">
<features tabularvalignment="middle">
<column alignment="center" valignment="top">
<column alignment="center" valignment="top">
<column alignment="center" valignment="top">
<column alignment="center" valignment="top">
<column alignment="center" valignment="top">
<column alignment="center" valignment="top">
<row>
<cell alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
Analysis
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
random effect
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
eBayes
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
SVA
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
weights
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" rightline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
voom
\end_layout

\end_inset
</cell>
</row>
<row>
<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
A
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
Yes
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
Yes
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
No
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
No
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" leftline="true" rightline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
No
\end_layout

\end_inset
</cell>
</row>
<row>
<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
B
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
Yes
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
Yes
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
Yes
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
Yes
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" leftline="true" rightline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
No
\end_layout

\end_inset
</cell>
</row>
<row>
<cell alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
C
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
Yes
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
Yes
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
Yes
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
Yes
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" rightline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
Yes
\end_layout

\end_inset
</cell>
</row>
</lyxtabular>

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout
\begin_inset Argument 1
status collapsed

\begin_layout Plain Layout
Summary of analysis variants for methylation array data.
\end_layout

\end_inset


\begin_inset CommandInset label
LatexCommand label
name "tab:Summary-of-meth-analysis"

\end_inset


\series bold
Summary of analysis variants for methylation array data.
 
\series default
Each analysis included a different set of steps to adjust or account for
 various systematic features of the data.
 Random effect: The model included a random effect accounting for correlation
 between samples from the same patient 
\begin_inset CommandInset citation
LatexCommand cite
key "Smyth2005a"
literal "false"

\end_inset

; eBayes: Empirical bayes squeezing of per-probe variances toward the mean-varia
nce trend 
\begin_inset CommandInset citation
LatexCommand cite
key "Ritchie2015"
literal "false"

\end_inset

; SVA: Surrogate variable analysis to account for unobserved confounders
 
\begin_inset CommandInset citation
LatexCommand cite
key "Leek2007"
literal "false"

\end_inset

; Weights: Estimate sample weights to account for differences in sample
 quality 
\begin_inset CommandInset citation
LatexCommand cite
key "Liu2015,Ritchie2006"
literal "false"

\end_inset

; voom: Use mean-variance trend to assign individual sample weights 
\begin_inset CommandInset citation
LatexCommand cite
key "Law2014"
literal "false"

\end_inset

.
 See the text for a more detailed explanation of each step.
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Standard
From the 
\begin_inset Flex Glossary Term (pl)
status open

\begin_layout Plain Layout
M-value
\end_layout

\end_inset

, a series of parallel analyses was performed, each adding additional steps
 into the model fit to accommodate a feature of the data (see Table 
\begin_inset CommandInset ref
LatexCommand ref
reference "tab:Summary-of-meth-analysis"
plural "false"
caps "false"
noprefix "false"

\end_inset

).
 For analysis A, a 
\begin_inset Quotes eld
\end_inset

basic
\begin_inset Quotes erd
\end_inset

 linear modeling analysis was performed, compensating for known confounders
 by including terms for the factor of interest (transplant status) as well
 as the known biological confounders: sex, age, ethnicity, and diabetes.
 Since some samples came from the same patients at different times, the
 intra-patient correlation was modeled as a random effect, estimating a
 shared correlation value across all probes 
\begin_inset CommandInset citation
LatexCommand cite
key "Smyth2005a"
literal "false"

\end_inset

.
 Then the linear model was fit, and the variance was modeled using empirical
 Bayes squeezing toward the mean-variance trend 
\begin_inset CommandInset citation
LatexCommand cite
key "Ritchie2015"
literal "false"

\end_inset

.
 Finally, t-tests or F-tests were performed as appropriate for each test:
 t-tests for single contrasts, and F-tests for multiple contrasts.
 P-values were corrected for multiple testing using the 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
BH
\end_layout

\end_inset

 procedure for 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
FDR
\end_layout

\end_inset

 control 
\begin_inset CommandInset citation
LatexCommand cite
key "Benjamini1995"
literal "false"

\end_inset

.
\end_layout

\begin_layout Standard
For the analysis B, 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
SVA
\end_layout

\end_inset

 was used to infer additional unobserved sources of heterogeneity in the
 data 
\begin_inset CommandInset citation
LatexCommand cite
key "Leek2007"
literal "false"

\end_inset

.
 These surrogate variables were added to the design matrix before fitting
 the linear model.
 In addition, sample quality weights were estimated from the data and used
 during linear modeling to down-weight the contribution of highly variable
 arrays while increasing the weight to arrays with lower variability
\begin_inset CommandInset citation
LatexCommand cite
key "Ritchie2006"
literal "false"

\end_inset

.
 The remainder of the analysis proceeded as in analysis A.
 For analysis C, the voom method was adapted to run on methylation array
 data and used to model and correct for the mean-variance trend using individual
 observation weights 
\begin_inset CommandInset citation
LatexCommand cite
key "Law2014"
literal "false"

\end_inset

, which were combined with the sample weights 
\begin_inset CommandInset citation
LatexCommand cite
key "Liu2015,Ritchie2006"
literal "false"

\end_inset

.
 Each time weights were used, they were estimated once before estimating
 the random effect correlation value, and then the weights were re-estimated
 taking the random effect into account.
 The remainder of the analysis proceeded as in analysis B.
\end_layout

\begin_layout Section
Results
\end_layout

\begin_layout Subsection
Separate normalization with RMA introduces unwanted biases in classification
\end_layout

\begin_layout Standard
To demonstrate the problem with non-single-channel normalization methods,
 we considered the problem of training a classifier to distinguish 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
TX
\end_layout

\end_inset

 from 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
AR
\end_layout

\end_inset

 using the samples from the internal set as training data, evaluating performanc
e on the external set.
 First, training and evaluation were performed after normalizing all array
 samples together as a single set using 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
RMA
\end_layout

\end_inset

, and second, the internal samples were normalized separately from the external
 samples and the training and evaluation were repeated.
 For each sample in the validation set, the classifier probabilities from
 both classifiers were plotted against each other (Fig.
 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:Classifier-probabilities-RMA"
plural "false"
caps "false"
noprefix "false"

\end_inset

).
 As expected, separate normalization biases the classifier probabilities,
 resulting in several misclassifications.
 In this case, the bias from separate normalization causes the classifier
 to assign a lower probability of 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
AR
\end_layout

\end_inset

 to every sample.
 
\end_layout

\begin_layout Standard
\begin_inset Float figure
wide false
sideways false
status collapsed

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename graphics/PAM/predplot.pdf
	lyxscale 50
	width 60col%
	groupId colwidth

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout
\begin_inset Argument 1
status collapsed

\begin_layout Plain Layout
Classifier probabilities on validation samples when normalized with RMA
 together vs.
 separately.
\end_layout

\end_inset


\begin_inset CommandInset label
LatexCommand label
name "fig:Classifier-probabilities-RMA"

\end_inset


\series bold
Classifier probabilities on validation samples when normalized with RMA
 together vs.
 separately.
 
\series default
The PAM classifier algorithm was trained on the training set of arrays to
 distinguish AR from TX and then used to assign class probabilities to the
 validation set.
 The process was performed after normalizing all samples together and after
 normalizing the training and test sets separately, and the class probabilities
 assigned to each sample in the validation set were plotted against each
 other.
 Each axis indicates the posterior probability of AR assigned to a sample
 by the classifier in the specified analysis.
 The color of each point indicates the true classification of that sample.
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Subsection
fRMA and SCAN maintain classification performance while eliminating dependence
 on normalization strategy
\end_layout

\begin_layout Standard
For internal validation, the 6 methods' AUC values ranged from 0.816 to 0.891,
 as shown in Table 
\begin_inset CommandInset ref
LatexCommand ref
reference "tab:AUC-PAM"
plural "false"
caps "false"
noprefix "false"

\end_inset

.
 Among the non-single-channel normalizations, dChip outperformed 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
RMA
\end_layout

\end_inset

, while 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
GRSN
\end_layout

\end_inset

 reduced the 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
AUC
\end_layout

\end_inset

 values for both dChip and 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
RMA
\end_layout

\end_inset

.
 Both single-channel methods, 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
fRMA
\end_layout

\end_inset

 and 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
SCAN
\end_layout

\end_inset

, slightly outperformed 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
RMA
\end_layout

\end_inset

, with 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
fRMA
\end_layout

\end_inset

 ahead of 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
SCAN
\end_layout

\end_inset

.
 However, the difference between 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
RMA
\end_layout

\end_inset

 and 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
fRMA
\end_layout

\end_inset

 is still quite small.
 Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:ROC-PAM-int"
plural "false"
caps "false"
noprefix "false"

\end_inset

 shows that the 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
ROC
\end_layout

\end_inset

 curves for 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
RMA
\end_layout

\end_inset

, dChip, and 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
fRMA
\end_layout

\end_inset

 look very similar and relatively smooth, while both 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
GRSN
\end_layout

\end_inset

 curves and the curve for 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
SCAN
\end_layout

\end_inset

 have a more jagged appearance.
\end_layout

\begin_layout Standard
\begin_inset Float figure
wide false
sideways false
status collapsed

\begin_layout Plain Layout
\align center
\begin_inset Float figure
placement tb
wide false
sideways false
status open

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename graphics/PAM/ROC-TXvsAR-internal.pdf
	lyxscale 50
	height 40theight%
	groupId roc-pam

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout
\begin_inset CommandInset label
LatexCommand label
name "fig:ROC-PAM-int"

\end_inset

ROC curves for PAM on internal validation data
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Plain Layout
\align center
\begin_inset Float figure
placement tb
wide false
sideways false
status open

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename graphics/PAM/ROC-TXvsAR-external.pdf
	lyxscale 50
	height 40theight%
	groupId roc-pam

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout
\begin_inset CommandInset label
LatexCommand label
name "fig:ROC-PAM-ext"

\end_inset

ROC curves for PAM on external validation data
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout
\begin_inset Argument 1
status collapsed

\begin_layout Plain Layout
ROC curves for PAM using different normalization strategies.
\end_layout

\end_inset


\begin_inset CommandInset label
LatexCommand label
name "fig:ROC-PAM-main"

\end_inset


\series bold
ROC curves for PAM using different normalization strategies.
 
\series default
ROC curves were generated for PAM classification of AR vs TX after 6 different
 normalization strategies applied to the same data sets.
 Only fRMA and SCAN are single-channel normalizations.
 The other normalizations are for comparison.
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Standard
\begin_inset Float table
wide false
sideways false
status collapsed

\begin_layout Plain Layout
\align center
\begin_inset Tabular
<lyxtabular version="3" rows="7" columns="4">
<features tabularvalignment="middle">
<column alignment="center" valignment="top">
<column alignment="center" valignment="top">
<column alignment="center" valignment="top">
<column alignment="center" valignment="top">
<row>
<cell alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout

\family roman
\series medium
\shape up
\size normal
\emph off
\bar no
\strikeout off
\xout off
\uuline off
\uwave off
\noun off
\color none
Normalization
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
Single-channel?
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout

\family roman
\series medium
\shape up
\size normal
\emph off
\bar no
\strikeout off
\xout off
\uuline off
\uwave off
\noun off
\color none
Internal Val.
 AUC
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" rightline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
External Val.
 AUC
\end_layout

\end_inset
</cell>
</row>
<row>
<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout

\family roman
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\color none
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<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
\begin_inset Text

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\end_layout

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<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
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RMA + GRSN
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
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\end_layout

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</cell>
<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout

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<cell alignment="center" valignment="top" topline="true" leftline="true" rightline="true" usebox="none">
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\color none
0.750
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\end_inset
</cell>
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<row>
<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
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\color none
dChip + GRSN
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
No
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout

\family roman
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</cell>
<cell alignment="center" valignment="top" topline="true" leftline="true" rightline="true" usebox="none">
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\begin_layout Plain Layout

\family roman
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\color none
0.642
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</cell>
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<row>
<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
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\series medium
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\color none
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\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
Yes
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<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
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0.718
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</cell>
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<row>
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\begin_layout Plain Layout

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\color none
SCAN
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
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<cell alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" usebox="none">
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</lyxtabular>

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\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout
\begin_inset Argument 1
status collapsed

\begin_layout Plain Layout
ROC curve AUC values for internal and external validation with 6 different
 normalization strategies.
\end_layout

\end_inset


\begin_inset CommandInset label
LatexCommand label
name "tab:AUC-PAM"

\end_inset


\series bold
ROC curve AUC values for internal and external validation with 6 different
 normalization strategies.

\series default
 These AUC values correspond to the ROC curves in Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:ROC-PAM-main"
plural "false"
caps "false"
noprefix "false"

\end_inset

.
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Standard
For external validation, as expected, all the 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
AUC
\end_layout

\end_inset

 values are lower than the internal validations, ranging from 0.642 to 0.750
 (Table 
\begin_inset CommandInset ref
LatexCommand ref
reference "tab:AUC-PAM"
plural "false"
caps "false"
noprefix "false"

\end_inset

).
 With or without 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
GRSN
\end_layout

\end_inset

, 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
RMA
\end_layout

\end_inset

 shows its dominance over dChip in this more challenging test.
 Unlike in the internal validation, 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
GRSN
\end_layout

\end_inset

 actually improves the classifier performance for 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
RMA
\end_layout

\end_inset

, although it does not for dChip.
 Once again, both single-channel methods perform about on par with 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
RMA
\end_layout

\end_inset

, with 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
fRMA
\end_layout

\end_inset

 performing slightly better and 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
SCAN
\end_layout

\end_inset

 performing a bit worse.
 Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:ROC-PAM-ext"
plural "false"
caps "false"
noprefix "false"

\end_inset

 shows the 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
ROC
\end_layout

\end_inset

 curves for the external validation test.
 As expected, none of them are as clean-looking as the internal validation
 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
ROC
\end_layout

\end_inset

 curves.
 The curves for 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
RMA
\end_layout

\end_inset

, RMA+GRSN, and 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
fRMA
\end_layout

\end_inset

 all look similar, while the other curves look more divergent.
\end_layout

\begin_layout Subsection
fRMA with custom-generated vectors enables single-channel normalization
 on hthgu133pluspm platform
\end_layout

\begin_layout Standard
In order to enable use of 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
fRMA
\end_layout

\end_inset

 to normalize hthgu133pluspm, a custom set of 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
fRMA
\end_layout

\end_inset

 vectors was created.
 First, an appropriate batch size was chosen by looking at the number of
 batches and number of samples included as a function of batch size (Figure
 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:frmatools-batch-size"
plural "false"
caps "false"
noprefix "false"

\end_inset

).
 For a given batch size, all batches with fewer samples that the chosen
 size must be ignored during training, while larger batches must be randomly
 downsampled to the chosen size.
 Hence, the number of samples included for a given batch size equals the
 batch size times the number of batches with at least that many samples.
 From Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:batch-size-samples"
plural "false"
caps "false"
noprefix "false"

\end_inset

, it is apparent that a batch size of 8 maximizes the number of samples
 included in training.
 Increasing the batch size beyond this causes too many smaller batches to
 be excluded, reducing the total number of samples for both tissue types.
 However, a batch size of 8 is not necessarily optimal.
 The article introducing frmaTools concluded that it was highly advantageous
 to use a smaller batch size in order to include more batches, even at the
 cost of including fewer total samples in training 
\begin_inset CommandInset citation
LatexCommand cite
key "McCall2011"
literal "false"

\end_inset

.
 To strike an appropriate balance between more batches and more samples,
 a batch size of 5 was chosen.
 For both blood and biopsy samples, this increased the number of batches
 included by 10, with only a modest reduction in the number of samples compared
 to a batch size of 8.
 With a batch size of 5, 26 batches of biopsy samples and 46 batches of
 blood samples were available.
\end_layout

\begin_layout Standard
\begin_inset Float figure
wide false
sideways false
status collapsed

\begin_layout Plain Layout
\align center
\begin_inset Float figure
placement tb
wide false
sideways false
status collapsed

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename graphics/frma-pax-bx/batchsize_batches.pdf
	lyxscale 50
	height 35theight%
	groupId frmatools-subfig

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout
\begin_inset CommandInset label
LatexCommand label
name "fig:batch-size-batches"

\end_inset


\series bold
Number of batches usable in fRMA probe weight learning as a function of
 batch size.
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Plain Layout
\align center
\begin_inset Float figure
placement tb
wide false
sideways false
status collapsed

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename graphics/frma-pax-bx/batchsize_samples.pdf
	lyxscale 50
	height 35theight%
	groupId frmatools-subfig

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout
\begin_inset CommandInset label
LatexCommand label
name "fig:batch-size-samples"

\end_inset


\series bold
Number of samples usable in fRMA probe weight learning as a function of
 batch size.
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout
\begin_inset Argument 1
status collapsed

\begin_layout Plain Layout
Effect of batch size selection on number of batches and number of samples
 included in fRMA probe weight learning.
\end_layout

\end_inset


\begin_inset CommandInset label
LatexCommand label
name "fig:frmatools-batch-size"

\end_inset


\series bold
Effect of batch size selection on number of batches and number of samples
 included in fRMA probe weight learning.
 
\series default
For batch sizes ranging from 3 to 15, the number of batches (a) and samples
 (b) included in probe weight training were plotted for biopsy (BX) and
 blood (PAX) samples.
 The selected batch size, 5, is marked with a dotted vertical line.
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Standard
Since 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
fRMA
\end_layout

\end_inset

 training requires equal-size batches, larger batches are downsampled randomly.
 This introduces a nondeterministic step in the generation of normalization
 vectors.
 To show that this randomness does not substantially change the outcome,
 the random downsampling and subsequent vector learning was repeated 5 times,
 with a different random seed each time.
 20 samples were selected at random as a test set and normalized with each
 of the 5 sets of 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
fRMA
\end_layout

\end_inset

 normalization vectors as well as ordinary RMA, and the normalized expression
 values were compared across normalizations.
 Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:m-bx-violin"
plural "false"
caps "false"
noprefix "false"

\end_inset

 shows a summary of these comparisons for biopsy samples.
 Comparing RMA to each of the 5 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
fRMA
\end_layout

\end_inset

 normalizations, the distribution of log ratios is somewhat wide, indicating
 that the normalizations disagree on the expression values of a fair number
 of probe sets.
 In contrast, comparisons of 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
fRMA
\end_layout

\end_inset

 against 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
fRMA
\end_layout

\end_inset

, the vast majority of probe sets have very small log ratios, indicating
 a very high agreement between the normalized values generated by the two
 normalizations.
 This shows that the 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
fRMA
\end_layout

\end_inset

 normalization's behavior is not very sensitive to the random downsampling
 of larger batches during training.
\end_layout

\begin_layout Standard
\begin_inset Float figure
wide false
sideways false
status collapsed

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename graphics/frma-pax-bx/M-BX-violin.pdf
	lyxscale 40
	height 90theight%
	groupId m-violin

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout
\begin_inset Argument 1
status collapsed

\begin_layout Plain Layout
Violin plot of log ratios between normalizations for 20 biopsy samples.
\end_layout

\end_inset


\begin_inset CommandInset label
LatexCommand label
name "fig:m-bx-violin"

\end_inset


\series bold
Violin plot of log ratios between normalizations for 20 biopsy samples.
 
\series default
Each of 20 randomly selected samples was normalized with RMA and with 5
 different sets of fRMA vectors.
 The distribution of log ratios between normalized expression values, aggregated
 across all 20 arrays, was plotted for each pair of normalizations.
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Standard
\begin_inset Float figure
wide false
sideways false
status collapsed

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename graphics/frma-pax-bx/M-PAX-violin.pdf
	lyxscale 40
	height 90theight%
	groupId m-violin

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout
\begin_inset CommandInset label
LatexCommand label
name "fig:m-pax-violin"

\end_inset


\begin_inset Argument 1
status open

\begin_layout Plain Layout
Violin plot of log ratios between normalizations for 20 blood samples.
\end_layout

\end_inset


\series bold
Violin plot of log ratios between normalizations for 20 blood samples.
 
\series default
Each of 20 randomly selected samples was normalized with RMA and with 5
 different sets of fRMA vectors.
 The distribution of log ratios between normalized expression values, aggregated
 across all 20 arrays, was plotted for each pair of normalizations.
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Standard
Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:ma-bx-rma-frma"
plural "false"
caps "false"
noprefix "false"

\end_inset

 shows an MA plot of the RMA-normalized values against the fRMA-normalized
 values for the same probe sets and arrays, corresponding to the first row
 of Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:m-bx-violin"
plural "false"
caps "false"
noprefix "false"

\end_inset

.
 This MA plot shows that not only is there a wide distribution of 
\begin_inset Flex Glossary Term (pl)
status open

\begin_layout Plain Layout
M-value
\end_layout

\end_inset

, but the trend of 
\begin_inset Flex Glossary Term (pl)
status open

\begin_layout Plain Layout
M-value
\end_layout

\end_inset

 is dependent on the average normalized intensity.
 This is expected, since the overall trend represents the differences in
 the quantile normalization step.
 When running 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
RMA
\end_layout

\end_inset

, only the quantiles for these specific 20 arrays are used, while for 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
fRMA
\end_layout

\end_inset

 the quantile distribution is taking from all arrays used in training.
 Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:ma-bx-frma-frma"
plural "false"
caps "false"
noprefix "false"

\end_inset

 shows a similar MA plot comparing 2 different 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
fRMA
\end_layout

\end_inset

 normalizations, corresponding to the 6th row of Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:m-bx-violin"
plural "false"
caps "false"
noprefix "false"

\end_inset

.
 The MA plot is very tightly centered around zero with no visible trend.
 Figures 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:m-pax-violin"
plural "false"
caps "false"
noprefix "false"

\end_inset

, 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:MA-PAX-rma-frma"
plural "false"
caps "false"
noprefix "false"

\end_inset

, and 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:ma-bx-frma-frma"
plural "false"
caps "false"
noprefix "false"

\end_inset

 show exactly the same information for the blood samples, once again comparing
 the normalized expression values between normalizations for all probe sets
 across 20 randomly selected test arrays.
 Once again, there is a wider distribution of log ratios between RMA-normalized
 values and fRMA-normalized, and a much tighter distribution when comparing
 different 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
fRMA
\end_layout

\end_inset

 normalizations to each other, indicating that the 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
fRMA
\end_layout

\end_inset

 training process is robust to random batch sub-sampling for the blood samples
 as well.
\end_layout

\begin_layout Standard
\begin_inset Float figure
wide false
sideways false
status collapsed

\begin_layout Plain Layout
\align center
\begin_inset Float figure
wide false
sideways false
status open

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename graphics/frma-pax-bx/MA-BX-RMA.fRMA-RASTER.png
	lyxscale 10
	width 45col%
	groupId ma-frma

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout
\begin_inset CommandInset label
LatexCommand label
name "fig:ma-bx-rma-frma"

\end_inset

RMA vs.
 fRMA for biopsy samples.
\end_layout

\end_inset


\end_layout

\end_inset


\begin_inset space \hfill{}
\end_inset


\begin_inset Float figure
wide false
sideways false
status collapsed

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename graphics/frma-pax-bx/MA-BX-fRMA.fRMA-RASTER.png
	lyxscale 10
	width 45col%
	groupId ma-frma

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout
\begin_inset CommandInset label
LatexCommand label
name "fig:ma-bx-frma-frma"

\end_inset

fRMA vs fRMA for biopsy samples.
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Plain Layout
\align center
\begin_inset Float figure
wide false
sideways false
status collapsed

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename graphics/frma-pax-bx/MA-PAX-RMA.fRMA-RASTER.png
	lyxscale 10
	width 45col%
	groupId ma-frma

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout
\begin_inset CommandInset label
LatexCommand label
name "fig:MA-PAX-rma-frma"

\end_inset

RMA vs.
 fRMA for blood samples.
\end_layout

\end_inset


\end_layout

\end_inset


\begin_inset space \hfill{}
\end_inset


\begin_inset Float figure
wide false
sideways false
status collapsed

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename graphics/frma-pax-bx/MA-PAX-fRMA.fRMA-RASTER.png
	lyxscale 10
	width 45col%
	groupId ma-frma

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout
\begin_inset CommandInset label
LatexCommand label
name "fig:MA-PAX-frma-frma"

\end_inset

fRMA vs fRMA for blood samples.
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout
\begin_inset Argument 1
status collapsed

\begin_layout Plain Layout
Representative MA plots comparing RMA and custom fRMA normalizations.
\end_layout

\end_inset


\begin_inset CommandInset label
LatexCommand label
name "fig:Representative-MA-plots"

\end_inset


\series bold
Representative MA plots comparing RMA and custom fRMA normalizations.
 
\series default
For each plot, 20 samples were normalized using 2 different normalizations,
 and then averages (A) and log ratios (M) were plotted between the two different
 normalizations for every probe.
 For the 
\begin_inset Quotes eld
\end_inset

fRMA vs fRMA
\begin_inset Quotes erd
\end_inset

 plots (b & d), two different fRMA normalizations using vectors from two
 independent batch samplings were compared.
 Density of points is represented by blue shading, and individual outlier
 points are plotted.
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Subsection
SVA, voom, and array weights improve model fit for methylation array data
\end_layout

\begin_layout Standard
Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:meanvar-basic"
plural "false"
caps "false"
noprefix "false"

\end_inset

 shows the relationship between the mean 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
M-value
\end_layout

\end_inset

 and the standard deviation calculated for each probe in the methylation
 array data set.
 A few features of the data are apparent.
 First, the data are very strongly bimodal, with peaks in the density around
 
\begin_inset Flex Glossary Term (pl)
status open

\begin_layout Plain Layout
M-value
\end_layout

\end_inset

 of +4 and -4.
 These modes correspond to methylation sites that are nearly 100% methylated
 and nearly 100% unmethylated, respectively.
 The strong bimodality indicates that a majority of probes interrogate sites
 that fall into one of these two categories.
 The points in between these modes represent sites that are either partially
 methylated in many samples, or are fully methylated in some samples and
 fully unmethylated in other samples, or some combination.
 The next visible feature of the data is the W-shaped variance trend.
 The upticks in the variance trend on either side are expected, based on
 the sigmoid transformation exaggerating small differences at extreme 
\begin_inset Flex Glossary Term (pl)
status open

\begin_layout Plain Layout
M-value
\end_layout

\end_inset

 (Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:Sigmoid-beta-m-mapping"
plural "false"
caps "false"
noprefix "false"

\end_inset

).
 However, the uptick in the center is interesting: it indicates that sites
 that are not constitutively methylated or unmethylated have a higher variance.
 This could be a genuine biological effect, or it could be spurious noise
 that is only observable at sites with varying methylation.
\end_layout

\begin_layout Standard
\begin_inset ERT
status open

\begin_layout Plain Layout


\backslash
afterpage{
\end_layout

\begin_layout Plain Layout


\backslash
begin{landscape}
\end_layout

\end_inset


\end_layout

\begin_layout Standard
\begin_inset Float figure
wide false
sideways false
status open

\begin_layout Plain Layout
\begin_inset Flex TODO Note (inline)
status open

\begin_layout Plain Layout
Fix axis labels: 
\begin_inset Quotes eld
\end_inset

log2 M-value
\begin_inset Quotes erd
\end_inset

 is redundant because M-values are already log scale
\end_layout

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Float figure
wide false
sideways false
status collapsed

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename graphics/methylvoom/unadj.dupcor/meanvar-trends-PAGE1-CROP-RASTER.png
	lyxscale 15
	width 30col%
	groupId voomaw-subfig

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout
\begin_inset CommandInset label
LatexCommand label
name "fig:meanvar-basic"

\end_inset

Mean-variance trend for analysis A.
\end_layout

\end_inset


\end_layout

\end_inset


\begin_inset space \hfill{}
\end_inset


\begin_inset Float figure
wide false
sideways false
status collapsed

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename graphics/methylvoom/unadj.dupcor.sva.aw/meanvar-trends-PAGE1-CROP-RASTER.png
	lyxscale 15
	width 30col%
	groupId voomaw-subfig

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout
\begin_inset CommandInset label
LatexCommand label
name "fig:meanvar-sva-aw"

\end_inset

Mean-variance trend for analysis B.
\end_layout

\end_inset


\end_layout

\end_inset


\begin_inset space \hfill{}
\end_inset


\begin_inset Float figure
wide false
sideways false
status collapsed

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename graphics/methylvoom/unadj.dupcor.sva.voomaw/meanvar-trends-PAGE2-CROP-RASTER.png
	lyxscale 15
	width 30col%
	groupId voomaw-subfig

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout
\begin_inset CommandInset label
LatexCommand label
name "fig:meanvar-sva-voomaw"

\end_inset

Mean-variance trend after voom modeling in analysis C.
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout
\begin_inset Argument 1
status collapsed

\begin_layout Plain Layout
Mean-variance trend modeling in methylation array data.
\end_layout

\end_inset


\begin_inset CommandInset label
LatexCommand label
name "fig:-Meanvar-trend-methyl"

\end_inset


\series bold
 Mean-variance trend modeling in methylation array data.
 
\series default
The estimated 
\begin_inset Formula $\log_{2}$
\end_inset

(standard deviation) for each probe is plotted against the probe's average
 M-value across all samples as a black point, with some transparency to
 make over-plotting more visible, since there are about 450,000 points.
 Density of points is also indicated by the dark blue contour lines.
 The prior variance trend estimated by eBayes is shown in light blue, while
 the lowess trend of the points is shown in red.
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Standard
\begin_inset ERT
status open

\begin_layout Plain Layout


\backslash
end{landscape}
\end_layout

\begin_layout Plain Layout

}
\end_layout

\end_inset


\end_layout

\begin_layout Standard
In Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:meanvar-sva-aw"
plural "false"
caps "false"
noprefix "false"

\end_inset

, we see the mean-variance trend for the same methylation array data, this
 time with surrogate variables and sample quality weights estimated from
 the data and included in the model.
 As expected, the overall average variance is smaller, since the surrogate
 variables account for some of the variance.
 In addition, the uptick in variance in the middle of the 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
M-value
\end_layout

\end_inset

 range has disappeared, turning the W shape into a wide U shape.
 This indicates that the excess variance in the probes with intermediate
 
\begin_inset Flex Glossary Term (pl)
status open

\begin_layout Plain Layout
M-value
\end_layout

\end_inset

 was explained by systematic variations not correlated with known covariates,
 and these variations were modeled by the surrogate variables.
 The result is a nearly flat variance trend for the entire intermediate
 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
M-value
\end_layout

\end_inset

 range from about -3 to +3.
 Note that this corresponds closely to the range within which the 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
M-value
\end_layout

\end_inset

 transformation shown in Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:Sigmoid-beta-m-mapping"
plural "false"
caps "false"
noprefix "false"

\end_inset

 is nearly linear.
 In contrast, the excess variance at the extremes (greater than +3 and less
 than -3) was not 
\begin_inset Quotes eld
\end_inset

absorbed
\begin_inset Quotes erd
\end_inset

 by the surrogate variables and remains in the plot, indicating that this
 variation has no systematic component: probes with extreme 
\begin_inset Flex Glossary Term (pl)
status open

\begin_layout Plain Layout
M-value
\end_layout

\end_inset

 are uniformly more variable across all samples, as expected.
 
\end_layout

\begin_layout Standard
Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:meanvar-sva-voomaw"
plural "false"
caps "false"
noprefix "false"

\end_inset

 shows the mean-variance trend after fitting the model with the observation
 weights assigned by voom based on the mean-variance trend shown in Figure
 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:meanvar-sva-aw"
plural "false"
caps "false"
noprefix "false"

\end_inset

.
 As expected, the weights exactly counteract the trend in the data, resulting
 in a nearly flat trend centered vertically at 1 (i.e.
 0 on the log scale).
 This shows that the observations with extreme 
\begin_inset Flex Glossary Term (pl)
status open

\begin_layout Plain Layout
M-value
\end_layout

\end_inset

 have been appropriately down-weighted to account for the fact that the
 noise in those observations has been amplified by the non-linear 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
M-value
\end_layout

\end_inset

 transformation.
 In turn, this gives relatively more weight to observations in the middle
 region, which are more likely to correspond to probes measuring interesting
 biology (not constitutively methylated or unmethylated).
\end_layout

\begin_layout Standard
To determine whether any of the known experimental factors had an impact
 on data quality, the sample quality weights estimated from the data were
 tested for association with each of the experimental factors (Table 
\begin_inset CommandInset ref
LatexCommand ref
reference "tab:weight-covariate-tests"
plural "false"
caps "false"
noprefix "false"

\end_inset

).
 Diabetes diagnosis was found to have a potentially significant association
 with the sample weights, with a t-test p-value of 
\begin_inset Formula $1.06\times10^{-3}$
\end_inset

.
 Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:diabetes-sample-weights"
plural "false"
caps "false"
noprefix "false"

\end_inset

 shows the distribution of sample weights grouped by diabetes diagnosis.
 The samples from patients with 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
T2D
\end_layout

\end_inset

 were assigned significantly lower weights than those from patients with
 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
T1D
\end_layout

\end_inset

.
 This indicates that the 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
T2D
\end_layout

\end_inset

 samples had an overall higher variance on average across all probes.
 
\end_layout

\begin_layout Standard
\begin_inset Float table
wide false
sideways false
status collapsed

\begin_layout Plain Layout
\align center
\begin_inset Tabular
<lyxtabular version="3" rows="5" columns="3">
<features tabularvalignment="middle">
<column alignment="center" valignment="top">
<column alignment="center" valignment="top">
<column alignment="center" valignment="top">
<row>
<cell alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
Covariate
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
Test used
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" rightline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
p-value
\end_layout

\end_inset
</cell>
</row>
<row>
<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
Transplant Status
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
F-test
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" leftline="true" rightline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
0.404
\end_layout

\end_inset
</cell>
</row>
<row>
<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
Diabetes Diagnosis
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout

\emph on
t
\emph default
-test
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" leftline="true" rightline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
0.00106
\end_layout

\end_inset
</cell>
</row>
<row>
<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
Sex
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout

\emph on
t
\emph default
-test
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" leftline="true" rightline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
0.148
\end_layout

\end_inset
</cell>
</row>
<row>
<cell alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
Age
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
linear regression
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" rightline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
0.212
\end_layout

\end_inset
</cell>
</row>
</lyxtabular>

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout
\begin_inset Argument 1
status collapsed

\begin_layout Plain Layout
Association of sample weights with clinical covariates in methylation array
 data.
\end_layout

\end_inset


\begin_inset CommandInset label
LatexCommand label
name "tab:weight-covariate-tests"

\end_inset


\series bold
Association of sample weights with clinical covariates in methylation array
 data.
 
\series default
Computed sample quality log weights were tested for significant association
 with each of the variables in the model (1st column).
 An appropriate test was selected for each variable based on whether the
 variable had 2 categories (
\emph on
t
\emph default
-test), had more than 2 categories (F-test), or was numeric (linear regression).
 The test selected is shown in the 2nd column.
 P-values for association with the log weights are shown in the 3rd column.
 No multiple testing adjustment was performed for these p-values.
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Standard
\begin_inset Float figure
wide false
sideways false
status collapsed

\begin_layout Plain Layout
\begin_inset Flex TODO Note (inline)
status open

\begin_layout Plain Layout
Redo the sample weight boxplot with notches, and remove fill colors
\end_layout

\end_inset


\end_layout

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename graphics/methylvoom/unadj.dupcor.sva.voomaw/sample-weights-PAGE3-CROP.pdf
	lyxscale 50
	width 60col%
	groupId colwidth

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout
\begin_inset Argument 1
status collapsed

\begin_layout Plain Layout
Box-and-whiskers plot of sample quality weights grouped by diabetes diagnosis.
\end_layout

\end_inset


\begin_inset CommandInset label
LatexCommand label
name "fig:diabetes-sample-weights"

\end_inset


\series bold
Box-and-whiskers plot of sample quality weights grouped by diabetes diagnosis.
 
\series default
Samples were grouped based on diabetes diagnosis, and the distribution of
 sample quality weights for each diagnosis was plotted as a box-and-whiskers
 plot 
\begin_inset CommandInset citation
LatexCommand cite
key "McGill1978"
literal "false"

\end_inset

.
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Standard
Table 
\begin_inset CommandInset ref
LatexCommand ref
reference "tab:methyl-num-signif"
plural "false"
caps "false"
noprefix "false"

\end_inset

 shows the number of significantly differentially methylated probes reported
 by each analysis for each comparison of interest at an 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
FDR
\end_layout

\end_inset

 of 10%.
 As expected, the more elaborate analyses, B and C, report more significant
 probes than the more basic analysis A, consistent with the conclusions
 above that the data contain hidden systematic variations that must be modeled.
 Table 
\begin_inset CommandInset ref
LatexCommand ref
reference "tab:methyl-est-nonnull"
plural "false"
caps "false"
noprefix "false"

\end_inset

 shows the estimated number differentially methylated probes for each test
 from each analysis.
 This was computed by estimating the proportion of null hypotheses that
 were true using the method of 
\begin_inset CommandInset citation
LatexCommand cite
key "Phipson2013Thesis"
literal "false"

\end_inset

 and subtracting that fraction from the total number of probes, yielding
 an estimate of the number of null hypotheses that are false based on the
 distribution of p-values across the entire dataset.
 Note that this does not identify which null hypotheses should be rejected
 (i.e.
 which probes are significant); it only estimates the true number of such
 probes.
 Once again, analyses B and C result it much larger estimates for the number
 of differentially methylated probes.
 In this case, analysis C, the only analysis that includes voom, estimates
 the largest number of differentially methylated probes for all 3 contrasts.
 If the assumptions of all the methods employed hold, then this represents
 a gain in statistical power over the simpler analysis A.
 Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:meth-p-value-histograms"
plural "false"
caps "false"
noprefix "false"

\end_inset

 shows the p-value distributions for each test, from which the numbers in
 Table 
\begin_inset CommandInset ref
LatexCommand ref
reference "tab:methyl-est-nonnull"
plural "false"
caps "false"
noprefix "false"

\end_inset

 were generated.
 The distributions for analysis A all have a dip in density near zero, which
 is a strong sign of a poor model fit.
 The histograms for analyses B and C are more well-behaved, with a uniform
 component stretching all the way from 0 to 1 representing the probes for
 which the null hypotheses is true (no differential methylation), and a
 zero-biased component representing the probes for which the null hypothesis
 is false (differentially methylated).
 These histograms do not indicate any major issues with the model fit.
\end_layout

\begin_layout Standard
\begin_inset Float table
wide false
sideways false
status collapsed

\begin_layout Plain Layout
\begin_inset Float table
wide false
sideways false
status open

\begin_layout Plain Layout
\align center
\begin_inset Tabular
<lyxtabular version="3" rows="5" columns="4">
<features tabularvalignment="middle">
<column alignment="center" valignment="top">
<column alignment="center" valignment="top">
<column alignment="center" valignment="top">
<column alignment="center" valignment="top">
<row>
<cell alignment="center" valignment="top" usebox="none">
\begin_inset Text

\begin_layout Plain Layout

\end_layout

\end_inset
</cell>
<cell multicolumn="1" alignment="center" valignment="top" topline="true" leftline="true" rightline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
Analysis
\end_layout

\end_inset
</cell>
<cell multicolumn="2" alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout

\end_layout

\end_inset
</cell>
<cell multicolumn="2" alignment="center" valignment="top" topline="true" leftline="true" rightline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout

\end_layout

\end_inset
</cell>
</row>
<row>
<cell alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
Contrast
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
A
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
B
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" rightline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
C
\end_layout

\end_inset
</cell>
</row>
<row>
<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
TX vs AR
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
0
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
25
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" leftline="true" rightline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
22
\end_layout

\end_inset
</cell>
</row>
<row>
<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
TX vs ADNR
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
7
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
338
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" leftline="true" rightline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
369
\end_layout

\end_inset
</cell>
</row>
<row>
<cell alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
TX vs CAN
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
0
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
231
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" rightline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
278
\end_layout

\end_inset
</cell>
</row>
</lyxtabular>

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout
\begin_inset CommandInset label
LatexCommand label
name "tab:methyl-num-signif"

\end_inset

Number of probes significant at 10% FDR.
\end_layout

\end_inset


\end_layout

\end_inset


\begin_inset space \hfill{}
\end_inset


\begin_inset Float table
wide false
sideways false
status open

\begin_layout Plain Layout
\align center
\begin_inset Tabular
<lyxtabular version="3" rows="5" columns="4">
<features tabularvalignment="middle">
<column alignment="center" valignment="top">
<column alignment="center" valignment="top">
<column alignment="center" valignment="top">
<column alignment="center" valignment="top">
<row>
<cell alignment="center" valignment="top" usebox="none">
\begin_inset Text

\begin_layout Plain Layout

\end_layout

\end_inset
</cell>
<cell multicolumn="1" alignment="center" valignment="top" topline="true" leftline="true" rightline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
Analysis
\end_layout

\end_inset
</cell>
<cell multicolumn="2" alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout

\end_layout

\end_inset
</cell>
<cell multicolumn="2" alignment="center" valignment="top" topline="true" leftline="true" rightline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout

\end_layout

\end_inset
</cell>
</row>
<row>
<cell alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
Contrast
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
A
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
B
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" rightline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
C
\end_layout

\end_inset
</cell>
</row>
<row>
<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
TX vs AR
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
0
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
10,063
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" leftline="true" rightline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
11,225
\end_layout

\end_inset
</cell>
</row>
<row>
<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
TX vs ADNR
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
27
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
12,674
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" leftline="true" rightline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
13,086
\end_layout

\end_inset
</cell>
</row>
<row>
<cell alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
TX vs CAN
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
966
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
20,039
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" rightline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
20,955
\end_layout

\end_inset
</cell>
</row>
</lyxtabular>

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout
\begin_inset CommandInset label
LatexCommand label
name "tab:methyl-est-nonnull"

\end_inset

Estimated number of non-null tests, using the method of averaging local
 FDR values 
\begin_inset CommandInset citation
LatexCommand cite
key "Phipson2013Thesis"
literal "false"

\end_inset

.
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout
\begin_inset Argument 1
status collapsed

\begin_layout Plain Layout
Estimates of degree of differential methylation in for each contrast in
 each analysis.
\end_layout

\end_inset


\series bold
Estimates of degree of differential methylation in for each contrast in
 each analysis.
 
\series default
For each of the analyses in Table 
\begin_inset CommandInset ref
LatexCommand ref
reference "tab:Summary-of-meth-analysis"
plural "false"
caps "false"
noprefix "false"

\end_inset

, these tables show the number of probes called significantly differentially
 methylated at a threshold of 10% FDR for each comparison between TX and
 the other 3 transplant statuses (a) and the estimated total number of probes
 that are differentially methylated (b).
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Standard
\begin_inset Float figure
wide false
sideways false
status collapsed

\begin_layout Plain Layout
\align center

\series bold
\begin_inset Float figure
wide false
sideways false
status collapsed

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename graphics/methylvoom/unadj.dupcor/pval-histograms-PAGE1.pdf
	lyxscale 33
	width 30col%
	groupId meth-pval-hist

\end_inset


\end_layout

\begin_layout Plain Layout

\series bold
\begin_inset Caption Standard

\begin_layout Plain Layout
AR vs.
 TX, Analysis A
\end_layout

\end_inset


\end_layout

\end_inset


\begin_inset space \hfill{}
\end_inset


\begin_inset Float figure
wide false
sideways false
status collapsed

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename graphics/methylvoom/unadj.dupcor/pval-histograms-PAGE2.pdf
	lyxscale 33
	width 30col%
	groupId meth-pval-hist

\end_inset


\end_layout

\begin_layout Plain Layout

\series bold
\begin_inset Caption Standard

\begin_layout Plain Layout
ADNR vs.
 TX, Analysis A
\end_layout

\end_inset


\end_layout

\end_inset


\begin_inset space \hfill{}
\end_inset


\begin_inset Float figure
wide false
sideways false
status collapsed

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename graphics/methylvoom/unadj.dupcor/pval-histograms-PAGE3.pdf
	lyxscale 33
	width 30col%
	groupId meth-pval-hist

\end_inset


\end_layout

\begin_layout Plain Layout

\series bold
\begin_inset Caption Standard

\begin_layout Plain Layout
CAN vs.
 TX, Analysis A
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Plain Layout
\align center

\series bold
\begin_inset Float figure
wide false
sideways false
status collapsed

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename graphics/methylvoom/unadj.dupcor.sva.aw/pval-histograms-PAGE1.pdf
	lyxscale 33
	width 30col%
	groupId meth-pval-hist

\end_inset


\end_layout

\begin_layout Plain Layout

\series bold
\begin_inset Caption Standard

\begin_layout Plain Layout
AR vs.
 TX, Analysis B
\end_layout

\end_inset


\end_layout

\end_inset


\begin_inset space \hfill{}
\end_inset


\begin_inset Float figure
wide false
sideways false
status collapsed

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename graphics/methylvoom/unadj.dupcor.sva.aw/pval-histograms-PAGE2.pdf
	lyxscale 33
	width 30col%
	groupId meth-pval-hist

\end_inset


\end_layout

\begin_layout Plain Layout

\series bold
\begin_inset Caption Standard

\begin_layout Plain Layout
ADNR vs.
 TX, Analysis B
\end_layout

\end_inset


\end_layout

\end_inset


\begin_inset space \hfill{}
\end_inset


\begin_inset Float figure
wide false
sideways false
status collapsed

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename graphics/methylvoom/unadj.dupcor.sva.aw/pval-histograms-PAGE3.pdf
	lyxscale 33
	width 30col%
	groupId meth-pval-hist

\end_inset


\end_layout

\begin_layout Plain Layout

\series bold
\begin_inset Caption Standard

\begin_layout Plain Layout
CAN vs.
 TX, Analysis B
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Plain Layout
\align center

\series bold
\begin_inset Float figure
wide false
sideways false
status collapsed

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename graphics/methylvoom/unadj.dupcor.sva.voomaw/pval-histograms-PAGE1.pdf
	lyxscale 33
	width 30col%
	groupId meth-pval-hist

\end_inset


\end_layout

\begin_layout Plain Layout

\series bold
\begin_inset Caption Standard

\begin_layout Plain Layout
AR vs.
 TX, Analysis C
\end_layout

\end_inset


\end_layout

\end_inset


\begin_inset space \hfill{}
\end_inset


\begin_inset Float figure
wide false
sideways false
status collapsed

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename graphics/methylvoom/unadj.dupcor.sva.voomaw/pval-histograms-PAGE2.pdf
	lyxscale 33
	width 30col%
	groupId meth-pval-hist

\end_inset


\end_layout

\begin_layout Plain Layout

\series bold
\begin_inset Caption Standard

\begin_layout Plain Layout
ADNR vs.
 TX, Analysis C
\end_layout

\end_inset


\end_layout

\end_inset


\begin_inset space \hfill{}
\end_inset


\begin_inset Float figure
wide false
sideways false
status collapsed

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename graphics/methylvoom/unadj.dupcor.sva.voomaw/pval-histograms-PAGE3.pdf
	lyxscale 33
	width 30col%
	groupId meth-pval-hist

\end_inset


\end_layout

\begin_layout Plain Layout

\series bold
\begin_inset Caption Standard

\begin_layout Plain Layout
CAN vs.
 TX, Analysis C
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout
\begin_inset Argument 1
status collapsed

\begin_layout Plain Layout
Probe p-value histograms for each contrast in each analysis.
\end_layout

\end_inset


\begin_inset CommandInset label
LatexCommand label
name "fig:meth-p-value-histograms"

\end_inset


\series bold
Probe p-value histograms for each contrast in each analysis.
 
\series default
For each differential methylation test of interest, the distribution of
 p-values across all probes is plotted as a histogram.
 The red solid line indicates the density that would be expected under the
 null hypothesis for all probes (a 
\begin_inset Formula $\mathrm{Uniform}(0,1)$
\end_inset

 distribution), while the blue dotted line indicates the fraction of p-values
 that actually follow the null hypothesis (
\begin_inset Formula $\hat{\pi}_{0}$
\end_inset

) estimated using the method of averaging local FDR values 
\begin_inset CommandInset citation
LatexCommand cite
key "Phipson2013Thesis"
literal "false"

\end_inset

.
 A blue line is only shown in each plot if the estimate of 
\begin_inset Formula $\hat{\pi}_{0}$
\end_inset

 for that p-value distribution is smaller than 1.
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Standard
\begin_inset Flex TODO Note (inline)
status open

\begin_layout Plain Layout
If time allows, maybe generate the PCA plots before/after SVA effect subtraction
?
\end_layout

\end_inset


\end_layout

\begin_layout Section
Discussion
\end_layout

\begin_layout Subsection
fRMA achieves clinically applicable normalization without sacrificing classifica
tion performance
\end_layout

\begin_layout Standard
As shown in Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:Classifier-probabilities-RMA"
plural "false"
caps "false"
noprefix "false"

\end_inset

, improper normalization, particularly separate normalization of training
 and test samples, leads to unwanted biases in classification.
 In a controlled experimental context, it is always possible to correct
 this issue by normalizing all experimental samples together.
 However, because it is not feasible to normalize all samples together in
 a clinical context, a single-channel normalization is required.
 
\end_layout

\begin_layout Standard
The major concern in using a single-channel normalization is that non-single-cha
nnel methods can share information between arrays to improve the normalization,
 and single-channel methods risk sacrificing the gains in normalization
 accuracy that come from this information sharing.
 In the case of 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
RMA
\end_layout

\end_inset

, this information sharing is accomplished through quantile normalization
 and median polish steps.
 The need for information sharing in quantile normalization can easily be
 removed by learning a fixed set of quantiles from external data and normalizing
 each array to these fixed quantiles, instead of the quantiles of the data
 itself.
 As long as the fixed quantiles are reasonable, the result will be similar
 to standard 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
RMA
\end_layout

\end_inset

.
 However, there is no analogous way to eliminate cross-array information
 sharing in the median polish step, so 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
fRMA
\end_layout

\end_inset

 replaces this with a weighted average of probes on each array, with the
 weights learned from external data.
 This step of 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
fRMA
\end_layout

\end_inset

 has the greatest potential to diverge from RMA in undesirable ways.
\end_layout

\begin_layout Standard
However, when run on real data, 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
fRMA
\end_layout

\end_inset

 performed at least as well as 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
RMA
\end_layout

\end_inset

 in both the internal validation and external validation tests.
 This shows that 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
fRMA
\end_layout

\end_inset

 can be used to normalize individual clinical samples in a class prediction
 context without sacrificing the classifier performance that would be obtained
 by using the more well-established 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
RMA
\end_layout

\end_inset

 for normalization.
 The other single-channel normalization method considered, 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
SCAN
\end_layout

\end_inset

, showed some loss of 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
AUC
\end_layout

\end_inset

 in the external validation test.
 Based on these results, 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
fRMA
\end_layout

\end_inset

 is the preferred normalization for clinical samples in a class prediction
 context.
\end_layout

\begin_layout Subsection
Robust fRMA vectors can be generated for new array platforms
\end_layout

\begin_layout Standard
The published 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
fRMA
\end_layout

\end_inset

 normalization vectors for the hgu133plus2 platform were generated from
 a set of 850 samples chosen from a wide range of tissues, which the authors
 determined was sufficient to generate a robust set of normalization vectors
 that could be applied across all tissues 
\begin_inset CommandInset citation
LatexCommand cite
key "McCall2010"
literal "false"

\end_inset

.
 Since we only had hthgu133pluspm for 2 tissues of interest, our needs were
 more modest.
 Even using only 130 samples in 26 batches of 5 samples each for kidney
 biopsies, we were able to train a robust set of 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
fRMA
\end_layout

\end_inset

 normalization vectors that were not meaningfully affected by the random
 selection of 5 samples from each batch.
 As expected, the training process was just as robust for the blood samples
 with 230 samples in 46 batches of 5 samples each.
 Because these vectors were each generated using training samples from a
 single tissue, they are not suitable for general use, unlike the vectors
 provided with 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
fRMA
\end_layout

\end_inset

 itself.
 They are purpose-built for normalizing a specific type of sample on a specific
 platform.
 This is a mostly acceptable limitation in the context of developing a machine
 learning classifier for diagnosing a disease from samples of a specific
 tissue.
\end_layout

\begin_layout Subsection
Methylation array data can be successfully analyzed using existing techniques,
 but machine learning poses additional challenges
\end_layout

\begin_layout Standard
Both analysis strategies B and C both yield a reasonable analysis, with
 a mean-variance trend that matches the expected behavior for the non-linear
 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
M-value
\end_layout

\end_inset

 transformation (Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:meanvar-sva-aw"
plural "false"
caps "false"
noprefix "false"

\end_inset

) and well-behaved p-value distributions (Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:meth-p-value-histograms"
plural "false"
caps "false"
noprefix "false"

\end_inset

).
 These two analyses also yield similar numbers of significant probes (Table
 
\begin_inset CommandInset ref
LatexCommand ref
reference "tab:methyl-num-signif"
plural "false"
caps "false"
noprefix "false"

\end_inset

) and similar estimates of the number of differentially methylated probes
 (Table 
\begin_inset CommandInset ref
LatexCommand ref
reference "tab:methyl-est-nonnull"
plural "false"
caps "false"
noprefix "false"

\end_inset

).
 The main difference between these two analyses is the method used to account
 for the mean-variance trend.
 In analysis B, the trend is estimated and applied at the probe level: each
 probe's estimated variance is squeezed toward the trend using an empirical
 Bayes procedure (Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:meanvar-sva-aw"
plural "false"
caps "false"
noprefix "false"

\end_inset

).
 In analysis C, the trend is still estimated at the probe level, but instead
 of estimating a single variance value shared across all observations for
 a given probe, the voom method computes an initial estimate of the variance
 for each observation individually based on where its model-fitted 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
M-value
\end_layout

\end_inset

 falls on the trend line and then assigns inverse-variance weights to model
 the difference in variance between observations.
 An overall variance is still estimated for each probe using the same empirical
 Bayes method, but now the residual trend is flat (Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:meanvar-sva-voomaw"
plural "false"
caps "false"
noprefix "false"

\end_inset

), indicating that the mean-variance trend is adequately modeled by scaling
 the estimated variance for each observation using the weights computed
 by voom.
 
\end_layout

\begin_layout Standard
The difference between the standard empirical Bayes trended variance modeling
 (analysis B) and voom (analysis C) is analogous to the difference between
 a t-test with equal variance and a t-test with unequal variance, except
 that the unequal group variances used in the latter test are estimated
 based on the mean-variance trend from all the probes rather than the data
 for the specific probe being tested, thus stabilizing the group variance
 estimates by sharing information between probes.
 Allowing voom to model the variance using observation weights in this manner
 allows the linear model fit to concentrate statistical power where it will
 do the most good.
 For example, if a particular probe's 
\begin_inset Flex Glossary Term (pl)
status open

\begin_layout Plain Layout
M-value
\end_layout

\end_inset

 are always at the extreme of the 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
M-value
\end_layout

\end_inset

 range (e.g.
 less than -4) for 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
ADNR
\end_layout

\end_inset

 samples, but the 
\begin_inset Flex Glossary Term (pl)
status open

\begin_layout Plain Layout
M-value
\end_layout

\end_inset

 for that probe in 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
TX
\end_layout

\end_inset

 and 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
CAN
\end_layout

\end_inset

 samples are within the flat region of the mean-variance trend (between
 
\begin_inset Formula $-3$
\end_inset

 and 
\begin_inset Formula $+3$
\end_inset

), voom is able to down-weight the contribution of the high-variance 
\begin_inset Flex Glossary Term (pl)
status open

\begin_layout Plain Layout
M-value
\end_layout

\end_inset

 from the 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
ADNR
\end_layout

\end_inset

 samples in order to gain more statistical power while testing for differential
 methylation between 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
TX
\end_layout

\end_inset

 and 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
CAN
\end_layout

\end_inset

.
 In contrast, modeling the mean-variance trend only at the probe level would
 combine the high-variance 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
ADNR
\end_layout

\end_inset

 samples and lower-variance samples from other conditions and estimate an
 intermediate variance for this probe.
 In practice, analysis B shows that this approach is adequate, but the voom
 approach in analysis C performs at least as well on all model fit criteria
 and yields a larger estimate for the number of differentially methylated
 genes, 
\emph on
and
\emph default
 it matches up slightly better with the theoretical properties of the data.
\end_layout

\begin_layout Standard
The significant association of diabetes diagnosis with sample quality is
 interesting.
 The samples with 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
T2D
\end_layout

\end_inset

 tended to have more variation, averaged across all probes, than those with
 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
T1D
\end_layout

\end_inset

.
 This is consistent with the consensus that 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
T2D
\end_layout

\end_inset

 and the associated metabolic syndrome represent a broad dysregulation of
 the body's endocrine signaling related to metabolism 
\begin_inset CommandInset citation
LatexCommand cite
key "Volkmar2012,Hall2018,Yokoi2018"
literal "false"

\end_inset

.
 This dysregulation could easily manifest as a greater degree of variation
 in the DNA methylation patterns of affected tissues.
 In contrast, 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
T1D
\end_layout

\end_inset

 has a more specific cause and effect, so a less variable methylation signature
 is expected.
\end_layout

\begin_layout Standard
This preliminary analysis suggests that some degree of differential methylation
 exists between 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
TX
\end_layout

\end_inset

 and each of the three types of transplant disfunction studied.
 Hence, it may be feasible to train a classifier to diagnose transplant
 disfunction from DNA methylation array data.
 However, the major importance of both 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
SVA
\end_layout

\end_inset

 and sample quality weighting for proper modeling of this data poses significant
 challenges for any attempt at a machine learning on data of similar quality.
 While these are easily used in a modeling context with full sample information,
 neither of these methods is directly applicable in a machine learning context,
 where the diagnosis is not known ahead of time.
 If a machine learning approach for methylation-based diagnosis is to be
 pursued, it will either require machine-learning-friendly methods to address
 the same systematic trends in the data that 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
SVA
\end_layout

\end_inset

 and sample quality weighting address, or it will require higher quality
 data with substantially less systematic perturbation of the data.
\end_layout

\begin_layout Section
Future Directions
\end_layout

\begin_layout Subsection
Improving fRMA to allow training from batches of unequal size
\end_layout

\begin_layout Standard
Because the tools for building 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
fRMA
\end_layout

\end_inset

 normalization vectors require equal-size batches, many samples must be
 discarded from the training data.
 This is undesirable for a few reasons.
 First, more data is simply better, all other things being equal.
 In this case, 
\begin_inset Quotes eld
\end_inset

better
\begin_inset Quotes erd
\end_inset

 means a more precise estimate of normalization parameters.
 In addition, the samples to be discarded must be chosen arbitrarily, which
 introduces an unnecessary element of randomness into the estimation process.
 While the randomness can be made deterministic by setting a consistent
 random seed, the need for equal size batches also introduces a need for
 the analyst to decide on the appropriate trade-off between batch size and
 the number of batches.
 This introduces an unnecessary and undesirable 
\begin_inset Quotes eld
\end_inset

researcher degree of freedom
\begin_inset Quotes erd
\end_inset

 into the analysis, since the generated normalization vectors now depend
 on the choice of batch size based on vague selection criteria and instinct,
 which can unintentionally introduce bias if the researcher chooses a batch
 size based on what seems to yield the most favorable downstream results
 
\begin_inset CommandInset citation
LatexCommand cite
key "Simmons2011"
literal "false"

\end_inset

.
\end_layout

\begin_layout Standard
Fortunately, the requirement for equal-size batches is not inherent to the
 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
fRMA
\end_layout

\end_inset

 algorithm but rather a limitation of the implementation in the 
\begin_inset Flex Code
status open

\begin_layout Plain Layout
frmaTools
\end_layout

\end_inset

 package.
 In personal communication, the package's author, Matthew McCall, has indicated
 that with some work, it should be possible to improve the implementation
 to work with batches of unequal sizes.
 The current implementation ignores the batch size when calculating with-batch
 and between-batch residual variances, since the batch size constant cancels
 out later in the calculations as long as all batches are of equal size.
 Hence, the calculations of these parameters would need to be modified to
 remove this optimization and properly calculate the variances using the
 full formula.
 Once this modification is made, a new strategy would need to be developed
 for assessing the stability of parameter estimates, since the random sub-sampli
ng step is eliminated, meaning that different sub-samplings can no longer
 be compared as in Figures 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:frma-violin"
plural "false"
caps "false"
noprefix "false"

\end_inset

 and 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:Representative-MA-plots"
plural "false"
caps "false"
noprefix "false"

\end_inset

.
 Bootstrap resampling is likely a good candidate here: sample many training
 sets of equal size from the existing training set with replacement, estimate
 parameters from each resampled training set, and compare the estimated
 parameters between bootstraps in order to quantify the variability in each
 parameter's estimation.
\end_layout

\begin_layout Subsection
Developing methylation arrays as a diagnostic tool for kidney transplant
 rejection
\end_layout

\begin_layout Standard
The current study has showed that DNA methylation, as assayed by Illumina
 450k methylation arrays, has some potential for diagnosing transplant dysfuncti
ons, including rejection.
 However, very few probes could be confidently identified as differentially
 methylated between healthy and dysfunctional transplants.
 One likely explanation for this is the predominant influence of unobserved
 confounding factors.
 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
SVA
\end_layout

\end_inset

 can model and correct for such factors, but the correction can never be
 perfect, so some degree of unwanted systematic variation will always remain
 after 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
SVA
\end_layout

\end_inset

 correction.
 If the effect size of the confounding factors was similar to that of the
 factor of interest (in this case, transplant status), this would be an
 acceptable limitation, since removing most of the confounding factors'
 effects would allow the main effect to stand out.
 However, in this data set, the confounding factors have a much larger effect
 size than transplant status, which means that the small degree of remaining
 variation not removed by 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
SVA
\end_layout

\end_inset

 can still swamp the effect of interest, making it difficult to detect.
 This is, of course, a major issue when the end goal is to develop a classifier
 to diagnose transplant rejection from methylation data, since batch-correction
 methods like 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
SVA
\end_layout

\end_inset

 that work in a linear modeling context cannot be applied in a machine learning
 context.
\end_layout

\begin_layout Standard
Currently, the source of these unwanted systematic variations in the data
 is unknown.
 The best solution would be to determine the cause of the variation and
 eliminate it, thereby eliminating the need to model and remove that variation.
 However, if this proves impractical, another option is to use 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
SVA
\end_layout

\end_inset

 to identify probes that are highly associated with the surrogate variables
 that describe the unwanted variation in the data.
 These probes could be discarded prior to classifier training, in order
 to maximize the chance that the training algorithm will be able to identify
 highly predictive probes from those remaining.
 Lastly, it is possible that some of this unwanted variation is a result
 of the array-based assay being used and would be eliminated by switching
 to assaying DNA methylation using bisulphite sequencing.
 However, this carries the risk that the sequencing assay will have its
 own set of biases that must be corrected for in a different way.
\end_layout

\begin_layout Chapter
\begin_inset CommandInset label
LatexCommand label
name "chap:Globin-blocking-cyno"

\end_inset

Globin-blocking for more effective blood RNA-seq analysis in primate animal
 model
\end_layout

\begin_layout Standard

\size large
Ryan C.
 Thompson, Terri Gelbart, Steven R.
 Head, Phillip Ordoukhanian, Courtney Mullen, Dongmei Han, Dora Berman,
 Amelia Bartholomew, Norma Kenyon, Daniel R.
 Salomon
\end_layout

\begin_layout Standard
\begin_inset ERT
status collapsed

\begin_layout Plain Layout


\backslash
glsresetall
\end_layout

\end_inset


\begin_inset Note Note
status collapsed

\begin_layout Plain Layout
Reintroduce all abbreviations
\end_layout

\end_inset


\end_layout

\begin_layout Standard
\begin_inset Flex TODO Note (inline)
status open

\begin_layout Plain Layout
Choose between above and the paper title: Optimizing yield of deep RNA sequencin
g for gene expression profiling by globin reduction of peripheral blood
 samples from cynomolgus monkeys (
\emph on
Macaca fascicularis
\emph default
).
\end_layout

\end_inset


\end_layout

\begin_layout Section*
Abstract
\end_layout

\begin_layout Paragraph
Background
\end_layout

\begin_layout Standard
Primate blood contains high concentrations of globin 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
mRNA
\end_layout

\end_inset

.
 Globin reduction is a standard technique used to improve the expression
 results obtained by DNA microarrays on RNA from blood samples.
 However, with 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
RNA-seq
\end_layout

\end_inset

 quickly replacing microarrays for many applications, the impact of globin
 reduction for 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
RNA-seq
\end_layout

\end_inset

 is less well-studied.
 Moreover, no off-the-shelf kits are available for globin reduction in nonhuman
 primates.
\end_layout

\begin_layout Paragraph
Results
\end_layout

\begin_layout Standard
Here we report a protocol for 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
RNA-seq
\end_layout

\end_inset

 in primate blood samples that uses complimentary 
\begin_inset Flex Glossary Term (pl)
status open

\begin_layout Plain Layout
oligo
\end_layout

\end_inset

 to block reverse transcription of the alpha and beta globin genes.
 In test samples from cynomolgus monkeys (
\emph on
Macaca fascicularis
\emph default
), this 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
GB
\end_layout

\end_inset

 protocol approximately doubles the yield of informative (non-globin) reads
 by greatly reducing the fraction of globin reads, while also improving
 the consistency in sequencing depth between samples.
 The increased yield enables detection of about 2000 more genes, significantly
 increases the correlation in measured gene expression levels between samples,
 and increases the sensitivity of differential gene expression tests.
\end_layout

\begin_layout Paragraph
Conclusions
\end_layout

\begin_layout Standard
These results show that 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
GB
\end_layout

\end_inset

 significantly improves the cost-effectiveness of 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
RNA-seq
\end_layout

\end_inset

 in primate blood samples by doubling the yield of useful reads, allowing
 detection of more genes, and improving the precision of gene expression
 measurements.
 Based on these results, a globin reducing or blocking protocol is recommended
 for all 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
RNA-seq
\end_layout

\end_inset

 studies of primate blood samples.
\end_layout

\begin_layout Standard
\begin_inset ERT
status collapsed

\begin_layout Plain Layout


\backslash
glsresetall
\end_layout

\end_inset


\end_layout

\begin_layout Section
Introduction
\end_layout

\begin_layout Standard
As part of a multi-lab PO1 grant to study 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
MSC
\end_layout

\end_inset

 infusion as a treatment for graft rejection in cynomolgus monkeys (
\emph on
Macaca fascicularis
\emph default
), a large number of serial blood draws from cynomolgus monkeys were planned
 in order to monitor the progress of graft healing and eventual rejection
 after transplantation.
 In order to streamline the process of performing 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
RNA-seq
\end_layout

\end_inset

 on these blood samples, we developed a custom sequencing protocol.
 In the developement of this protocol, we required a solution for the problem
 of excess globin reads.
 High fractions of globin 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
mRNA
\end_layout

\end_inset

 are naturally present in mammalian peripheral blood samples (up to 70%
 of total 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
mRNA
\end_layout

\end_inset

) and these are known to interfere with the results of array-based expression
 profiling 
\begin_inset CommandInset citation
LatexCommand cite
key "Winn2010"
literal "false"

\end_inset

.
 Globin reduction is also necessary for 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
RNA-seq
\end_layout

\end_inset

 of blood samples, though for unrelated reasons: without globin reduction,
 many 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
RNA-seq
\end_layout

\end_inset

 reads will be derived from the globin genes, leaving fewer for the remainder
 of the genes in the transcriptome.
 However, existing strategies for globin reduction require an additional
 step during sample preparation to deplete the population of globin transcripts
 from the sample prior to reverse transcription 
\begin_inset CommandInset citation
LatexCommand cite
key "Mastrokolias2012,Choi2014,Shin2014"
literal "false"

\end_inset

.
 Furthermore, off-the-shelf globin reduction kits are generally targeted
 at human or mouse globin, not cynomolgus monkey, and sequence identity
 between human and cyno globin genes cannot be automatically assumed.
 Hence, we sought to incorporate a custom globin reduction method into our
 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
RNA-seq
\end_layout

\end_inset

 protocol purely by adding additional reagents to an existing step in the
 sample preparation.
\end_layout

\begin_layout Section
Approach
\end_layout

\begin_layout Standard
\begin_inset Note Note
status collapsed

\begin_layout Plain Layout
Consider putting some of this in the Intro chapter
\end_layout

\begin_layout Itemize
Cynomolgus monkeys as a model organism
\end_layout

\begin_deeper
\begin_layout Itemize
Highly related to humans
\end_layout

\begin_layout Itemize
Small size and short life cycle - good research animal
\end_layout

\begin_layout Itemize
Genomics resources still in development
\end_layout

\end_deeper
\begin_layout Itemize
Inadequacy of existing blood RNA-seq protocols
\end_layout

\begin_deeper
\begin_layout Itemize
Existing protocols use a separate globin pulldown step, slowing down processing
\end_layout

\end_deeper
\end_inset


\end_layout

\begin_layout Standard
We evaluated globin reduction for 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
RNA-seq
\end_layout

\end_inset

 by blocking reverse transcription of globin transcripts using custom blocking
 
\begin_inset Flex Glossary Term (pl)
status open

\begin_layout Plain Layout
oligo
\end_layout

\end_inset

.
 We demonstrate that 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
GB
\end_layout

\end_inset

 significantly improves the cost-effectiveness of 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
RNA-seq
\end_layout

\end_inset

 in blood samples.
 Thus, our protocol offers a significant advantage to any investigator planning
 to use 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
RNA-seq
\end_layout

\end_inset

 for gene expression profiling of nonhuman primate blood samples.
 Our method can be generally applied to any species by designing complementary
 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
oligo
\end_layout

\end_inset

 blocking probes to the globin gene sequences of that species.
 Indeed, any highly expressed but biologically uninformative transcripts
 can also be blocked to further increase sequencing efficiency and value
 
\begin_inset CommandInset citation
LatexCommand cite
key "Arnaud2016"
literal "false"

\end_inset

.
\end_layout

\begin_layout Section
Methods
\end_layout

\begin_layout Subsection
Sample collection
\end_layout

\begin_layout Standard
All research reported here was done under IACUC-approved protocols at the
 University of Miami and complied with all applicable federal and state
 regulations and ethical principles for nonhuman primate research.
 Blood draws occurred between 16
\begin_inset space ~
\end_inset

April
\begin_inset space ~
\end_inset

2012 and 18
\begin_inset space ~
\end_inset

June
\begin_inset space ~
\end_inset

2015.
 The experimental system involved intrahepatic pancreatic islet transplantation
 into Cynomolgus monkeys with induced diabetes mellitus with or without
 concomitant infusion of mesenchymal stem cells.
 Blood was collected at serial time points before and after transplantation
 into PAXgene Blood RNA tubes (PreAnalytiX/Qiagen, Valencia, CA) at the
 precise volume:volume ratio of 2.5
\begin_inset space ~
\end_inset

ml whole blood into 6.9
\begin_inset space ~
\end_inset

ml of PAX gene additive.
\end_layout

\begin_layout Subsection
Globin blocking oligonucleotide design
\end_layout

\begin_layout Standard
Four 
\begin_inset Flex Glossary Term (pl)
status open

\begin_layout Plain Layout
oligo
\end_layout

\end_inset

 were designed to hybridize to the 
\begin_inset Formula $3^{\prime}$
\end_inset

 end of the transcripts for the Cynomolgus alpha and beta globin, with two
 hybridization sites for each gene.
 All 
\begin_inset Flex Glossary Term (pl)
status open

\begin_layout Plain Layout
oligo
\end_layout

\end_inset

 were purchased from Sigma and were entirely composed of 2
\begin_inset Formula $^{\prime}$
\end_inset

O-Me bases with a C3 spacer positioned at the 
\begin_inset Formula $3^{\prime}$
\end_inset

 ends to prevent any polymerase mediated primer extension.
\end_layout

\begin_layout Description
HBA1/2
\begin_inset space ~
\end_inset

site
\begin_inset space ~
\end_inset

1: 
\family typewriter
GCCCACUCAGACUUUAUUCAAAG-C3spacer
\end_layout

\begin_layout Description
HBA1/2
\begin_inset space ~
\end_inset

site
\begin_inset space ~
\end_inset

2: 
\family typewriter
GGUGCAAGGAGGGGAGGAG-C3spacer
\end_layout

\begin_layout Description
HBB
\begin_inset space ~
\end_inset

site
\begin_inset space ~
\end_inset

1: 
\family typewriter
AAUGAAAAUAAAUGUUUUUUAUUAG-C3spacer
\end_layout

\begin_layout Description
HBB
\begin_inset space ~
\end_inset

site
\begin_inset space ~
\end_inset

2: 
\family typewriter
CUCAAGGCCCUUCAUAAUAUCCC-C3spacer
\end_layout

\begin_layout Subsection
RNA-seq library preparation 
\end_layout

\begin_layout Standard
Sequencing libraries were prepared with 200
\begin_inset space ~
\end_inset

ng total RNA from each sample.
 Polyadenylated 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
mRNA
\end_layout

\end_inset

 was selected from 200
\begin_inset space ~
\end_inset

ng aliquots of cynomolgus blood-derived total RNA using Ambion Dynabeads
 Oligo(dT)25 beads (Invitrogen) following the manufacturer’s recommended
 protocol.
 PolyA selected RNA was then combined with 8
\begin_inset space ~
\end_inset

pmol of HBA1/2
\begin_inset space ~
\end_inset

(site
\begin_inset space ~
\end_inset

1), 8
\begin_inset space ~
\end_inset

pmol of HBA1/2
\begin_inset space ~
\end_inset

(site
\begin_inset space ~
\end_inset

2), 12
\begin_inset space ~
\end_inset

pmol of HBB
\begin_inset space ~
\end_inset

(site
\begin_inset space ~
\end_inset

1) and 12
\begin_inset space ~
\end_inset

pmol of HBB
\begin_inset space ~
\end_inset

(site
\begin_inset space ~
\end_inset

2) 
\begin_inset Flex Glossary Term (pl)
status open

\begin_layout Plain Layout
oligo
\end_layout

\end_inset

.
 In addition, 20
\begin_inset space ~
\end_inset

pmol of RT primer containing a portion of the Illumina adapter sequence
 (B-oligo-dTV: GAGTTCCTTGGCACCCGAGAATTCCATTTTTTTTTTTTTTTTTTTV) and 4
\begin_inset space ~
\end_inset


\emph on
μ
\emph default
L of 5X First Strand buffer (250
\begin_inset space ~
\end_inset

mM Tris-HCl pH
\begin_inset space ~
\end_inset

8.3, 375
\begin_inset space ~
\end_inset

mM KCl, 15
\begin_inset space ~
\end_inset

mM 
\begin_inset Formula $\textrm{MgCl}_{2}$
\end_inset

) were added in a total volume of 15
\begin_inset space ~
\end_inset

µL.
 The RNA was fragmented by heating this cocktail for 3 minutes at 95°C and
 then placed on ice.
 This was followed by the addition of 2
\begin_inset space ~
\end_inset

µL 0.1
\begin_inset space ~
\end_inset

M DTT, 1
\begin_inset space ~
\end_inset

µL RNaseOUT, 1
\begin_inset space ~
\end_inset

µL 10
\begin_inset space ~
\end_inset

mM dNTPs 10% biotin-16 aminoallyl-
\begin_inset Formula $2^{\prime}$
\end_inset

- dUTP and 10% biotin-16 aminoallyl-
\begin_inset Formula $2^{\prime}$
\end_inset

-dCTP (TriLink Biotech, San Diego, CA), 1
\begin_inset space ~
\end_inset

µL Superscript II (200
\begin_inset space ~
\end_inset

U/µL, Thermo-Fisher).
 A second “unblocked” library was prepared in the same way for each sample
 but replacing the blocking 
\begin_inset Flex Glossary Term (pl)
status open

\begin_layout Plain Layout
oligo
\end_layout

\end_inset

 with an equivalent volume of water.
 The reaction was carried out at 25°C for 15 minutes and 42°C for 40 minutes,
 followed by incubation at 75°C for 10 minutes to inactivate the reverse
 transcriptase.
\end_layout

\begin_layout Standard
The cDNA/RNA hybrid molecules were purified using 1.8X Ampure XP beads (Agencourt
) following supplier’s recommended protocol.
 The cDNA/RNA hybrid was eluted in 25
\begin_inset space ~
\end_inset

µL of 10
\begin_inset space ~
\end_inset

mM Tris-HCl pH
\begin_inset space ~
\end_inset

8.0, and then bound to 25
\begin_inset space ~
\end_inset

µL of M280 Magnetic Streptavidin beads washed per recommended protocol (Thermo-F
isher).
 After 30 minutes of binding, beads were washed one time in 100
\begin_inset space ~
\end_inset

µL 0.1
\begin_inset space ~
\end_inset

N NaOH to denature and remove the bound RNA, followed by two 100
\begin_inset space ~
\end_inset

µL washes with 1X TE buffer.
\end_layout

\begin_layout Standard
Subsequent attachment of the 
\begin_inset Formula $5^{\prime}$
\end_inset

 Illumina A adapter was performed by on-bead random primer extension of
 the following sequence (A-N8 primer: 
\family typewriter
TTCAGAGTTCTACAGTCCGACGATCNNNNNNNN
\family default
).
 Briefly, beads were resuspended in a 20
\begin_inset space ~
\end_inset

µL reaction containing 5
\begin_inset space ~
\end_inset

µM A-N8 primer, 40
\begin_inset space ~
\end_inset

mM Tris-HCl pH
\begin_inset space ~
\end_inset

7.5, 20
\begin_inset space ~
\end_inset

mM 
\begin_inset Formula $\textrm{MgCl}_{2}$
\end_inset

, 50
\begin_inset space ~
\end_inset

mM NaCl, 0.325
\begin_inset space ~
\end_inset

U/µL Sequenase
\begin_inset space ~
\end_inset

2.0 (Affymetrix, Santa Clara, CA), 0.0025
\begin_inset space ~
\end_inset

U/µL inorganic pyrophosphatase (Affymetrix) and 300
\begin_inset space ~
\end_inset

µM each dNTP.
 Reaction was incubated at 22°C for 30 minutes, then beads were washed 2
 times with 1X TE buffer (200
\begin_inset space ~
\end_inset

µL).
\end_layout

\begin_layout Standard
The magnetic streptavidin beads were resuspended in 34
\begin_inset space ~
\end_inset

µL nuclease-free water and added directly to a 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
PCR
\end_layout

\end_inset

 tube.
 The two Illumina protocol-specified 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
PCR
\end_layout

\end_inset

 primers were added at 0.53
\begin_inset space ~
\end_inset

µM (Illumina TruSeq Universal Primer 1 and Illumina TruSeq barcoded 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
PCR
\end_layout

\end_inset

 primer 2), along with 40
\begin_inset space ~
\end_inset

µL 2X KAPA HiFi Hotstart ReadyMix (KAPA, Willmington MA) and thermocycled
 as follows: starting with 98°C (2 min-hold); 15 cycles of 98°C, 20sec;
 60°C, 30sec; 72°C, 30sec; and finished with a 72°C (2 min-hold).
\end_layout

\begin_layout Standard
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
PCR
\end_layout

\end_inset

 products were purified with 1X Ampure Beads following manufacturer’s recommende
d protocol.
 Libraries were then analyzed using the Agilent TapeStation and quantitation
 of desired size range was performed by “smear analysis”.
 Samples were pooled in equimolar batches of 16 samples.
 Pooled libraries were size selected on 2% agarose gels (E-Gel EX Agarose
 Gels; Thermo-Fisher).
 Products were cut between 250 and 350
\begin_inset space ~
\end_inset

bp (corresponding to insert sizes of 130 to 230
\begin_inset space ~
\end_inset

bp).
 Finished library pools were then sequenced on the Illumina NextSeq500 instrumen
t with 75
\begin_inset space ~
\end_inset

bp read lengths.
 
\end_layout

\begin_layout Subsection
Read alignment and counting
\end_layout

\begin_layout Standard
\begin_inset ERT
status collapsed

\begin_layout Plain Layout


\backslash
emergencystretch 3em
\end_layout

\end_inset


\begin_inset Note Note
status collapsed

\begin_layout Plain Layout
Need to relax the justification parameters just for this paragraph, or else
 featureCounts can break out of the margin.
\end_layout

\end_inset


\end_layout

\begin_layout Standard
Reads were aligned to the cynomolgus genome using STAR 
\begin_inset CommandInset citation
LatexCommand cite
key "Wilson2013,Dobin2012"
literal "false"

\end_inset

.
 Counts of uniquely mapped reads were obtained for every gene in each sample
 with the 
\begin_inset Flex Code
status open

\begin_layout Plain Layout
featureCounts
\end_layout

\end_inset

 function from the 
\begin_inset Flex Code
status open

\begin_layout Plain Layout
Rsubread
\end_layout

\end_inset

 package, using each of the three possibilities for the 
\begin_inset Flex Code
status open

\begin_layout Plain Layout
strandSpecific
\end_layout

\end_inset

 option: sense, antisense, and unstranded 
\begin_inset CommandInset citation
LatexCommand cite
key "Liao2014"
literal "false"

\end_inset

.
 A few artifacts in the cynomolgus genome annotation complicated read counting.
 First, no ortholog is annotated for alpha globin in the cynomolgus genome,
 presumably because the human genome has two alpha globin genes with nearly
 identical sequences, making the orthology relationship ambiguous.
 However, two loci in the cynomolgus genome are annotated as “hemoglobin
 subunit alpha-like” (LOC102136192 and LOC102136846).
 LOC102136192 is annotated as a pseudogene while LOC102136846 is annotated
 as protein-coding.
 Our globin reduction protocol was designed to include blocking of these
 two genes.
 Indeed, these two genes together have almost the same read counts in each
 library as the properly-annotated HBB gene and much larger counts than
 any other gene in the unblocked libraries, giving confidence that reads
 derived from the real alpha globin are mapping to both genes.
 Thus, reads from both of these loci were counted as alpha globin reads
 in all further analyses.
 The second artifact is a small, uncharacterized non-coding RNA gene (LOC1021365
91), which overlaps the HBA-like gene (LOC102136192) on the opposite strand.
 If counting is not performed in stranded mode (or if a non-strand-specific
 sequencing protocol is used), many reads mapping to the globin gene will
 be discarded as ambiguous due to their overlap with this 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
ncRNA
\end_layout

\end_inset

 gene, resulting in significant undercounting of globin reads.
 Therefore, stranded sense counts were used for all further analysis in
 the present study to insure that we accurately accounted for globin transcript
 reduction.
 However, we note that stranded reads are not necessary for 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
RNA-seq
\end_layout

\end_inset

 using our protocol in standard practice.
 
\end_layout

\begin_layout Standard
\begin_inset ERT
status collapsed

\begin_layout Plain Layout


\backslash
emergencystretch 0em
\end_layout

\end_inset


\end_layout

\begin_layout Subsection
Normalization and exploratory data analysis
\end_layout

\begin_layout Standard
Libraries were normalized by computing scaling factors using the 
\begin_inset Flex Code
status open

\begin_layout Plain Layout
edgeR
\end_layout

\end_inset

 package's 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
TMM
\end_layout

\end_inset

 method 
\begin_inset CommandInset citation
LatexCommand cite
key "Robinson2010"
literal "false"

\end_inset

.
 
\begin_inset Flex Glossary Term (Capital)
status open

\begin_layout Plain Layout
logCPM
\end_layout

\end_inset

 values were calculated using the 
\begin_inset Flex Code
status open

\begin_layout Plain Layout
cpm
\end_layout

\end_inset

 function in 
\begin_inset Flex Code
status open

\begin_layout Plain Layout
edgeR
\end_layout

\end_inset

 for individual samples and 
\begin_inset Flex Code
status open

\begin_layout Plain Layout
aveLogCPM
\end_layout

\end_inset

 function for averages across groups of samples, using those functions’
 default prior count values to avoid taking the logarithm of 0.
 Genes were considered “present” if their average normalized 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
logCPM
\end_layout

\end_inset

 values across all libraries were at least 
\begin_inset Formula $-1$
\end_inset

.
 Normalizing for gene length was unnecessary because the sequencing protocol
 is 
\begin_inset Formula $3^{\prime}$
\end_inset

-biased and hence the expected read count for each gene is related to the
 transcript’s copy number but not its length.
\end_layout

\begin_layout Standard
In order to assess the effect of 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
GB
\end_layout

\end_inset

 on reproducibility, Pearson and Spearman correlation coefficients were
 computed between the 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
logCPM
\end_layout

\end_inset

 values for every pair of libraries within the 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
GB
\end_layout

\end_inset

 non-GB groups, and 
\begin_inset Flex Code
status open

\begin_layout Plain Layout
edgeR
\end_layout

\end_inset

's 
\begin_inset Flex Code
status open

\begin_layout Plain Layout
estimateDisp
\end_layout

\end_inset

 function was used to compute 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
NB
\end_layout

\end_inset

 dispersions separately for the two groups 
\begin_inset CommandInset citation
LatexCommand cite
key "Chen2014"
literal "false"

\end_inset

.
\end_layout

\begin_layout Subsection
Differential expression analysis
\end_layout

\begin_layout Standard
All tests for differential gene expression were performed using 
\begin_inset Flex Code
status open

\begin_layout Plain Layout
edgeR
\end_layout

\end_inset

, by first fitting a 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
NB
\end_layout

\end_inset

 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
GLM
\end_layout

\end_inset

 to the counts and normalization factors and then performing a quasi-likelihood
 F-test with robust estimation of outlier gene dispersions 
\begin_inset CommandInset citation
LatexCommand cite
key "Lund2012,Phipson2016"
literal "false"

\end_inset

.
 To investigate the effects of 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
GB
\end_layout

\end_inset

 on each gene, an additive model was fit to the full data with coefficients
 for 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
GB
\end_layout

\end_inset

 and Sample 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
ID
\end_layout

\end_inset

.
 To test the effect of 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
GB
\end_layout

\end_inset

 on detection of differentially expressed genes, the 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
GB
\end_layout

\end_inset

 samples and non-GB samples were each analyzed independently as follows:
 for each animal with both a pre-transplant and a post-transplant time point
 in the data set, the pre-transplant sample and the earliest post-transplant
 sample were selected, and all others were excluded, yielding a pre-/post-transp
lant pair of samples for each animal (
\begin_inset Formula $N=7$
\end_inset

 animals with paired samples).
 These samples were analyzed for pre-transplant vs.
 post-transplant differential gene expression while controlling for inter-animal
 variation using an additive model with coefficients for transplant and
 animal 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
ID
\end_layout

\end_inset

.
 In all analyses, p-values were adjusted using the 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
BH
\end_layout

\end_inset

 procedure for 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
FDR
\end_layout

\end_inset

 control 
\begin_inset CommandInset citation
LatexCommand cite
key "Benjamini1995"
literal "false"

\end_inset

.
\end_layout

\begin_layout Standard
\begin_inset Note Note
status open

\begin_layout Itemize
New blood RNA-seq protocol to block reverse transcription of globin genes
\end_layout

\begin_layout Itemize
Blood RNA-seq time course after transplants with/without MSC infusion
\end_layout

\end_inset


\end_layout

\begin_layout Section
Results
\end_layout

\begin_layout Subsection
Globin blocking yields a larger and more consistent fraction of useful reads
\end_layout

\begin_layout Standard
The objective of the present study was to validate a new protocol for deep
 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
RNA-seq
\end_layout

\end_inset

 of whole blood drawn into PaxGene tubes from cynomolgus monkeys undergoing
 islet transplantation, with particular focus on minimizing the loss of
 useful sequencing space to uninformative globin reads.
 The details of the analysis with respect to transplant outcomes and the
 impact of mesenchymal stem cell treatment will be reported in a separate
 manuscript (in preparation).
 To focus on the efficacy of our 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
GB
\end_layout

\end_inset

 protocol, 37 blood samples, 16 from pre-transplant and 21 from post-transplant
 time points, were each prepped once with and once without 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
GB
\end_layout

\end_inset

 
\begin_inset Flex Glossary Term (pl)
status open

\begin_layout Plain Layout
oligo
\end_layout

\end_inset

, and were then sequenced on an Illumina NextSeq500 instrument.
 The number of reads aligning to each gene in the cynomolgus genome was
 counted.
 Table 
\begin_inset CommandInset ref
LatexCommand ref
reference "tab:Fractions-of-reads"
plural "false"
caps "false"
noprefix "false"

\end_inset

 summarizes the distribution of read fractions among the 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
GB
\end_layout

\end_inset

 and non-GB libraries.
 In the libraries with no 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
GB
\end_layout

\end_inset

, globin reads made up an average of 44.6% of total input reads, while reads
 assigned to all other genes made up an average of 26.3%.
 The remaining reads either aligned to intergenic regions (that include
 long non-coding RNAs) or did not align with any annotated transcripts in
 the current build of the cynomolgus genome.
 In the 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
GB
\end_layout

\end_inset

 libraries, globin reads made up only 3.48% and reads assigned to all other
 genes increased to 50.4%.
 Thus, 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
GB
\end_layout

\end_inset

 resulted in a 92.2% reduction in globin reads and a 91.6% increase in yield
 of useful non-globin reads.
\end_layout

\begin_layout Standard
\begin_inset ERT
status open

\begin_layout Plain Layout


\backslash
afterpage{
\end_layout

\begin_layout Plain Layout


\backslash
begin{landscape}
\end_layout

\end_inset


\end_layout

\begin_layout Standard
\begin_inset Float table
placement p
wide false
sideways false
status collapsed

\begin_layout Plain Layout
\align center
\begin_inset Tabular
<lyxtabular version="3" rows="4" columns="7">
<features tabularvalignment="middle">
<column alignment="center" valignment="top">
<column alignment="center" valignment="top">
<column alignment="center" valignment="top">
<column alignment="center" valignment="top">
<column alignment="center" valignment="top">
<column alignment="center" valignment="top">
<column alignment="center" valignment="top">
<row>
<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout

\end_layout

\end_inset
</cell>
<cell multicolumn="1" alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout

\family roman
\series medium
\shape up
\size normal
\emph off
\bar no
\strikeout off
\xout off
\uuline off
\uwave off
\noun off
\color none
Percent of Total Reads
\end_layout

\end_inset
</cell>
<cell multicolumn="2" alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout

\end_layout

\end_inset
</cell>
<cell multicolumn="2" alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout

\end_layout

\end_inset
</cell>
<cell multicolumn="2" alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout

\end_layout

\end_inset
</cell>
<cell multicolumn="1" alignment="center" valignment="top" topline="true" leftline="true" rightline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout

\family roman
\series medium
\shape up
\size normal
\emph off
\bar no
\strikeout off
\xout off
\uuline off
\uwave off
\noun off
\color none
Percent of Genic Reads
\end_layout

\end_inset
</cell>
<cell multicolumn="2" alignment="center" valignment="top" topline="true" leftline="true" rightline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout

\end_layout

\end_inset
</cell>
</row>
<row>
<cell alignment="center" valignment="top" bottomline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
GB
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout

\family roman
\series medium
\shape up
\size normal
\emph off
\bar no
\strikeout off
\xout off
\uuline off
\uwave off
\noun off
\color none
Non-globin Reads
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout

\family roman
\series medium
\shape up
\size normal
\emph off
\bar no
\strikeout off
\xout off
\uuline off
\uwave off
\noun off
\color none
Globin Reads
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout

\family roman
\series medium
\shape up
\size normal
\emph off
\bar no
\strikeout off
\xout off
\uuline off
\uwave off
\noun off
\color none
All Genic Reads
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout

\family roman
\series medium
\shape up
\size normal
\emph off
\bar no
\strikeout off
\xout off
\uuline off
\uwave off
\noun off
\color none
All Aligned Reads
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout

\family roman
\series medium
\shape up
\size normal
\emph off
\bar no
\strikeout off
\xout off
\uuline off
\uwave off
\noun off
\color none
Non-globin Reads
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" rightline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout

\family roman
\series medium
\shape up
\size normal
\emph off
\bar no
\strikeout off
\xout off
\uuline off
\uwave off
\noun off
\color none
Globin Reads
\end_layout

\end_inset
</cell>
</row>
<row>
<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout

\family roman
\series medium
\shape up
\size normal
\emph off
\bar no
\strikeout off
\xout off
\uuline off
\uwave off
\noun off
\color none
Yes
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout

\family roman
\series medium
\shape up
\size normal
\emph off
\bar no
\strikeout off
\xout off
\uuline off
\uwave off
\noun off
\color none
50.4% ± 6.82
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout

\family roman
\series medium
\shape up
\size normal
\emph off
\bar no
\strikeout off
\xout off
\uuline off
\uwave off
\noun off
\color none
3.48% ± 2.94
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout

\family roman
\series medium
\shape up
\size normal
\emph off
\bar no
\strikeout off
\xout off
\uuline off
\uwave off
\noun off
\color none
53.9% ± 6.81
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout

\family roman
\series medium
\shape up
\size normal
\emph off
\bar no
\strikeout off
\xout off
\uuline off
\uwave off
\noun off
\color none
89.7% ± 2.40
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout

\family roman
\series medium
\shape up
\size normal
\emph off
\bar no
\strikeout off
\xout off
\uuline off
\uwave off
\noun off
\color none
93.5% ± 5.25
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" leftline="true" rightline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout

\family roman
\series medium
\shape up
\size normal
\emph off
\bar no
\strikeout off
\xout off
\uuline off
\uwave off
\noun off
\color none
6.49% ± 5.25
\end_layout

\end_inset
</cell>
</row>
<row>
<cell alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout

\family roman
\series medium
\shape up
\size normal
\emph off
\bar no
\strikeout off
\xout off
\uuline off
\uwave off
\noun off
\color none
No
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout

\family roman
\series medium
\shape up
\size normal
\emph off
\bar no
\strikeout off
\xout off
\uuline off
\uwave off
\noun off
\color none
26.3% ± 8.95
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout

\family roman
\series medium
\shape up
\size normal
\emph off
\bar no
\strikeout off
\xout off
\uuline off
\uwave off
\noun off
\color none
44.6% ± 16.6
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout

\family roman
\series medium
\shape up
\size normal
\emph off
\bar no
\strikeout off
\xout off
\uuline off
\uwave off
\noun off
\color none
70.1% ± 9.38
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout

\family roman
\series medium
\shape up
\size normal
\emph off
\bar no
\strikeout off
\xout off
\uuline off
\uwave off
\noun off
\color none
90.7% ± 5.16
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout

\family roman
\series medium
\shape up
\size normal
\emph off
\bar no
\strikeout off
\xout off
\uuline off
\uwave off
\noun off
\color none
38.8% ± 17.1
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" rightline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout

\family roman
\series medium
\shape up
\size normal
\emph off
\bar no
\strikeout off
\xout off
\uuline off
\uwave off
\noun off
\color none
61.2% ± 17.1
\end_layout

\end_inset
</cell>
</row>
</lyxtabular>

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout
\begin_inset Argument 1
status collapsed

\begin_layout Plain Layout
Fractions of reads mapping to genomic features in GB and non-GB samples.
\end_layout

\end_inset


\begin_inset CommandInset label
LatexCommand label
name "tab:Fractions-of-reads"

\end_inset


\series bold
Fractions of reads mapping to genomic features in GB and non-GB samples.
 
\series default
All values are given as mean ± standard deviation.
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Standard
\begin_inset ERT
status open

\begin_layout Plain Layout


\backslash
end{landscape}
\end_layout

\begin_layout Plain Layout

}
\end_layout

\end_inset


\end_layout

\begin_layout Standard
This reduction is not quite as efficient as the previous analysis showed
 for human samples by DeepSAGE (<0.4% globin reads after globin reduction)
 
\begin_inset CommandInset citation
LatexCommand cite
key "Mastrokolias2012"
literal "false"

\end_inset

.
 Nonetheless, this degree of globin reduction is sufficient to nearly double
 the yield of useful reads.
 Thus, 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
GB
\end_layout

\end_inset

 cuts the required sequencing effort (and costs) to achieve a target coverage
 depth by almost 50%.
 Consistent with this near doubling of yield, the average difference in
 un-normalized 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
logCPM
\end_layout

\end_inset

 across all genes between the 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
GB
\end_layout

\end_inset

 libraries and non-GB libraries is approximately 1 (mean = 1.01, median =
 1.08), an overall 2-fold increase.
 Un-normalized values are used here because the 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
TMM
\end_layout

\end_inset

 normalization correctly identifies this 2-fold difference as biologically
 irrelevant and removes it.
\end_layout

\begin_layout Standard
Another important aspect is that the standard deviations in Table 
\begin_inset CommandInset ref
LatexCommand ref
reference "tab:Fractions-of-reads"
plural "false"
caps "false"
noprefix "false"

\end_inset

 are uniformly smaller in the 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
GB
\end_layout

\end_inset

 samples than the non-GB ones, indicating much greater consistency of yield.
 This is best seen in the percentage of non-globin reads as a fraction of
 total reads aligned to annotated genes (genic reads).
 For the non-GB samples, this measure ranges from 10.9% to 80.9%, while for
 the 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
GB
\end_layout

\end_inset

 samples it ranges from 81.9% to 99.9% (Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:Fraction-of-genic-reads"
plural "false"
caps "false"
noprefix "false"

\end_inset


\begin_inset Float figure
wide false
sideways false
status collapsed

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename graphics/globin-paper/figure1-globin-fractions.pdf
	lyxscale 50
	width 100col%
	groupId colfullwidth

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout
\begin_inset Argument 1
status collapsed

\begin_layout Plain Layout
Fraction of genic reads in each sample aligned to non-globin genes, with
 and without GB.
\end_layout

\end_inset


\begin_inset CommandInset label
LatexCommand label
name "fig:Fraction-of-genic-reads"

\end_inset


\series bold
Fraction of genic reads in each sample aligned to non-globin genes, with
 and without GB.

\series default
 All reads in each sequencing library were aligned to the cyno genome, and
 the number of reads uniquely aligning to each gene was counted.
 For each sample, counts were summed separately for all globin genes and
 for the remainder of the genes (non-globin genes), and the fraction of
 genic reads aligned to non-globin genes was computed.
 Each point represents an individual sample.
 Gray + signs indicate the means for globin-blocked libraries and unblocked
 libraries.
 The overall distribution for each group is represented as a notched box
 plot.
 Points are randomly spread vertically to avoid excessive overlapping.
\end_layout

\end_inset


\end_layout

\end_inset


\begin_inset Note Note
status open

\begin_layout Plain Layout
Float lost issues
\end_layout

\end_inset

).
 This means that for applications where it is critical that each sample
 achieve a specified minimum coverage in order to provide useful information,
 it would be necessary to budget up to 10 times the sequencing depth per
 sample without 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
GB
\end_layout

\end_inset

, even though the average yield improvement for 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
GB
\end_layout

\end_inset

 is only 2-fold, because every sample has a chance of being 90% globin and
 10% useful reads.
 Hence, the more consistent behavior of 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
GB
\end_layout

\end_inset

 samples makes planning an experiment easier and more efficient because
 it eliminates the need to over-sequence every sample in order to guard
 against the worst case of a high-globin fraction.
\end_layout

\begin_layout Subsection
Globin blocking lowers the noise floor and allows detection of about 2000
 more low-expression genes
\end_layout

\begin_layout Standard
\begin_inset Flex TODO Note (inline)
status open

\begin_layout Plain Layout
Remove redundant titles from figures
\end_layout

\end_inset


\end_layout

\begin_layout Standard
Since 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
GB
\end_layout

\end_inset

 yields more usable sequencing depth, it should also allow detection of
 more genes at any given threshold.
 When we looked at the distribution of average normalized 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
logCPM
\end_layout

\end_inset

 values across all libraries for genes with at least one read assigned to
 them, we observed the expected bimodal distribution, with a high-abundance
 "signal" peak representing detected genes and a low-abundance "noise" peak
 representing genes whose read count did not rise above the noise floor
 (Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:logcpm-dists"
plural "false"
caps "false"
noprefix "false"

\end_inset

).
 Consistent with the 2-fold increase in raw counts assigned to non-globin
 genes, the signal peak for 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
GB
\end_layout

\end_inset

 samples is shifted to the right relative to the non-GB signal peak.
 When all the samples are normalized together, this difference is normalized
 out, lining up the signal peaks, and this reveals that, as expected, the
 noise floor for the 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
GB
\end_layout

\end_inset

 samples is about 2-fold lower.
 This greater separation between signal and noise peaks in the 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
GB
\end_layout

\end_inset

 samples means that low-expression genes should be more easily detected
 and more precisely quantified than in the non-GB samples.
\end_layout

\begin_layout Standard
\begin_inset Float figure
wide false
sideways false
status open

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename graphics/globin-paper/figure2-aveLogCPM-colored.pdf
	lyxscale 50
	height 60theight%

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout
\begin_inset Argument 1
status collapsed

\begin_layout Plain Layout
Distributions of average group gene abundances when normalized separately
 or together.
\end_layout

\end_inset


\begin_inset CommandInset label
LatexCommand label
name "fig:logcpm-dists"

\end_inset


\series bold
Distributions of average group gene abundances when normalized separately
 or together.

\series default
 All reads in each sequencing library were aligned to the cyno genome, and
 the number of reads uniquely aligning to each gene was counted.
 Genes with zero counts in all libraries were discarded.
 Libraries were normalized using the TMM method.
 Libraries were split into GB and non-GB groups and the average logCPM was
 computed.
 The distribution of average gene logCPM values was plotted for both groups
 using a kernel density plot to approximate a continuous distribution.
 The GB logCPM distributions are marked in red, non-GB in blue.
 The black vertical line denotes the chosen detection threshold of 
\begin_inset Formula $-1$
\end_inset

.
 Top panel: Libraries were split into GB and non-GB groups first and normalized
 separately.
 Bottom panel: Libraries were all normalized together first and then split
 into groups.
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Standard
Based on these distributions, we selected a detection threshold of 
\begin_inset Formula $-1$
\end_inset

, which is approximately the leftmost edge of the trough between the signal
 and noise peaks.
 This represents the most liberal possible detection threshold that doesn't
 call substantial numbers of noise genes as detected.
 Among the full dataset, 13429 genes were detected at this threshold, and
 22276 were not.
 When considering the 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
GB
\end_layout

\end_inset

 libraries and non-GB libraries separately and re-computing normalization
 factors independently within each group, 14535 genes were detected in the
 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
GB
\end_layout

\end_inset

 libraries while only 12460 were detected in the non-GB libraries.
 Thus, 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
GB
\end_layout

\end_inset

 allowed the detection of 2000 extra genes that were buried under the noise
 floor without 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
GB
\end_layout

\end_inset

.
 This pattern of at least 2000 additional genes detected with 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
GB
\end_layout

\end_inset

 was also consistent across a wide range of possible detection thresholds,
 from -2 to 3 (see Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:Gene-detections"
plural "false"
caps "false"
noprefix "false"

\end_inset

).
\end_layout

\begin_layout Standard
\begin_inset Float figure
wide false
sideways false
status open

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename graphics/globin-paper/figure3-detection.pdf
	lyxscale 50
	width 70col%

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout
\begin_inset Argument 1
status collapsed

\begin_layout Plain Layout
Gene detections as a function of abundance thresholds in GB and non-GB samples.
\end_layout

\end_inset


\begin_inset CommandInset label
LatexCommand label
name "fig:Gene-detections"

\end_inset


\series bold
Gene detections as a function of abundance thresholds in GB and non-GB samples.

\series default
 Average logCPM was computed by separate group normalization as described
 in Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:logcpm-dists"
plural "false"
caps "false"
noprefix "false"

\end_inset

 for both the GB and non-GB groups, as well as for all samples considered
 as one large group.
 For each every integer threshold from 
\begin_inset Formula $-2$
\end_inset

 to 3, the number of genes detected at or above that logCPM threshold was
 plotted for each group.
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Subsection
Globin blocking does not add significant additional noise or decrease sample
 quality
\end_layout

\begin_layout Standard
One potential worry is that the 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
GB
\end_layout

\end_inset

 protocol could perturb the levels of non-globin genes.
 There are two kinds of possible perturbations: systematic and random.
 The former is not a major concern for detection of differential expression,
 since a 2-fold change in every sample has no effect on the relative fold
 change between samples.
 In contrast, random perturbations would increase the noise and obscure
 the signal in the dataset, reducing the capacity to detect differential
 expression.
\end_layout

\begin_layout Standard
The data do indeed show small systematic perturbations in gene levels (Figure
 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:MA-plot"
plural "false"
caps "false"
noprefix "false"

\end_inset

).
 Other than the 3 designated alpha and beta globin genes, two other genes
 stand out as having especially large negative 
\begin_inset Flex Glossary Term (pl)
status open

\begin_layout Plain Layout
logFC
\end_layout

\end_inset

: HBD and LOC1021365.
 HBD, delta globin, is most likely targeted by the blocking 
\begin_inset Flex Glossary Term (pl)
status open

\begin_layout Plain Layout
oligo
\end_layout

\end_inset

 due to high sequence homology with the other globin genes.
 LOC1021365 is the aforementioned 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
ncRNA
\end_layout

\end_inset

 that is reverse-complementary to one of the alpha-like genes and that would
 be expected to be removed during the 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
GB
\end_layout

\end_inset

 step.
 All other genes appear in a cluster centered vertically at 0, and the vast
 majority of genes in this cluster show an absolute 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
logFC
\end_layout

\end_inset

 of 0.5 or less.
 Nevertheless, many of these small perturbations are still statistically
 significant, indicating that the 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
GB
\end_layout

\end_inset

 
\begin_inset Flex Glossary Term (pl)
status open

\begin_layout Plain Layout
oligo
\end_layout

\end_inset

 likely cause very small but non-zero systematic perturbations in measured
 gene expression levels.
\end_layout

\begin_layout Standard
\begin_inset Float figure
wide false
sideways false
status open

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename graphics/globin-paper/figure4-maplot-colored.pdf
	lyxscale 50
	width 100col%
	groupId colfullwidth

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout
\begin_inset Argument 1
status collapsed

\begin_layout Plain Layout
MA plot showing effects of GB on each gene's abundance.
\end_layout

\end_inset


\begin_inset CommandInset label
LatexCommand label
name "fig:MA-plot"

\end_inset


\series bold
MA plot showing effects of GB on each gene's abundance.
 
\series default
All libraries were normalized together as described in Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:logcpm-dists"
plural "false"
caps "false"
noprefix "false"

\end_inset

, and genes with an average logCPM below 
\begin_inset Formula $-1$
\end_inset

 were filtered out.
 Each remaining gene was tested for differential abundance with respect
 to 
\begin_inset Flex Glossary Term (glstext)
status open

\begin_layout Plain Layout
GB
\end_layout

\end_inset

 using 
\begin_inset Flex Code
status open

\begin_layout Plain Layout
edgeR
\end_layout

\end_inset

’s quasi-likelihood F-test, fitting a NB GLM to table of read counts in
 each library.
 For each gene, 
\begin_inset Flex Code
status open

\begin_layout Plain Layout
edgeR
\end_layout

\end_inset

 reported average logCPM, logFC, p-value, and BH-adjusted FDR.
 Each gene's logFC was plotted against its logCPM, colored by FDR.
 Red points are significant at 
\begin_inset Formula $≤10\%$
\end_inset

 FDR, and blue are not significant at that threshold.
 The alpha and beta globin genes targeted for blocking are marked with large
 triangles, while all other genes are represented as small points.
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Standard
To evaluate the possibility of 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
GB
\end_layout

\end_inset

 causing random perturbations and reducing sample quality, we computed the
 Pearson correlation between 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
logCPM
\end_layout

\end_inset

 values for every pair of samples with and without 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
GB
\end_layout

\end_inset

 and plotted them against each other (Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:gene-abundance-correlations"
plural "false"
caps "false"
noprefix "false"

\end_inset

).
 The plot indicated that the 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
GB
\end_layout

\end_inset

 libraries have higher sample-to-sample correlations than the non-GB libraries.
 Parametric and nonparametric tests for differences between the correlations
 with and without 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
GB
\end_layout

\end_inset

 both confirmed that this difference was highly significant (2-sided paired
 t-test: 
\begin_inset Formula $t=37.2$
\end_inset

, 
\begin_inset Formula $d.f.=665$
\end_inset

, 
\begin_inset Formula $P\ll2.2\times10^{-16}$
\end_inset

; 2-sided Wilcoxon sign-rank test: 
\begin_inset Formula $V=2195$
\end_inset

, 
\begin_inset Formula $P\ll2.2\times10^{-16}$
\end_inset

).
 Performing the same tests on the Spearman correlations gave the same conclusion
 (t-test: 
\begin_inset Formula $t=26.8$
\end_inset

, 
\begin_inset Formula $d.f.=665$
\end_inset

, 
\begin_inset Formula $P\ll2.2\times10^{-16}$
\end_inset

; sign-rank test: 
\begin_inset Formula $V=8781$
\end_inset

, 
\begin_inset Formula $P\ll2.2\times10^{-16}$
\end_inset

).
 The 
\begin_inset Flex Code
status open

\begin_layout Plain Layout
edgeR
\end_layout

\end_inset

 package was used to compute the overall 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
BCV
\end_layout

\end_inset

 for 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
GB
\end_layout

\end_inset

 and non-GB libraries, and found that 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
GB
\end_layout

\end_inset

 resulted in a negligible increase in the 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
BCV
\end_layout

\end_inset

 (0.417 with 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
GB
\end_layout

\end_inset

 vs.
 0.400 without).
 The near equality of the 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
BCV
\end_layout

\end_inset

 for both sets indicates that the higher correlations in the 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
GB
\end_layout

\end_inset

 libraries are most likely a result of the increased yield of useful reads,
 which reduces the contribution of Poisson counting uncertainty to the overall
 variance of the 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
logCPM
\end_layout

\end_inset

 values 
\begin_inset CommandInset citation
LatexCommand cite
key "McCarthy2012"
literal "false"

\end_inset

.
 This improves the precision of expression measurements and more than offsets
 the negligible increase in 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
BCV
\end_layout

\end_inset

.
\end_layout

\begin_layout Standard
\begin_inset Float figure
wide false
sideways false
status open

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename graphics/globin-paper/figure5-corrplot.pdf
	lyxscale 50
	width 100col%
	groupId colfullwidth

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout
\begin_inset Argument 1
status collapsed

\begin_layout Plain Layout
Comparison of inter-sample gene abundance correlations with and without
 GB.
\end_layout

\end_inset


\begin_inset CommandInset label
LatexCommand label
name "fig:gene-abundance-correlations"

\end_inset


\series bold
Comparison of inter-sample gene abundance correlations with and without
 GB.

\series default
 All libraries were normalized together as described in Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:logcpm-dists"
plural "false"
caps "false"
noprefix "false"

\end_inset

, and genes with an average logCPM less than 
\begin_inset Formula $-1$
\end_inset

 were filtered out.
 Each gene’s logCPM was computed in each library using 
\begin_inset Flex Code
status open

\begin_layout Plain Layout
edgeR
\end_layout

\end_inset

's 
\begin_inset Flex Code
status open

\begin_layout Plain Layout
cpm
\end_layout

\end_inset

 function.
 For each pair of biological samples, the Pearson correlation between those
 samples' GB libraries was plotted against the correlation between the same
 samples' non-GB libraries.
 Each point represents an unique pair of samples.
 The solid gray line shows a quantile-quantile plot of the distribution
 of inter-sample correlations with GB vs.
 without GB.
 The thin dashed line is the identity line, provided for reference.
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Subsection
More differentially expressed genes are detected with globin blocking
\end_layout

\begin_layout Standard
To compare performance on differential gene expression tests, we took subsets
 of both the 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
GB
\end_layout

\end_inset

 and non-GB libraries with exactly one pre-transplant and one post-transplant
 sample for each animal that had paired samples available for analysis (
\begin_inset Formula $N=7$
\end_inset

 animals, 
\begin_inset Formula $N=14$
\end_inset

 samples in each subset).
 The same test for pre- vs.
 post-transplant differential gene expression was performed on the same
 7 pairs of samples from 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
GB
\end_layout

\end_inset

 libraries and non-GB libraries, in each case using an 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
FDR
\end_layout

\end_inset

 of 10% as the threshold of significance.
 Out of 12,954 genes that passed the detection threshold in both subsets,
 358 were called significantly differentially expressed in the same direction
 in both sets; 1063 were differentially expressed in the 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
GB
\end_layout

\end_inset

 set only; 296 were differentially expressed in the non-GB set only; 2 genes
 were called significantly up in the 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
GB
\end_layout

\end_inset

 set but significantly down in the non-GB set; and the remaining 11,235
 were not called differentially expressed in either set.
 These data are summarized in Table 
\begin_inset CommandInset ref
LatexCommand ref
reference "tab:Comparison-of-significant"
plural "false"
caps "false"
noprefix "false"

\end_inset

.
 The differences in 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
BCV
\end_layout

\end_inset

 calculated by 
\begin_inset Flex Code
status open

\begin_layout Plain Layout
edgeR
\end_layout

\end_inset

 for these subsets of samples were negligible (
\begin_inset Formula $\textrm{BCV}=0.302$
\end_inset

 for 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
GB
\end_layout

\end_inset

 and 0.297 for non-GB).
\end_layout

\begin_layout Standard
\begin_inset Float table
wide false
sideways false
status collapsed

\begin_layout Plain Layout
\align center
\begin_inset Tabular
<lyxtabular version="3" rows="5" columns="5">
<features tabularvalignment="middle">
<column alignment="center" valignment="top">
<column alignment="center" valignment="top">
<column alignment="center" valignment="top">
<column alignment="center" valignment="top">
<column alignment="center" valignment="top">
<row>
<cell alignment="center" valignment="top" usebox="none">
\begin_inset Text

\begin_layout Plain Layout

\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" usebox="none">
\begin_inset Text

\begin_layout Plain Layout

\end_layout

\end_inset
</cell>
<cell multicolumn="1" alignment="center" valignment="top" topline="true" leftline="true" rightline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout

\series bold
No Globin Blocking
\end_layout

\end_inset
</cell>
<cell multicolumn="2" alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout

\end_layout

\end_inset
</cell>
<cell multicolumn="2" alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" rightline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout

\end_layout

\end_inset
</cell>
</row>
<row>
<cell alignment="center" valignment="top" usebox="none">
\begin_inset Text

\begin_layout Plain Layout

\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" usebox="none">
\begin_inset Text

\begin_layout Plain Layout

\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout

\series bold
Up
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout

\series bold
NS
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" leftline="true" rightline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout

\series bold
Down
\end_layout

\end_inset
</cell>
</row>
<row>
<cell multirow="3" alignment="center" valignment="middle" topline="true" bottomline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout

\series bold
Globin-Blocking
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout

\series bold
Up
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout

\family roman
\series medium
\shape up
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\emph off
\bar no
\strikeout off
\xout off
\uuline off
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\color none
231
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout

\family roman
\series medium
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\color none
515
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" leftline="true" rightline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout

\family roman
\series medium
\shape up
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\emph off
\bar no
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\xout off
\uuline off
\uwave off
\noun off
\color none
2
\end_layout

\end_inset
</cell>
</row>
<row>
<cell multirow="4" alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout

\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout

\series bold
NS
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout

\family roman
\series medium
\shape up
\size normal
\emph off
\bar no
\strikeout off
\xout off
\uuline off
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\noun off
\color none
160
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout

\family roman
\series medium
\shape up
\size normal
\emph off
\bar no
\strikeout off
\xout off
\uuline off
\uwave off
\noun off
\color none
11235
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" leftline="true" rightline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout

\family roman
\series medium
\shape up
\size normal
\emph off
\bar no
\strikeout off
\xout off
\uuline off
\uwave off
\noun off
\color none
136
\end_layout

\end_inset
</cell>
</row>
<row>
<cell multirow="4" alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout

\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout

\series bold
Down
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout

\family roman
\series medium
\shape up
\size normal
\emph off
\bar no
\strikeout off
\xout off
\uuline off
\uwave off
\noun off
\color none
0
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout

\family roman
\series medium
\shape up
\size normal
\emph off
\bar no
\strikeout off
\xout off
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\noun off
\color none
548
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" rightline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout

\family roman
\series medium
\shape up
\size normal
\emph off
\bar no
\strikeout off
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\uuline off
\uwave off
\noun off
\color none
127
\end_layout

\end_inset
</cell>
</row>
</lyxtabular>

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout
\begin_inset Argument 1
status collapsed

\begin_layout Plain Layout
Comparison of significantly differentially expressed genes with and without
 globin blocking.
\end_layout

\end_inset


\begin_inset CommandInset label
LatexCommand label
name "tab:Comparison-of-significant"

\end_inset


\series bold
Comparison of significantly differentially expressed genes with and without
 globin blocking.

\series default
 Up, Down: Genes significantly up/down-regulated in post-transplant samples
 relative to pre-transplant samples, with a false discovery rate of 10%
 or less.
 NS: Non-significant genes (false discovery rate greater than 10%).
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Standard
The key point is that the 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
GB
\end_layout

\end_inset

 data results in substantially more differentially expressed calls than
 the non-GB data.
 Since there is no gold standard for this dataset, it is impossible to be
 certain whether this is due to under-calling of differential expression
 in the non-GB samples or over-calling in the 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
GB
\end_layout

\end_inset

 samples.
 However, given that both datasets are derived from the same biological
 samples and have nearly equal 
\begin_inset Flex Glossary Term (pl)
status open

\begin_layout Plain Layout
BCV
\end_layout

\end_inset

, it is more likely that the larger number of differential expression calls
 in the 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
GB
\end_layout

\end_inset

 samples are genuine detections that were enabled by the higher sequencing
 depth and measurement precision of the 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
GB
\end_layout

\end_inset

 samples.
 Note that the same set of genes was considered in both subsets, so the
 larger number of differentially expressed gene calls in the 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
GB
\end_layout

\end_inset

 data set reflects a greater sensitivity to detect significant differential
 gene expression and not simply the larger total number of detected genes
 in 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
GB
\end_layout

\end_inset

 samples described earlier.
\end_layout

\begin_layout Section
Discussion
\end_layout

\begin_layout Standard
The original experience with whole blood gene expression profiling on DNA
 microarrays demonstrated that the high concentration of globin transcripts
 reduced the sensitivity to detect genes with relatively low expression
 levels, in effect, significantly reducing the sensitivity.
 To address this limitation, commercial protocols for globin reduction were
 developed based on strategies to block globin transcript amplification
 during labeling or physically removing globin transcripts by affinity bead
 methods 
\begin_inset CommandInset citation
LatexCommand cite
key "Winn2010"
literal "false"

\end_inset

.
 More recently, using the latest generation of labeling protocols and arrays,
 it was determined that globin reduction was no longer necessary to obtain
 sufficient sensitivity to detect differential transcript expression 
\begin_inset CommandInset citation
LatexCommand cite
key "NuGEN2010"
literal "false"

\end_inset

.
 However, we are not aware of any publications using these currently available
 protocols with the latest generation of microarrays that actually compare
 the detection sensitivity with and without globin reduction.
 However, in practice this has now been adopted generally primarily driven
 by concerns for cost control.
 The main objective of our work was to directly test the impact of globin
 gene transcripts and a new 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
GB
\end_layout

\end_inset

 protocol for application to the newest generation of differential gene
 expression profiling determined using next generation sequencing.
 
\end_layout

\begin_layout Standard
The challenge of doing global gene expression profiling in cynomolgus monkeys
 is that the current available arrays were never designed to comprehensively
 cover this genome and have not been updated since the first assemblies
 of the cynomolgus genome were published.
 Therefore, we determined that the best strategy for peripheral blood profiling
 was to perform deep 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
RNA-seq
\end_layout

\end_inset

 and inform the workflow using the latest available genome assembly and
 annotation 
\begin_inset CommandInset citation
LatexCommand cite
key "Wilson2013"
literal "false"

\end_inset

.
 However, it was not immediately clear whether globin reduction was necessary
 for 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
RNA-seq
\end_layout

\end_inset

 or how much improvement in efficiency or sensitivity to detect differential
 gene expression would be achieved for the added cost and effort.
 
\end_layout

\begin_layout Standard
Existing strategies for globin reduction involve degradation or physical
 removal of globin transcripts in a separate step prior to reverse transcription
 
\begin_inset CommandInset citation
LatexCommand cite
key "Mastrokolias2012,Choi2014,Shin2014"
literal "false"

\end_inset

.
 This additional step adds significant time, complexity, and cost to sample
 preparation.
 Faced with the need to perform 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
RNA-seq
\end_layout

\end_inset

 on large numbers of blood samples we sought a solution to globin reduction
 that could be achieved purely by adding additional reagents during the
 reverse transcription reaction.
 Furthermore, we needed a globin reduction method specific to cynomolgus
 globin sequences that would work an organism for which no kit is available
 off the shelf.
\end_layout

\begin_layout Standard
As mentioned above, the addition of 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
GB
\end_layout

\end_inset

 
\begin_inset Flex Glossary Term (pl)
status open

\begin_layout Plain Layout
oligo
\end_layout

\end_inset

 has a very small impact on measured expression levels of gene expression.
 However, this is a non-issue for the purposes of differential expression
 testing, since a systematic change in a gene in all samples does not affect
 relative expression levels between samples.
 However, we must acknowledge that simple comparisons of gene expression
 data obtained by 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
GB
\end_layout

\end_inset

 and non-GB protocols are not possible without additional normalization.
 
\end_layout

\begin_layout Standard
More importantly, 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
GB
\end_layout

\end_inset

 not only nearly doubles the yield of usable reads, it also increases inter-samp
le correlation and sensitivity to detect differential gene expression relative
 to the same set of samples profiled without 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
GB
\end_layout

\end_inset

.
 In addition, 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
GB
\end_layout

\end_inset

 does not add a significant amount of random noise to the data.
 
\begin_inset Flex Glossary Term (Capital)
status open

\begin_layout Plain Layout
GB
\end_layout

\end_inset

 thus represents a cost-effective and low-effort way to squeeze more data
 and statistical power out of the same blood samples and the same amount
 of sequencing.
 In conclusion, 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
GB
\end_layout

\end_inset

 greatly increases the yield of useful 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
RNA-seq
\end_layout

\end_inset

 reads mapping to the rest of the genome, with minimal perturbations in
 the relative levels of non-globin genes.
 Based on these results, globin transcript reduction using sequence-specific,
 complementary blocking 
\begin_inset Flex Glossary Term (pl)
status open

\begin_layout Plain Layout
oligo
\end_layout

\end_inset

 is recommended for all deep 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
RNA-seq
\end_layout

\end_inset

 of cynomolgus and other nonhuman primate blood samples.
\end_layout

\begin_layout Section
Future Directions
\end_layout

\begin_layout Standard
One drawback of the 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
GB
\end_layout

\end_inset

 method presented in this analysis is a poor yield of genic reads, only
 around 50%.
 In a separate experiment, the reagent mixture was modified so as to address
 this drawback, resulting in a method that produces an even better reduction
 in globin reads without reducing the overall fraction of genic reads.
 However, the data showing this improvement consists of only a few test
 samples, so the larger data set analyzed above was chosen in order to demonstra
te the effectiveness of the method in reducing globin reads while preserving
 the biological signal.
\end_layout

\begin_layout Standard
The motivation for developing a fast practical way to enrich for non-globin
 reads in cyno blood samples was to enable a large-scale 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
RNA-seq
\end_layout

\end_inset

 experiment investigating the effects of mesenchymal stem cell infusion
 on blood gene expression in cynomologus transplant recipients in a time
 course after transplantation.
 With the 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
GB
\end_layout

\end_inset

 method in place, the way is now clear for this experiment to proceed.
\end_layout

\begin_layout Chapter
\begin_inset CommandInset label
LatexCommand label
name "chap:Conclusions"

\end_inset

Conclusions
\end_layout

\begin_layout Standard
\begin_inset ERT
status collapsed

\begin_layout Plain Layout


\backslash
glsresetall
\end_layout

\end_inset


\begin_inset Note Note
status collapsed

\begin_layout Plain Layout
Reintroduce all abbreviations
\end_layout

\end_inset


\end_layout

\begin_layout Standard
In this work, I have presented a wide range of applications for high-thoughput
 genomic and epigenomic assays based on sequencing and arrays in the context
 of immunology and transplant rejection.
 Chapter 
\begin_inset CommandInset ref
LatexCommand ref
reference "chap:CD4-ChIP-seq"
plural "false"
caps "false"
noprefix "false"

\end_inset

 described the use of 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
RNA-seq
\end_layout

\end_inset

 and 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
ChIP-seq
\end_layout

\end_inset

 to investigate the interplay between promoter histone marks and gene expression
 during activation of naïve and memory CD4
\begin_inset Formula $^{+}$
\end_inset

 T-cells.
 Chapter 
\begin_inset CommandInset ref
LatexCommand ref
reference "chap:Improving-array-based-diagnostic"
plural "false"
caps "false"
noprefix "false"

\end_inset

 explored the use of expression microarrays and methylation arrays for diagnosin
g transplant rejection.
 Chapter 
\begin_inset CommandInset ref
LatexCommand ref
reference "chap:Globin-blocking-cyno"
plural "false"
caps "false"
noprefix "false"

\end_inset

 introduced a new 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
RNA-seq
\end_layout

\end_inset

 protocol for sequencing blood samples from cynomolgus monkeys designed
 to expedite gene expression profiling in serial blood samples from monkeys
 who received an experimental treatment for transplant rejection based on
 
\begin_inset Flex Glossary Term (pl)
status open

\begin_layout Plain Layout
MSC
\end_layout

\end_inset

.
 These applications range from basic science to translational medicine,
 but in all cases, high-thoughput genomic assays were central to the results.
 
\end_layout

\begin_layout Section
Every high-throughput analysis presents unique analysis challenges
\end_layout

\begin_layout Standard
In addition, each of these applications of high-throughput genomic assays
 presented unique analysis challenges that could not be solved simply by
 stringing together standard off-the-shelf methods into a straightforward
 analysis pipeline.
 In every case, a bespoke analysis workflow tailored to the data was required,
 and in no case was it possible to determine every step in the workflow
 fully prior to seeing the data.
 For example, exploratory data analysis of the CD4
\begin_inset Formula $^{+}$
\end_inset

 T-cell 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
RNA-seq
\end_layout

\end_inset

 data uncovered the batch effect, and the analysis was adjusted to compensate
 for it.
 Similarly, analysis of the 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
ChIP-seq
\end_layout

\end_inset

 data required choosing an 
\begin_inset Quotes eld
\end_inset

effective promoter radius
\begin_inset Quotes erd
\end_inset

 based on the data itself, and several different peak callers were tested
 before the correct choice became clear.
 In the development of custom 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
fRMA
\end_layout

\end_inset

 vectors, an appropriate batch size had to be chosen based on the properties
 of the training data.
 In the analysis of methylation array data, the appropriate analysis strategy
 was not obvious and was determined by trying several plausible strategies
 and inspecting the model paramters afterward to determine which strategy
 appeared to best capture the observed properties of the data and which
 strategies appeared to have systematic errors as a result of failing to
 capture those properties.
 The 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
GB
\end_layout

\end_inset

 protocol went through several rounds of testing before satisfactory performance
 was achieved, and as mentioned, optimization of the protocol has continued
 past the version described here.
 These are only a few examples out of many instances of analysis decisions
 motivated by the properties of the data.
\end_layout

\begin_layout Section
Successful data analysis requires a toolbox, not a pipeline
\end_layout

\begin_layout Standard
Multiple times throughout this work, I have attempted to construct standard,
 reusable, pipelines for analysis of specific kinds of data, such as 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
RNA-seq
\end_layout

\end_inset

 or 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
ChIP-seq
\end_layout

\end_inset

.
 Each time, the very next data set containing this data broke one or more
 of the assumptions I had built into the pipeline, such as an RNA-seq dataset
 where some samples aligned to the sense strand while others aligned to
 the antisense strand, or the discovery that the effective promoter radius
 varies by histone mark.
 Each violation of an assumption required a significant rewrite of the pipeline'
s code in order to accommodate the new aspect of the data.
 The prospect of reusability turned out to be a pipe(line) dream.
 After several attempts to extend my pipelines to be general enough to handle
 an ever-increasing variety of data idiosyncrasies, I realized that it was
 actually 
\emph on
less
\emph default
 work to reimplement an analysis workflow from scratch each time rather
 than try to adapt an existing workflow that was originally designed for
 a different data set.
\end_layout

\begin_layout Standard
Once I embraced the idea of writing a bespoke analysis workflow for every
 data set instead of a one-size-fits-all pipeline, I stopped thinking of
 the pipeline as the atomic unit of analysis.
 Instead, I focused on developing an understanding of the component parts
 of each pipeline, which problems each part solves, and what assumptions
 it makes, so that when I was presented with a new data set, I could quickly
 select the appropriate analysis methods for that data set and compose them
 into a new workflow to answer the demands of a new data set.
 In cases where no off-the-shelf method existed to address a specific aspect
 of the data, knowing about a wide range of analysis methods allowed me
 to select the one that was closest to what I needed and adapt it accordingly,
 even if it was not originally designed to handle the kind of data I was
 analyzing.
 For example, when analyzing heteroskedastic methylation array data, I adapted
 the 
\begin_inset Flex Code
status open

\begin_layout Plain Layout
voom
\end_layout

\end_inset

 method from 
\begin_inset Flex Code
status open

\begin_layout Plain Layout
limma
\end_layout

\end_inset

, which was originally designed to model heteroskedasticity in 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
RNA-seq
\end_layout

\end_inset

data 
\begin_inset CommandInset citation
LatexCommand cite
key "Law2014"
literal "false"

\end_inset

.
 While 
\begin_inset Flex Code
status open

\begin_layout Plain Layout
voom
\end_layout

\end_inset

 was designed to accept read counts, I determined that this was not a fundamenta
l assumption of the method but rather a limitation of the specific implementatio
n, and I was able to craft a modified implementation that accepted 
\begin_inset Flex Glossary Term (pl)
status open

\begin_layout Plain Layout
M-value
\end_layout

\end_inset

 from methylation arrays.
 In contrast, adapting another method such as 
\begin_inset Flex Code
status open

\begin_layout Plain Layout
edgeR
\end_layout

\end_inset

 for methylation arrays would not be possible, since many steps of the 
\begin_inset Flex Code
status open

\begin_layout Plain Layout
edgeR
\end_layout

\end_inset

 workflow, from normalization to dispersion estimation to model fitting,
 assume that the input is given on the scale of raw counts and take full
 advantage of this assumption 
\begin_inset CommandInset citation
LatexCommand cite
key "Robinson2010,Robinson2010a,McCarthy2012,Chen2014"
literal "false"

\end_inset

.
 In short, I collected a 
\begin_inset Quotes eld
\end_inset

toolbox
\begin_inset Quotes erd
\end_inset

 full of useful modular analysis methods and developed the knowledge of
 when and where each could be applied, as well as how to compose them on
 demand into pipelines for specific data sets.
 This prepared me to handle the idiosyncrasies of any new data set, even
 when the new data has problems that I have not previously encountered in
 any other data set.
 
\end_layout

\begin_layout Standard
Reusable pipelines have their place, but that place is in automating established
 processes, not researching new science.
 For example, the custom 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
fRMA
\end_layout

\end_inset

 vectors developed in Chapter 
\begin_inset CommandInset ref
LatexCommand ref
reference "chap:Improving-array-based-diagnostic"
plural "false"
caps "false"
noprefix "false"

\end_inset

, are being incorporated into an automated pipeline for diagnosing transplant
 rejection using biopsy and blood samples from transplant recipients.
 Once ready, this diagnostic method will consist of normalization using
 the pre-trained 
\begin_inset Flex Glossary Term
status open

\begin_layout Plain Layout
fRMA
\end_layout

\end_inset

 vectors, followed by classification of the sample by a pre-trained classifier,
 which outputs a posterior probability of acute rejection.
 This is a perfect use case for a proper pipeline: repeating the exact same
 sequence of analysis steps many times.
 The input to the pipeline is sufficiently well-controlled that we can guarantee
 it will satisfy the assumptions of the pipeline.
 But research data is not so well-controlled, so when analyzing data in
 a research context, the analysis must conform to the data, rather than
 trying to force the data to conform to a preferred analysis strategy.
 That means having a toolbox full of composable methods ready to respond
 to the observed properties of the data.
\end_layout

\begin_layout Standard
\align center
\begin_inset ERT
status collapsed

\begin_layout Plain Layout

% Use "References" as the title of the Bibliography
\end_layout

\begin_layout Plain Layout


\backslash
renewcommand{
\backslash
bibname}{References}
\end_layout

\end_inset


\end_layout

\begin_layout Standard
\begin_inset CommandInset bibtex
LatexCommand bibtex
btprint "btPrintCited"
bibfiles "code-refs,refs-PROCESSED"
options "bibtotoc"

\end_inset


\end_layout

\begin_layout Standard
\begin_inset Note Note
status open

\begin_layout Plain Layout
How to include other PDFs: https://tex.stackexchange.com/a/28323/5654
\end_layout

\end_inset


\end_layout

\begin_layout Standard
\begin_inset Flex TODO Note (inline)
status open

\begin_layout Plain Layout
Appendix: Publications?
\end_layout

\end_inset


\end_layout

\begin_layout Standard
\begin_inset Flex TODO Note (inline)
status open

\begin_layout Plain Layout
Curriculum vitae
\end_layout

\end_inset


\end_layout

\end_body
\end_document