Thesis/thesis.lyx

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\begin_body

\begin_layout Title
Bioinformatic analysis of complex, high-throughput genomic and epigenomic
 data in the context of immunology and transplant rejection
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\begin_layout Author
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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\begin_layout Standard
[Copyright notice]
\end_layout

\begin_layout Standard
[Thesis acceptance form]
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\begin_layout Standard
[Dedication]
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\begin_layout Standard
[Acknowledgements]
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I'm looking for feedback on: Section titles; figure formatting; figure legends;
 typographical errors; ...
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[List of Abbreviations]
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Search and replace: naive -> naïve
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Look into auto-generated nomenclature list: https://wiki.lyx.org/Tips/Nomenclature.
 Otherwise, do a manual pass for all abbreviations at the end.
 Do nomenclature/abbreviations independently for each chapter.
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Make all descriptions consistent in terms of 
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we did X
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I did X
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X was done
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\begin_layout Chapter*
Abstract
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It is included as an integral part of the thesis and should immediately
 precede the introduction.
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\begin_layout Plain Layout
Preparing your Abstract.
 Your abstract (a succinct description of your work) is limited to 350 words.
 UMI will shorten it if they must; please do not exceed the limit.
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\begin_layout Itemize
Include pertinent place names, names of persons (in full), and other proper
 nouns.
 These are useful in automated retrieval.
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\begin_layout Itemize
Display symbols, as well as foreign words and phrases, clearly and accurately.
 Include transliterations for characters other than Roman and Greek letters
 and Arabic numerals.
 Include accents and diacritical marks.
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Do not include graphs, charts, tables, or illustrations in your abstract.
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Obviously the abstract gets written last.
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\begin_layout Chapter
Introduction
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\begin_layout Section
Background & Significance
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\begin_layout Subsection
Biological motivation
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Rethink the subsection organization after the intro is written.
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Citations are needed all over the place.
 A lot of this is knowledge I've just absorbed from years of conversation
 in the Salomon lab, without ever having seen a citation for it.
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\begin_layout Subsubsection
Rejection is the major long-term threat to organ and tissue allografts
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\begin_layout Standard
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 – the donor – is required.
 This is referred to as an allograft.
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Possible citation for degree of generic variability: https://www.ncbi.nlm.nih.gov/pu
bmed/22424236?dopt=Abstract
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How much mechanistic detail is needed here? My work doesn't really go into
 specific rejection mechanisms, so I think it's best to keep it basic.
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\begin_layout Standard
Because an allograft comes from a different person, it is genetically distinct
 from the rest of the recipient's body.
 Some genetic variants occur in protein coding regions, resulting in protein
 products that differ from the equivalent proteins in 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, eventually
 resulting in failure and death of the graft, a process referred to as transplan
t rejection.
 Rejection is the most significant challenge to the long-term health of
 an allograft.
 Like any adaptive immune response, graft rejection generally occurs via
 two broad mechanisms: cellular immunity, in which CD8+ T-cells 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.
 In either case, rejection shows most of the typical hallmarks of an adaptive
 immune response, in particular mediation by CD4+ T-cells and formation
 of immune memory.
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\begin_layout Subsubsection
Diagnosis and treatment of allograft rejection is a major challenge
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\begin_layout Standard
To prevent rejection, allograft recipients are treated with immune suppression.
 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; exceissive suppression
 leaves the patient vulnerable to life-threatening opportunistic infections.
 Because every patient is different, immune suppression must be tailored
 for each patient.
 Furthermore, immune suppression must be tuned over time, as the immune
 system's activity is not static, nor is it held in a steady state.
 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 is observed.
\end_layout

\begin_layout Standard
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.
 The current gold standard test for graft rejection is a tissue biopsy,
 examained for visible signs of rejection by a trained histologist.
 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 
\begin_inset CommandInset citation
LatexCommand cite
key "Wilkinson2006"
literal "false"

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.
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\begin_layout Standard
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.
 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 only occurring in one section of the graft and the tissue
 is extracted from a different section, it may result in a false negative
 diagnosis.
 Most importantly, however, 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 the can safely be performed.
 Typically protocol biopsies are not scheduled more than about once per
 month 
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LatexCommand cite
key "Wilkinson2006"
literal "false"

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.
 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, more frequent tests would be a boon to the transplant research
 community.
 Beyond simply providing more data, the increased 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 Subsubsection
Memory cells are resistant to immune suppression
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\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.
 When the immune system first encounters a new antigen, the lymphocytes
 that respond are known as naive cells – T-cells and B-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, naive cells differentiate into
 effector cells that carry out their respective functions in targeting and
 destroying the source of the foreign antigen.
 The requirement for co-stimulation is an important feature of naive cells
 that limits 
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false positive
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 immune responses, because antigen-presenting cells usually only express
 the proper co-stimulation after detecting evidence of an infection, such
 as the presence of common bacterial cell components or inflamed tissue.
 Most effector cells die after the foreign antigen is cleared, but some
 remain and differentiate into memory cells.
 Like naive cells, memory cells respond to detection of their specific antigen
 by differentiating into effector cells, ready to fight an infection.
 However, unlike naive cells, memory cells do not require the same degree
 of co-stimulatory signaling for activation, and once activated, they proliferat
e and differentiate into effector cells more quickly than naive cells do.
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\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.
 However, if effector cells that recognize an antigen from an allograft
 are allowed to differentiate into memory cells, suppressing rejection of
 the graft becomes much more difficult.
 Many immune suppression drugs work by interfering with the co-stimulation
 that naive cells require in order to mount an immune response.
 Since memory cells do not require this 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 they require stronger immune
 suppression than naive cells to prevent an immune response.
 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.
 While the differences in cell surface markers between naive 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 immune suppression that either prevents the formation
 of memory cells or works more effectively against memory cells, the mechanisms
 of immune memory formation and regulation must be better understood.
\end_layout

\begin_layout Subsection
Overview of bioinformatic analysis methods
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\begin_layout Standard
The studies presented in this work all involve the analysis of high-throughput
 genomic and epigenomic data.
 These data present many unique analysis challenges, and a wide array of
 software tools are available to analyze them.
 This section presents an overview of the methods used, including what problems
 they solve, what assumptions they make, and a basic description of how
 they work.
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Many of these points may also be addressed in the approach/methods sections
 of the following chapters? Redundant?
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\begin_layout Subsubsection
Limma: The standard linear modeling framework for genomics
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\begin_layout Standard
Linear models are a generalization of the 
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-test and ANOVA to arbitrarily complex experimental designs.
 In a typical linear model, there is one dependent variable observation
 per sample.
 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 an RNA-seq experiment, the dependent variables may be the
 count of RNA-seq reads for each annotated gene.
 In abstract terms, each dependent variable being measured is referred to
 as a feature.
 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 high-throughput sequencing are expensive, and often
 generating the samples is also quite expensive and time-consuming.
 This expense limits the sample sizes typically employed in genomics experiments
, and as a result the statistical power of each individual feature's linear
 model is likewise limited.
 However, because thousands of features from the same 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 limma, a linear modeling framework designed
 for genomic data.
 Limma is typically used to analyze expression microarray data, and more
 recently RNA-seq data, but it can also be used to analyze any other data
 for which linear modeling is appropriate.
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\begin_layout Standard
The central challenge when fitting a linear model is to estimate the variance
 of the data accurately.
 This quantity 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 every feature is equally
 variable, which is known to be false for most genomic data sets.
 Limma offers a compromise between these two extremes by using a method
 called empirical Bayes moderation to 
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squeeze
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 the distribution of estimated variances toward a single common value that
 represents the variance of an average feature in the data 
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LatexCommand cite
key "Smyth2004"
literal "false"

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.
 While the individual feature variance estimates are not stable, the common
 variance estiamate for the entire data set is quite stable, so using a
 combination of the two yields a variance estimate for each feature with
 greater precision than the individual feature varaiances.
 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, limma assumes that extreme variances are less common than
 variances close to the common value.
 The variance estimates from this empirical Bayes procedure are shown empiricall
y to yield greater statistical power than either the individual feature
 variances or the single common value.
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\begin_layout Standard
On top of this core framework, limma 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, limma can model
 the common variance as a function of a covariate, such as average expression
 
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LatexCommand cite
key "Law2013"
literal "false"

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.
 This is essential for RNA-seq 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,
 limma is able to relax this assumption by identifying and down-weighting
 samples the diverge more strongly from the lienar model across many features
 
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LatexCommand cite
key "Ritchie2006,Liu2015"
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.
 In addition, limma is also able to fit simple mixed models incorporating
 one random effect in addition to the fixed effects represented by an ordinary
 linear model 
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LatexCommand cite
key "Smyth2005a"
literal "false"

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.
 Once again, limma shares information between features to obtain a robust
 estimate for the random effect correlation.
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\begin_layout Subsubsection
edgeR provides limma-like analysis features for count data
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\begin_layout Standard
Although limma can be applied to read counts from RNA-seq data, it is less
 suitable for counts from ChIP-seq data, which tend to be much smaller and
 therefore violate the assumption of a normal distribution more severely.
 For all count-based data, the edgeR package works similarly to limma, but
 uses a generalized linear model instead of a linear model.
 The most important difference is that the GLM in edgeR models the counts
 directly using a negative binomial distribution rather than modeling the
 normalized log counts using a normal distribution 
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LatexCommand cite
key "Chen2014,McCarthy2012,Robinson2010a"
literal "false"

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.
 The negative binomial is a good fit for count data because it can be derived
 as a gamma-distributed mixture of Poisson distributions.
 The Poisson distribution accurately represents the distribution of counts
 expected for a given gene abundance, and the gamma distribution is then
 used to represent the variation in gene abundance between biological replicates.
 For this reason, the square root of the dispersion paramter of the negative
 binomial is sometimes referred to as the biological coefficient of variation,
 since it represents the variability that was present in the samples prior
 to the Poisson 
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noise
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 that was generated by the random sampling of reads in proportion to feature
 abundances.
 The choice of a gamma distribution is arbitrary and motivated by mathematical
 convenience, since a gamma-Poisson mixture yields the numerically tractable
 negative binomial distribution.
 Thus, edgeR assumes 
\emph on
a prioi 
\emph default
that the variation in abundances between replicates follows a gamma distribution.
 For differential abundance testing, edgeR offers a likelihood ratio test,
 but more recently recommends a quasi-likelihood test that properly factors
 the uncertainty in variance estimation into the statistical significance
 for each feature 
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LatexCommand cite
key "Lund2012"
literal "false"

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.
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\begin_layout Subsubsection
ChIP-seq Peak calling
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\begin_layout Standard
Unlike RNA-seq data, in which gene annotations provide a well-defined set
 of genomic regions in which to count reads, ChIP-seq data can potentially
 occur anywhere in the genome.
 However, most genome regions will not contain significant ChIP-seq read
 coverage, and analyzing every position in the entire genome is statistically
 and computationally infeasible, so it is necesary to identify regions of
 interest inside which ChIP-seq 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 ChIP-seq data itself to identify
 regions with ChIP-seq read coverage significantly above the background
 level, known as peaks.
 
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\begin_layout Standard
There are generally two kinds of peaks that can be identified: narrow peaks
 and broadly enriched regions.
 Proteins like transcription factors that bind specific sites in the genome
 typically show most of their 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 size, typically tens to hundreds of base pairs.
 Algorithms like MACS exploit this pattern to identify specific loci at
 which such 
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narrow peaks
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\end_inset

 occur by looking for the characteristic peak shape in the ChIP-seq 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 potentailly 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 ChIP-seq 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
 SICER assume that peaks are represented in the ChIP-seq 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

.
 In all cases, better results are obtained if the local background coverage
 level can be estimated from ChIP-seq input samples, since various biases
 can result in uneven background coverage.
\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 ENCODE project has developed a method called irreproducible discovery
 rate for this purpose 
\begin_inset CommandInset citation
LatexCommand cite
key "Li2006"
literal "false"

\end_inset

.
 The IDR 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 true list of significant
 features, IDR instead measures the degree of correspondence between two
 ranked lists derived from different data.
 IDR 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.
 IDR 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 for down the list the
 correspondence between feature ranks breaks down.
\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 csaw package provides guidelines for calling peaks in this way: peaks
 are called based on a combination of all ChIP-seq 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 condtions 
\begin_inset CommandInset citation
LatexCommand cite
key "Lun2015a"
literal "false"

\end_inset

.
\end_layout

\begin_layout Subsubsection
Normalization of high-throughput data is non-trivial and application-dependant
\end_layout

\begin_layout Standard
High-throughput data sets invariable 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 Robust Multichip Average (RMA).
 RMA 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 represented by multiple probes by implementing normalization and summarizati
on steps that are robust against outlier probes.
 However, RMA 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, frozen RMA implements a variant of RMA 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.
\end_layout

\begin_layout Standard
In contrast, high-throughput sequencing data present very different normalizatio
n challenges.
 The simplest case is RNA-seq 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 RNA-seq 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, RNA-seq abundances are often reported as counts per million
 (CPM).
 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 accomodate 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 RNA-seq
 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

.
\end_layout

\begin_layout Standard
In ChIP-seq data, normalization is not as straightforward.
 The csaw 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 ChIP-seq 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 potential normalization targets: equalizing background
 coverage or equalizing signal coverage.
 If the experiment is well controlled and ChIP 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 RNA-seq data by assuming that the
 average signal region is not changing abundance between samples.
 Beyond this, if a ChIP-seq 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 log fold change is zero across all abundance levels.
 Hence, the simpler scaling normalziations based on background or signal
 regions are generally preferred whenever possible.
\end_layout

\begin_layout Subsubsection
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 limma'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 subtraction.
\end_layout

\begin_layout Standard
In some data sets, unknown batch effects may be present due to inherent
 variability in in the data, either caused by technical or biological effects.
 Examples of unknown batch effects include variations in enrichment efficiency
 between ChIP-seq 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 singular value decomposition
 (SVD) on the matrix os 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 uncorrelated with any of the effects being modeled.
 Surrogate variable analysis (SVA) 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 SVA much more freedom to estimate the true extent of the batch
 effects compared to simple residual SVD.
 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 Subsubsection
Factor analysis: PCA, MDS, MOFA
\end_layout

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

\begin_layout Section
Innovation
\end_layout

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

\begin_layout Plain Layout
Is this entire section redundant with the Approach sections of each chapter?
 I'm not really sure what to write here.
\end_layout

\end_inset


\end_layout

\begin_layout Subsection
MSC infusion to improve transplant outcomes (prevent/delay rejection)
\end_layout

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

\begin_layout Plain Layout
Do I still talk about this? It's the motivation for chapter 4, but I don't
 actually present any work related to MSCs.
\end_layout

\end_inset


\end_layout

\begin_layout Itemize
Demonstrated in mice, but not yet in primates
\end_layout

\begin_layout Itemize
Mechanism currently unknown, but MSC are known to be immune modulatory
\end_layout

\begin_layout Itemize
Characterize MSC response to interferon gamma
\end_layout

\begin_layout Itemize
IFN-g is thought to stimulate their function
\end_layout

\begin_layout Itemize
Test IFN-g treated MSC infusion as a therapy to delay graft rejection in
 cynomolgus monkeys
\end_layout

\begin_layout Itemize
Monitor animals post-transplant using blood RNA-seq at serial time points
\end_layout

\begin_layout Subsection
Investigate dynamics of histone marks in CD4 T-cell activation and memory
\end_layout

\begin_layout Itemize
Previous studies have looked at single snapshots of histone marks
\end_layout

\begin_layout Itemize
Instead, look at changes in histone marks across activation and memory
\end_layout

\begin_layout Subsection
High-throughput sequencing and microarray technologies
\end_layout

\begin_layout Itemize
Powerful methods for assaying gene expression and epigenetics across entire
 genomes
\end_layout

\begin_layout Itemize
Proper analysis requires finding and exploiting systematic genome-wide trends
\end_layout

\begin_layout Chapter
Reproducible genome-wide epigenetic analysis of H3K4 and H3K27 methylation
 in naive and memory CD4 T-cell activation
\end_layout

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

\begin_layout Plain Layout
Chapter author list: Me, Sarah, Dan
\end_layout

\end_inset


\end_layout

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

\begin_layout Plain Layout
Need better section titles throughout the entire chapter
\end_layout

\end_inset


\end_layout

\begin_layout Section
Approach
\end_layout

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

\begin_layout Plain Layout
Check on the exact correct way to write 
\begin_inset Quotes eld
\end_inset

CD4 T-cell
\begin_inset Quotes erd
\end_inset

.
 I think there might be a plus sign somwehere in there now? Also, maybe
 figure out a reasonable way to abbreviate 
\begin_inset Quotes eld
\end_inset

naive CD4 T-cells
\begin_inset Quotes erd
\end_inset

 and 
\begin_inset Quotes eld
\end_inset

memory CD4 T-cells
\begin_inset Quotes erd
\end_inset

.
\end_layout

\end_inset


\end_layout

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

\begin_layout Plain Layout
Is it ok to just copy a bunch of citations from the intros to Sarah's papers?
 That feels like cheating somehow.
\end_layout

\end_inset


\end_layout

\begin_layout Standard
CD4 T-cells are central to all adaptive immune responses, as well as immune
 memory [CITE?].
 After an infection is cleared, a subset of the naive CD4 T-cells that responded
 to that infection differentiate into memory CD4 T-cells, which are responsible
 for responding to the same pathogen in the future.
 Memory CD4 T-cells are functionally distinct, able to respond to an infection
 more quickly and without the co-stimulation requried by naive CD4 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 naive 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 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 Standard
In order to investigate the relationship between gene expression and these
 histone modifications in the context of naive and memory CD4 T-cell activation,
 a previously published data set of combined RNA-seq and ChIP-seq 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 naive and memory CD4 T-cell samples in a time course
 before and after activation.
 Like the original analysis, this analysis looks at the dynamics of these
 marks histone marks and compare them to gene expression dynamics at the
 same time points during activation, as well as comapre them between naive
 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 monolithinc
 unit and mostly assumed that ChIP-seq reads or peaks occuring 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
 ChIP-seq peaks for differential modification, and second by taking a mor
 granular look at the ChIP-seq read coverage within promoter regions to
 ask whether the location of histone modifications relative to the gene's
 TSS is an important factor, as opposed to simple proximity.
\end_layout

\begin_layout Section
Methods
\end_layout

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

\begin_layout Plain Layout
Look up some more details from the papers (e.g.
 activation method).
\end_layout

\end_inset


\end_layout

\begin_layout Standard
A reproducible workflow was written to analyze the raw ChIP-seq and RNA-seq
 data from previous studies 
\begin_inset CommandInset citation
LatexCommand cite
key "gh-cd4-csaw,LaMere2016,LaMere2017"
literal "true"

\end_inset

.
 Briefly, this data consists of RNA-seq and ChIP-seq from CD4 T-cells cultured
 from 4 donors.
 From each donor, naive and memory CD4 T-cells were isolated separately.
 Then cultures of both cells were activated [how?], 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 ChIP-seq was performed for each of 3 histone marks: H3K4me2,
 H3K4me3, and H3K27me3.
 The ChIP-seq 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
Salomn 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 Sequence Read Archive (SRA) 
\begin_inset CommandInset citation
LatexCommand cite
key "Leinonen2011"
literal "false"

\end_inset

.
 Five different alignment and quantification methods were tested for the
 RNA-seq 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 UCSC known
 gene annotations [CITE? Also which versions of each?].
 Comparisons of downstream results from each combination of quantification
 method and reference revealed that all quantifications gave broadly similar
 results for most genes, so shoal with the Ensembl annotation was chosen
 as the method theoretically most likely to partially mitigate some of the
 batch effect in the data.
\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

\series bold
\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

\series bold
\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

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

\end_inset

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

\end_inset


\end_layout

\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 counfounding 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
\begin_inset Flex TODO Note (inline)
status open

\begin_layout Plain Layout
Just take the top row
\end_layout

\end_inset


\end_layout

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename graphics/CD4-csaw/RNA-seq/weights-vs-covars-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:RNA-seq-weights-vs-covars"

\end_inset

RNA-seq sample weights, grouped by experimental and technical covariates.
\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 limma's sample weighting method
 to assign lower weights to the noisy samples of batch 1 
\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 unfortuantely means a loss of statistical power
 for comparisons involving samples in batch 1.
\end_layout

\begin_layout Standard
In any case, the RNA-seq counts were first normalized using trimmed mean
 of M-values 
\begin_inset CommandInset citation
LatexCommand cite
key "Robinson2010"
literal "false"

\end_inset

, converted to normalized logCPM with quality weights using voomWithQualityWeigh
ts 
\begin_inset CommandInset citation
LatexCommand cite
key "Law2013,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 limma, and each gene was tested for differential expression
 using limma's empirical Bayes moderated 
\begin_inset Formula $t$
\end_inset

-test 
\begin_inset CommandInset citation
LatexCommand cite
key "Smyth2005,Law2013,Phipson2013"
literal "false"

\end_inset

.
\end_layout

\begin_layout Subsection
ChIP-seq differential modification analysis
\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 50
	height 40theight%
	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


\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/csaw/CCF-plots-PAGE2-CROP.pdf
	lyxscale 50
	height 40theight%
	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 Caption Standard

\begin_layout Plain Layout

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

\end_inset

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

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Standard
\begin_inset Note Note
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/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

\series bold
\begin_inset CommandInset label
LatexCommand label
name "fig:MA-plot-bigbins"

\end_inset

MA plot of H3K4me2 read counts in 10kb bins for two arbitrary samples.
\end_layout

\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
Be consistent about use of 
\begin_inset Quotes eld
\end_inset

differential binding
\begin_inset Quotes erd
\end_inset

 vs 
\begin_inset Quotes eld
\end_inset

differential modification
\begin_inset Quotes erd
\end_inset

 throughout this chapter.
 The latter is usually preferred.
\end_layout

\end_inset


\end_layout

\begin_layout Standard
Sequence reads were retrieved from SRA 
\begin_inset CommandInset citation
LatexCommand cite
key "Leinonen2011"
literal "false"

\end_inset

.
 ChIP-seq (and input) reads were aligned to GRCh38 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 GreyListCh
IP algorithm, and these 
\begin_inset Quotes eld
\end_inset

greylists
\begin_inset Quotes erd
\end_inset

 were merged with the published ENCODE blacklists 
\begin_inset CommandInset citation
LatexCommand cite
key "greylistchip,Amemiya2019,Dunham2012,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 ChIP-seq data.
 Peaks were called using epic, an implementation of the SICER algorithm
 
\begin_inset CommandInset citation
LatexCommand cite
key "Zang2009,gh-epic"
literal "false"

\end_inset

.
 Peaks were also called separately using MACS, but MACS 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 irreproducible discovery
 rate (IDR) framework 
\begin_inset CommandInset citation
LatexCommand cite
key "Li2006,gh-idr"
literal "false"

\end_inset

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

\begin_layout Standard
Promoters were defined by computing the distance from each annotated TSS
 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 TSS.
 For H3K4me2 and H3K4me3, this distance was about 1
\begin_inset space ~
\end_inset

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

kb.
 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 TSS.
 For genes with multiple annotated TSSs, a promoter region was defined for
 each TSS individually, and any promoters that overlapped (due to multiple
 TSSs 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
\begin_inset Float figure
wide false
sideways false
status collapsed

\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/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 collapsed

\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 Caption Standard

\begin_layout Plain Layout

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

\end_inset

PCoA plots of ChIP-seq sliding window data, before and after subtracting
 surrogate variables (SVs).
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Standard
Reads in promoters, peaks, and sliding windows across the genome were counted
 and normalized using csaw and analyzed for differential modification using
 edgeR 
\begin_inset CommandInset citation
LatexCommand cite
key "Lun2014,Lun2015a,Lund2012,Phipson2016"
literal "false"

\end_inset

.
 Unobserved confounding factors in the ChIP-seq data were corrected using
 SVA 
\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
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 TSS: one
 window centered on the TSS itself, and 10 windows each upstream and downstream,
 thus covering a 10.5-kb region centered on the TSS with 21 windows.
 Reads in each window for each TSS were counted in each sample, and the
 counts were normalized and converted to log CPM as in the differential
 modification analysis.
 Then, the logCPM values 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 Subsection
MOFA recovers biologically relevant variation from blind analysis by correlating
 across datasets
\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 open

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename graphics/CD4-csaw/MOFA-varExplaiend-matrix-CROP.png
	lyxscale 25
	width 45col%
	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 latent factor (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 open

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename graphics/CD4-csaw/MOFA-LF-scatter-CROP.png
	lyxscale 25
	width 45col%
	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
 are plotted against each other in order to reveal patterns of variation
 that are shared across all data sets.
\end_layout

\end_inset


\end_layout

\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-master"

\end_inset

MOFA latent factors separate technical confounders from 
\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
MOFA was run on all the ChIP-seq windows overlapping consensus peaks for
 each histone mark, as well as the RNA-seq 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

.
 Latent factors 1, 4, and 5 were determined to explain the most variation
 consistently across all data sets (Fgure 
\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

).
 Latent factor 2 captures the batch effect in the RNA-seq data.
 Removing the effect of LF2 using MOFA 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 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 Standard
\begin_inset Flex TODO Note (inline)
status open

\begin_layout Plain Layout
Focus on what hypotheses were tested, then select figures that show how
 those hypotheses were tested, even if the result is a negative.
 Not every interesting result needs to be in here.
 Chapter should tell a story.
 
\end_layout

\end_inset


\end_layout

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

\begin_layout Plain Layout
Maybe reorder these sections to do RNA-seq, then ChIP-seq, then combined
 analyses?
\end_layout

\end_inset


\end_layout

\begin_layout Subsection
Interpretation of RNA-seq analysis is limited by a major confounding factor
\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="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
Naive Day 0 vs 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
5992
\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
1613
\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
Naive Day 0 vs 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
3038
\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
32
\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
Naive Day 0 vs Day 14
\end_layout

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

\begin_layout Plain Layout
1870
\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
190
\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
Memory Day 0 vs 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
3195
\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
411
\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
Memory Day 0 vs 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
2688
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" leftline="true" rightline="true" usebox="none">
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\begin_layout Plain Layout
18
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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
Memory Day 0 vs Day 14
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</cell>
<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
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\begin_layout Plain Layout
1911
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<cell alignment="center" valignment="top" topline="true" leftline="true" rightline="true" usebox="none">
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227
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</cell>
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<row>
<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
Day 0 Naive vs Memory
\end_layout

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</cell>
<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
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\begin_layout Plain Layout
0
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</row>
<row>
<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
Day 1 Naive 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
9167
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</cell>
<cell alignment="center" valignment="top" topline="true" leftline="true" rightline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
5532
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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
Day 5 Naive 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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\begin_inset Text

\begin_layout Plain Layout
Day 14 Naive vs Memory
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6446
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</row>
</lyxtabular>

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\end_layout

\begin_layout Plain Layout
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\begin_layout Plain Layout

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

\end_inset

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


\end_layout

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wide false
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status collapsed

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\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

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

\end_inset

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 with of 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

\begin_layout Plain Layout

\end_layout

\end_inset


\end_layout

\begin_layout Standard
Genes called present in the RNA-seq 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 RNA-seq 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.
 
\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 naive 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 MOFA latent factor 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 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
14965
\end_layout

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

\begin_layout Plain Layout
3970
\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
6163
\end_layout

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

\begin_layout Plain Layout
2946
\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
18139
\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
18967
\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

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

\end_inset

Peak-calling summary.
 
\series default
For each histone mark, the number of peaks called using SICER at an IDR
 threshold of ???, 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 [CITATION NEEDED], 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
\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
Ensure this figure uses the peak calls from the new analysis.
\end_layout

\end_inset


\end_layout

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

\begin_layout Plain Layout
Need a control: shuffle all peaks and repeat, N times.
 Do real vs shuffled control both in a top/bottom arrangement.
\end_layout

\end_inset


\end_layout

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

\begin_layout Plain Layout
Consider counting TSS inside peaks as negative number indicating how far
 
\emph on
inside
\emph default
 the peak the TSS is (i.e.
 distance to nearest non-peak area).
\end_layout

\end_inset


\end_layout

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

\begin_layout Plain Layout
The H3K4 part of this figure is included in 
\begin_inset CommandInset citation
LatexCommand cite
key "LaMere2016"
literal "false"

\end_inset

 as Fig.
 S2.
 Do I need to do anything about that?
\end_layout

\end_inset


\end_layout

\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 Caption Standard

\begin_layout Plain Layout

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

\end_inset

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 [CITE? see ggplot2
 stat_density()].
 Transcription start sites 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.
\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 kb
\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 kb
\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 kb
\end_layout

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

\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 "tab:effective-promoter-radius"

\end_inset

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

\begin_layout Plain Layout

\end_layout

\end_inset


\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 transcription start site (TSS) 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 TSS 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 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
H3K4 and H3K27 promoter methylation has broadly the expected correlation
 with gene expression
\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
This figure is generated from the old analysis.
 Eiher note that in some way or re-generate it from the new peak calls.
\end_layout

\end_inset


\end_layout

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename graphics/CD4-csaw/FPKM by Peak Violin Plots-CROP.pdf
	lyxscale 50
	width 100col%

\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:fpkm-by-peak"

\end_inset

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

\end_inset


\end_layout

\end_inset


\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 [CITE].
 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 $p\mathrm{-values}\ll2.2\times10^{-16}$
\end_inset

).
 The difference in average log FPKM 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 Flex TODO Note (inline)
status open

\begin_layout Plain Layout
I also have some figures looking at interactions between marks (e.g.
 what if a promoter has both H3K4me3 and H3K27me3), but I don't know if
 that much detail is warranted here, since all the effects just seem approximate
ly additive anyway.
\end_layout

\end_inset


\end_layout

\begin_layout Subsection
Gene expression and promoter histone methylation patterns in naive and memory
 show convergence at day 14
\end_layout

\begin_layout Standard
\begin_inset ERT
status open

\begin_layout Plain Layout


\backslash
afterpage{
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\backslash
begin{landscape}
\end_layout

\end_inset


\end_layout

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

\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">
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<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

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</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

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</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

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</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">
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Day 1
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Day 14
\end_layout

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</cell>
</row>
</lyxtabular>

\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 "tab:Number-signif-promoters"

\end_inset

Number of differentially modified promoters between naive 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 as estimated using the method of 
\begin_inset CommandInset citation
LatexCommand cite
key "Phipson2013"
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 Standard
\begin_inset Float figure
placement p
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/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

\series bold
\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

\series bold
\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 collapsed

\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

\series bold
\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

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

\end_inset

RNA-seq PCoA showing principal coordiantes 2 and 3.
\end_layout

\end_inset


\end_layout

\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-promoters"

\end_inset

PCoA plots for promoter ChIP-seq and expression RNA-seq data
\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
Check up on figure refs in this paragraph
\end_layout

\end_inset


\end_layout

\begin_layout Standard
We hypothesized that if naive 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 principal coordinate analysis.
 All 3 marks show a noticeable convergence between the naive 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 naive
 and memory samples was detected at every time point except day 14.
 The day 14 convergence pattern is also present in the RNA-seq data (Figure
 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:RNA-PCA-group"
plural "false"
caps "false"
noprefix "false"

\end_inset

), albiet 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 naive and memory cells are most
 similar at day 14, the furthest time point after activation.
 MOFA was also able to capture this day 14 convergence pattern in latent
 factor 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
 RNA-seq data, confirming that this convergence is a coordinated pattern
 across all 4 data sets.
 While this observation does not prove that the naive cells have differentiated
 into memory cells at Day 14, it is consistent with that hypothesis.
\end_layout

\begin_layout Subsection
Effect of H3K4me2 and H3K4me3 promoter coverage upstream vs downstream of
 TSS
\end_layout

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

\begin_layout Plain Layout
Need a better section title, for this and the next one.
\end_layout

\end_inset


\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
\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/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

\series bold
\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

\series bold
\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 Caption Standard

\begin_layout Plain Layout

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

\end_inset

K-means clustering of promoter H3K4me2 relative coverage depth in naive
 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 principal components
 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
To test whether the position of a histone mark relative to a gene's transcriptio
n start site (TSS) was important, we looked at the 
\begin_inset Quotes eld
\end_inset

landscape
\begin_inset Quotes erd
\end_inset

 of ChIP-seq read coverage in naive Day 0 samples within 5 kb of each gene's
 TSS by binning reads into 500-bp windows tiled across each promoter LogCPM
 values were calculated for the bins in each promoter and then the average
 logCPM 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 TSS.
 In order from must 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 PCA 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 PCA 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.
 A better representation might be something like a polar coordinate system
 with the origin at the center of Cluster 5, where the radius represents
 the peak height above the background and the angle represents the peak's
 position upstream or downstream of the TSS.
 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 Flex TODO Note (inline)
status open

\begin_layout Plain Layout
RNA-seq values in the plots use logCPM but should really use logFPKM or
 logTPM.
 Fix if time allows.
\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 Naive 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
 TSS, Clusters 1, 3, and 4, show the highest average expression distributions.
 Specifically, these clusters all have their highest ChIP-seq abundance
 within 1kb of the TSS, consistent with the previously determined promoter
 radius.
 In contrast, cluster 6, which represents peaks several kb upstream of the
 TSS, shows a slightly higher average expression than baseline, while Cluster
 2, which represents peaks several kb 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 kb downstream of the TSS, rather than
 Cluster 3, which represents peaks centered directly at the TSS.
 This suggests that conceptualizing the promoter as a region centered on
 the TSS 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 TSS may have a different degree of influence depending on whether it
 is upstream or downstream of the TSS.
\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/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

\series bold
\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

\series bold
\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

\series bold
\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

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

\end_inset

K-means clustering of promoter H3K4me3 relative coverage depth in naive
 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 principal components
 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

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\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
Is there more to say here?
\end_layout

\end_inset


\end_layout

\begin_layout Standard
All observations described above for H3K4me2 ChIP-seq 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 Subsection
Promoter coverage H3K27me3
\end_layout

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sideways false
status collapsed

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\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

\series bold
\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


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wide false
sideways false
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\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

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

\end_inset

PCA of relative coverage depth, colored by K-means cluster membership.
 
\series default
Note that Cluster 6 is hidden behind all the other clusters.
\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

\series bold
\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 Flex TODO Note (inline)
status open

\begin_layout Plain Layout
Repeated figure legends are kind of an issue here.
 What to do?
\end_layout

\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:H3K27me3-neighborhood"

\end_inset

K-means clustering of promoter H3K27me3 relative coverage depth in naive
 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 principal components
 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

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\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 maybe re-explain what was done or refer back to the previous section.
\end_layout

\end_inset


\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 TSS, 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 TSS itself: peak (Cluster 4) or trough (Cluster 2); lastly, the
 third axis represents a trough upstream of the TSS (Cluster 5) vs.
 downstream of the TSS (Cluster 6).
 Referring to these opposing pairs of clusters as axes of variation is justified
, because they correspond precisely to the first 3 principal components
 in the PCA 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 PCA 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 pyrimidal section of the
 ellipsoid.
\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 TSS, 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 TSS is potentially an important factor beyond simple proximity.
\end_layout

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

\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

\begin_layout Section
Discussion
\end_layout

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

\begin_layout Plain Layout
Write better section headers
\end_layout

\end_inset


\end_layout

\begin_layout Subsection
Effective promoter radius
\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 \thinspace{}
\end_inset

kb radius, while H3K27me3 is enriched within 2.5
\begin_inset space \thinspace{}
\end_inset

kb.
 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 TSS)
 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 assymetry 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
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

kb is approximately consistent with the distance from the TSS at which enrichmen
t of H3K4 methylationis correlates with increased expression, showing that
 this radius, which was determined by a simple analysis of measuring the
 distance from each TSS 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 TSS and asymmetric coverage upstream and
 downstream, so it is difficult in this case to evaluate whether the 2.5
\begin_inset space ~
\end_inset

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

\begin_layout Plain Layout
My instinct is to say 
\begin_inset Quotes eld
\end_inset

further study is needed
\begin_inset Quotes erd
\end_inset

 here, but that goes in Chapter 5, right?
\end_layout

\end_inset


\end_layout

\begin_layout Subsection
Convergence
\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 have observed that all 3 histone marks and the gene expression data all
 exhibit evidence of convergence in abundance between naive 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 MOFA latent factor 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 latent factor 5.
 Like all the latent factors 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, of course, is consistent with the expectation that any naive CD4
 T-cells remaining at day 14 should have differentiated into memory cells
 by that time, and should therefore have a genomic state similar to memory
 cells.
 This convergence is evidence that these histone marks all play an important
 role in the naive-to-memory differentiation process.
 A histone mark that was not involved in naive-to-memory differentiation
 would not be expected to converge in this way after activation.
\end_layout

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\align center
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	filename graphics/CD4-csaw/LaMere2016_fig8.pdf
	lyxscale 50
	width 60col%
	groupId colwidth

\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:Lamere2016-Fig8"

\end_inset

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 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 Standard
In H3K4me2, H3K4me3, and RNA-seq, this convergence appears to be in progress
 already by Day 5, shown by the smaller distance between naive 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 naive cells
 and memory cells converging at day 5.
 This model was developed without the benefit of the PCoA 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 SVA.
 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 PCoA to
 reveal interesting behaviors in the data that were previously only detectable
 by a detailed manual analysis.
\end_layout

\begin_layout Standard
While the ideal comparison to demonstrate this convergence would be naive
 cells at day 14 to memory cells at day 0, this is not feasible in this
 experimental system, since neither naive 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 naive 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 Subsection
Positional
\end_layout

\begin_layout Standard
When looking at patterns in the relative coverage of each histone mark near
 the TSS 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 kb wide, with the
 main axis of variation being the position of this peak relative to the
 TSS (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 TSS were more strongly associated with elevated gene expression.
 Coverage downstream of the TSS appears to be more strongly associated with
 elevated expression than coverage 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 TSS.
\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 TSS relative to the
 surrounding area, and a depletion of H3K27me3 downstream of the TSS 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 TSS 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 TSS
 with higher expression.
\end_layout

\begin_layout Subsection
Workflow
\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 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 CommandInset label
LatexCommand label
name "fig:rulegraph"

\end_inset


\series bold
Dependency graph of steps in reproducible workflow.
\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
The analyses described in this chapter were organized into a reproducible
 workflow using the Snakemake workflow management system.
 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 ChIP-seq 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 Formula $\texttt{chipseq\_count\_tss\_neighborhoods}$
\end_inset

, depends on the RNA-seq abundance estimates in order to select the most-used
 TSS for each gene, the aligned ChIP-seq reads, the index for those reads,
 and the blacklist of regions to be excluded from ChIP-seq 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
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 RNA-seq quantification methods were tested against
 two different reference transcriptome annotations for a total of 10 different
 quantifications of the same RNA-seq 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 SICER was unambiguously
 superior to MACS 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 Subsection
Data quality issues limit conclusions
\end_layout

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

\begin_layout Plain Layout
Is this needed?
\end_layout

\end_inset


\end_layout

\begin_layout Section
Future Directions
\end_layout

\begin_layout Standard
The analysis of RNA-seq and ChIP-seq in CD4 T-cells in Chapter 2 is in many
 ways a preliminary study that suggests a multitude of new avenues of investigat
ion.
 Here we consider a selection of such avenues.
\end_layout

\begin_layout Subsection
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 CpG island
 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 ChIP-seq 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 distince from the TSS 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 ChIP-seq 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 TSS, 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 TSS 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 TSS.
 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 naive & memory
 cells
\end_layout

\begin_layout Standard
In this study, a convergence between naive 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 naive 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 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-activa
tion state, or perhaps this process takes substantially longer than 14 days.
 This is a challenge for the convergence hypothesis because the ideal comparison
 to prove that naive cells are converging to a resting memory state would
 be to compare the final naive 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
To better study the convergence hypothesis, a new experiment should be designed
 using a model system for T-cell activation that is known to allow cells
 to return as closely as possible to their pre-activation state.
 Alternatively, if it is not possible to find or design such a model system,
 the same cell cultures could be activated serially multiple times, and
 sequenced after each activation cycle right before the next activation.
 It is likely that several activations in the same model system will settle
 into a cylical pattern, converging to a consistent 
\begin_inset Quotes eld
\end_inset

resting
\begin_inset Quotes erd
\end_inset

 state after each activation, even if this state is different from the initial
 resting state at Day 0.
 If so, it will be possible to compare the final states of both naive and
 memory cells to show that they converge despite different initial conditions.
\end_layout

\begin_layout Standard
In addition, if naive-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 MOFA can then be used to identify
 coordinated patterns of regulation shared across many epigenetic marks.
 If possible, some 
\begin_inset Quotes eld
\end_inset

negative control
\begin_inset Quotes erd
\end_inset

 marks should be included that are known 
\emph on
not
\emph default
 to be involved in T-cell activation or memory formation.
 Of course, CD4 T-cells are not the only adaptive immune cells with memory.
 A similar study could be designed for CD8 T-cells, B-cells, and even specific
 subsets of CD4 T-cells.
\end_layout

\begin_layout Subsection
Follow up on hints of interesting patterns in promoter relative coverage
 profiles
\end_layout

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

\begin_layout Plain Layout
I think I might need to write up the negative results for the Promoter CpG
 and defined pattern analysis before writing this section.
\end_layout

\end_inset


\end_layout

\begin_layout Itemize
Also find better normalizations: maybe borrow from MACS/SICER background
 correction methods?
\end_layout

\begin_layout Itemize
For H3K4, define polar coordinates based on PC1 & 2: R = peak size, Theta
 = peak position.
 Then correlate with expression.
\end_layout

\begin_layout Itemize
Current analysis only at Day 0.
 Need to study across time points.
\end_layout

\begin_layout Itemize
Integrating data across so many dimensions is a significant analysis challenge
\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
These three hypotheses could be disentangled by single-cell ChIP-seq.
 If the correlation between these two histone marks persists even within
 the reads for each individual cell, then cell population heterogeneity
 cannot explain the correlation.
 Allele-specific modification can be tested for by looking at the correlation
 between read coverage of the two histone marks at heterozygous loci.
 If the correlation between read counts for opposite loci is low, then this
 is consistent with allele-specific modification.
 Finally if the modifications do not separate by either cell or allele,
 the colocation of these two marks is most likely occurring at the level
 of individual histones, with the heterogenously modified histone representing
 a distinct state.
 
\end_layout

\begin_layout Standard
However, another experiment would be required to show direct evidence of
 such a heterogeneously modified state.
 Specifically a 
\begin_inset Quotes eld
\end_inset

double ChIP
\begin_inset Quotes erd
\end_inset

 experiment would need to be performed, where the input DNA is first subjected
 to an immunoprecipitation pulldown from the anti-H3K4me2 antibody, and
 then the enriched material is collected, with proteins still bound, and
 immunoprecipitated 
\emph on
again
\emph default
 using the anti-H3K4me3 antibody.
 If this yields significant numbers of non-artifactual reads in the same
 regions as the individual pulldowns of the two marks, this is strong evidence
 that the two marks are occurring on opposite H3 subunits of the same histones.
\end_layout

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

\begin_layout Plain Layout
Try to see if double ChIP-seq is actually feasible, and if not, come up
 with some other idea for directly detecting the mixed mod state.
 Oh! Actually ChIP-seq isn't required, only double ChIP followed by quantificati
on.
 That's one possible angle.
\end_layout

\end_inset


\end_layout

\begin_layout Chapter
Improving array-based diagnostics for transplant rejection by optimizing
 data preprocessing
\end_layout

\begin_layout Standard
\begin_inset Note Note
status open

\begin_layout Plain Layout
Chapter author list: Me, Sunil, Tom, Padma, Dan
\end_layout

\end_inset


\end_layout

\begin_layout Section
Approach
\end_layout

\begin_layout Subsection
Proper pre-processing is essential for array data
\end_layout

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

\begin_layout Plain Layout
This section could probably use some citations
\end_layout

\end_inset


\end_layout

\begin_layout Standard
Microarrays, bead arrays, and similar assays produce raw data in the form
 of fluorescence intensity measurements, with the 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, these measurements for each probe are also affected my many technical
 confounding factors, such as the concentration of target material, strength
 of off-target binding, and the sensitivity of the imaging sensor.
 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.
\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 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
 healthy transplants (TX) from transplants undergoing acute rejection (AR)
 or acute dysfunction with no rejection (ADNR).
 However, the the standard normalization algorithm used for microarray data,
 Robust Multi-chip Average (RMA) 
\begin_inset CommandInset citation
LatexCommand cite
key "Irizarry2003a"
literal "false"

\end_inset

, is not applicable in a clinical setting.
 Two of the steps in RMA, 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 RMA, 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
Frozen RMA (fRMA) addresses these concerns by replacing the quantile normalizati
on 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 publically available arrays sampled from
 a wide variety of tissues in the Gene Expression Omnibus (GEO).
 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
 GEO 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 fRMA 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 fRMA 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 Single Channel Array Normalization (SCAN),
 which adapts a normalization method originally designed for tiling arrays
 
\begin_inset CommandInset citation
LatexCommand cite
key "Piccolo2012"
literal "false"

\end_inset

.
 SCAN is truly single-channel in that it does not require a set of normalization
 paramters estimated from an external set of reference samples like fRMA
 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 then become thymine after amplication) 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
\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 CommandInset label
LatexCommand label
name "fig:Sigmoid-beta-m-mapping"

\end_inset


\series bold
Sigmoid shape of the mapping between β and M values
\end_layout

\end_inset


\end_layout

\end_inset


\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.
 M-values, interpreted as the log ratio of methylated to unmethylated copies,
 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 perperties:
 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 M-values.
\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 M-values and underestimated for
 probes with extreme M-values.
 This is particularly undesirable for methylation data because the intermediate
 M-values are the ones of most interest, since they are more likely to represent
 areas of varying methylation, whereas extreme M-values typically represent
 complete methylation or complete lack of methylation.
\end_layout

\begin_layout Standard
RNA-seq 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 "Law2013"
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 RNA-seq 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, the standard implementation of voom assumes that the input is
 given in raw read counts, and it must be adapted to run on methylation
 M-values.
\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 TX, AR, or ADNR 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 GEO 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 PAM package was used to train a nearest shrunken centroid classifier
 on the training set and select the appropriate threshold for centroid shrinking.
 Then the trained classifier was used to predict the class probabilities
 of each validation sample.
 From these class probabilities, ROC curves and area-under-curve (AUC) 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 TX and AR 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 TX and AR samples as the
 other set.
 For external validation, the full set of 115 TX and AR samples were used
 as a training set, and the 75 external TX and AR samples were used as the
 validation set.
 Thus, 2 ROC curves and AUC values were generated for each normalization
 method: one internal and one external.
 Because the external validation set contains no ADNR samples, only classificati
on of TX and AR samples was considered.
 The ADNR 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: distinguising
 TX from AR.
\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:
 RMA and dChip 
\begin_inset CommandInset citation
LatexCommand cite
key "Li2001,Irizarry2003a"
literal "false"

\end_inset

.
 Since RMA produces expression values on a log2 scale and dChip does not,
 the values from dChip were log2 transformed after normalization.
 Next, RMA and dChip followed by Global Rank-invariant Set Normalization
 (GRSN) were tested 
\begin_inset CommandInset citation
LatexCommand cite
key "Pelz2008"
literal "false"

\end_inset

.
 Post-processing with GRSN does not turn RMA 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, fRMA and SCAN, were
 tested 
\begin_inset CommandInset citation
LatexCommand cite
key "McCall2010,Piccolo2012"
literal "false"

\end_inset

.
 When evaluting 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 RMA, and the normalized
 data for each set were combined into a single set with no further attempts
 at normalizing between the two sets.
 The represents approximately how RMA 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 fRMA normalization for the hthgu133pluspm array platform,
 custom fRMA normalization vectors were trained using the frmaTools 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, a 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 fRMA 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 fRMA normalization was also compared against the normalized expression
 values obtained by normalizing the same 20 samples with ordinary RMA.
\end_layout

\begin_layout Subsection
Modeling methylation array M-value heteroskedasticy in linear models with
 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 metylation between 4 transplant statuses: healthy transplant
 (TX), transplants undergoing acute rejection (AR), acute dysfunction with
 no rejection (ADNR), and chronic allograpft nephropathy (CAN).
 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 ID (anonymized), Sex,
 Age, Ethnicity, Creatinine Level, and Diabetes diagnosois (all samples
 in this data set came from patients with either Type 1 or Type 2 diabetes).
 
\end_layout

\begin_layout Standard
The intensity data were first normalized using subset-quantile within array
 normalization (SWAN) 
\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 M-values.
\end_layout

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Analysis
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random effect
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eBayes
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weights
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voom
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A
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</lyxtabular>

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\begin_inset Caption Standard

\begin_layout Plain Layout

\series bold
\begin_inset CommandInset label
LatexCommand label
name "tab:Summary-of-meth-analysis"

\end_inset

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
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\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 "Law2013"
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 M-values, a series of parallel analyses was performed, each adding
 additional steps into the model fit to accomodate 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 Benjamini-Hochberg
 procedure for FDR control 
\begin_inset CommandInset citation
LatexCommand cite
key "Benjamini1995"
literal "false"

\end_inset

.
\end_layout

\begin_layout Standard
For the analysis B, surrogate variable analysis (SVA) 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 "Law2013"
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 Standard
\begin_inset Flex TODO Note (inline)
status open

\begin_layout Plain Layout
Improve subsection titles in this section.
\end_layout

\end_inset


\end_layout

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

\begin_layout Plain Layout
Reconsider subsection organization?
\end_layout

\end_inset


\end_layout

\begin_layout Subsection
Separate normalization with RMA introduces unwanted biases in classification
\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/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 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 (PP(AR), posterior probability of being AR).
 The color of each point indicates the true classification of that sample.
\end_layout

\end_inset


\end_layout

\end_inset


\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 TX from
 AR using the samples from the internal set as training data, evaluating
 performance on the external set.
 First, training and evaluation were performed after normalizing all array
 samples together as a single set using RMA, 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 AR to every sample.
 
\end_layout

\begin_layout Subsection
fRMA and SCAN maintain classification performance while eliminating dependence
 on normalization strategy
\end_layout

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status open

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\begin_layout Plain Layout
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\begin_inset Graphics
	filename graphics/PAM/ROC-TXvsAR-internal.pdf
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	height 40theight%
	groupId roc-pam

\end_inset


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\begin_inset Caption Standard

\begin_layout Plain Layout
\begin_inset CommandInset label
LatexCommand label
name "fig:ROC-PAM-int"

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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
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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

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\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

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\begin_inset Caption Standard

\begin_layout Plain Layout

\series bold
\begin_inset CommandInset label
LatexCommand label
name "fig:ROC-PAM-main"

\end_inset

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

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Normalization
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Internal Val.
 AUC
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 AUC
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RMA + GRSN
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0.750
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dChip + GRSN
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fRMA
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0.718
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SCAN
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\begin_layout Plain Layout
\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 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 RMA, while
 GRSN reduced the AUC values for both dChip and RMA.
 Both single-channel methods, fRMA and SCAN, slightly outperformed RMA,
 with fRMA ahead of SCAN.
 However, the difference between RMA and fRMA 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 ROC curves for RMA, dChip, and fRMA look very similar and
 relatively smooth, while both GRSN curves and the curve for SCAN have a
 more jagged appearance.
\end_layout

\begin_layout Standard
For external validation, as expected, all the AUC 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 GRSN, RMA shows its dominance over dChip in this more challengi
ng test.
 Unlike in the internal validation, GRSN actually improves the classifier
 performance for RMA, although it does not for dChip.
 Once again, both single-channel methods perform about on par with RMA,
 with fRMA performing slightly better and SCAN 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 ROC curves for the external validation test.
 As expected, none of them are as clean-looking as the internal validation
 ROC curves.
 The curves for RMA, RMA+GRSN, and fRMA 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
\begin_inset Float figure
wide false
sideways false
status open

\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

\series bold
\begin_inset CommandInset label
LatexCommand label
name "fig:frmatools-batch-size"

\end_inset

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
In order to enable use of fRMA to normalize hthgu133pluspm, a custom set
 of fRMA 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 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
 expense 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
\begin_inset Float figure
wide false
sideways false
status open

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename graphics/frma-pax-bx/M-BX-violin.pdf
	lyxscale 40
	width 45col%
	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-bx-violin"

\end_inset


\series bold
Violin plot of inter-normalization log ratios 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/M-PAX-violin.pdf
	lyxscale 40
	width 45col%
	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


\series bold
Violin plot of inter-normalization log ratios 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 CommandInset label
LatexCommand label
name "fig:frma-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
Since fRMA 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 fRMA 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 fRMA 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 fRMA against fRMA, the vast mojority 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 fRMA 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 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/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

\series bold
\begin_inset CommandInset label
LatexCommand label
name "fig:Representative-MA-plots"

\end_inset

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 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 M-values,
 but the trend of M-values is dependent on the average normalized intensity.
 This is expected, since the overall trend represents the differences in
 the quantile normalization step.
 When running RMA, only the quantiles for these specific 20 arrays are used,
 while for fRMA 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 fRMA normalizations, correspondin
g 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 fRMA normalizations to each other, indicating that the fRMA training
 process is robust to random batch downsampling for the blood samples as
 well.
\end_layout

\begin_layout Subsection
SVA, voom, and array weights improve model fit for methylation array data
\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

\series bold
Mean-variance trend modeling in methylation array data.
 
\series default
The estimated log2(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 overplotting 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
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 M-value 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
 M-values of +4 and -4.
 These modes correspond to methylation sites that are nearly 100% methylated
 and nearly 100% unmethylated, respectively.
 The strong bomodality 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 M-values
 (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 constitutitively 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
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 M-value range
 has disappeared, turning the W shape into a wide U shape.
 This indicates that the excess variance in the probes with intermediate
 M-values 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
 M-value range from about -3 to +3.
 Note that this corresponds closely to the range within which the M-value
 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 M-values 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 M-values have been appropriately
 down-weighted to account for the fact that the noise in those observations
 has been amplified by the non-linear M-value transformation.
 In turn, this gives relatively more weight to observervations 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
\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="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
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\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">
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0.00106
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<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

\series bold
\begin_inset CommandInset label
LatexCommand label
name "tab:weight-covariate-tests"

\end_inset

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 open

\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 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

\begin_layout Plain Layout

\end_layout

\end_inset


\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 Type 2 diabetes were assigned significantly
 lower weights than those from patients with Type 1 diabetes.
 This indicates that the type 2 diabetes 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 open

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

\begin_layout Plain Layout
Consider transposing these tables
\end_layout

\end_inset


\end_layout

\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>
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\begin_inset Text

\begin_layout Plain Layout

\end_layout

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</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>
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\begin_inset Text

\begin_layout Plain Layout

\end_layout

\end_inset
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\begin_inset Text

\begin_layout Plain Layout

\end_layout

\end_inset
</cell>
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Contrast
\end_layout

\end_inset
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\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">
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\begin_layout Plain Layout
0
\end_layout

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

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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
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</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
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7
\end_layout

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</cell>
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\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

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

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</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

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

\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 open

\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

\begin_layout Plain Layout

\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
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\begin_layout Plain Layout
\align center
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	filename graphics/methylvoom/unadj.dupcor.sva.aw/pval-histograms-PAGE2.pdf
	lyxscale 33
	width 30col%
	groupId meth-pval-hist

\end_inset


\end_layout

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\series bold
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\begin_layout Plain Layout
ADNR vs.
 TX, Analysis B
\end_layout

\end_inset


\end_layout

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wide false
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	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

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\end_layout

\begin_layout Plain Layout
\align center

\series bold
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wide false
sideways false
status collapsed

\begin_layout Plain Layout
\align center
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	filename graphics/methylvoom/unadj.dupcor.sva.voomaw/pval-histograms-PAGE1.pdf
	lyxscale 33
	width 30col%
	groupId meth-pval-hist

\end_inset


\end_layout

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\series bold
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\begin_layout Plain Layout
AR vs.
 TX, Analysis C
\end_layout

\end_inset


\end_layout

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wide false
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\begin_layout Plain Layout
\align center
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	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

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\end_layout

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\begin_inset Float figure
wide false
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\begin_layout Plain Layout
\align center
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	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

\series bold
\begin_inset CommandInset label
LatexCommand label
name "fig:meth-p-value-histograms"

\end_inset

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

.
 the 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 different from 1.
\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 FDR 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 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 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 RMA, 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 RMA.
 However, there is no analogous way to eliminate cross-array information
 sharing in the median polish step, so fRMA replaces this with a weighted
 average of probes on each array, with the weights learned from external
 data.
 This step of fRMA has the greatest potential to diverge from RMA un undesirable
 ways.
\end_layout

\begin_layout Standard
However, when run on real data, fRMA performed at least as well as RMA in
 both the internal validation and external validation tests.
 This shows that fRMA 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 RMA for normalization.
 The other single-channel normalization method considered, SCAN, showed
 some loss of AUC in the external validation test.
 Based on these results, fRMA 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
\begin_inset Flex TODO Note (inline)
status open

\begin_layout Plain Layout
Look up the exact numbers, do a find & replace for 
\begin_inset Quotes eld
\end_inset

850
\begin_inset Quotes erd
\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Standard
The published fRMA normalization vectors for the hgu133plus2 platform were
 generated from a set of about 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 fRMA 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 fRMA 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 based on samples of a specific
 tissue.
\end_layout

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

\begin_layout Plain Layout
Talk about how these vectors can be used for any data from these tissues
 on this platform even though they were custom made for this data set.
\end_layout

\end_inset


\end_layout

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

\begin_layout Plain Layout
How to bring up that these custom vectors were used in another project by
 someone else that was never published?
\end_layout

\end_inset


\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
 M-value 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 estiamte of the variance
 for each observation individually based on where its model-fitted M-value
 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 M-values are always at the extreme
 of the M-value range (e.g.
 less than -4) for ADNR samples, but the M-values for that probe in TX and
 CAN samples are within the flat region of the mean-variance trend (between
 -3 and +3), voom is able to down-weight the contribution of the high-variance
 M-values from the ADNR samples in order to gain more statistical power
 while testing for differential methylation between TX and CAN.
 In contrast, modeling the mean-variance trend only at the probe level would
 combine the high-variance ADNR 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 is at least as good 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 better with the theoretical 
\end_layout

\begin_layout Standard
The significant association of diebetes diagnosis with sample quality is
 interesting.
 The samples with Type 2 diabetes tended to have more variation, averaged
 across all probes, than those with Type 1 diabetes.
 This is consistent with the consensus that type 2 disbetes and the associated
 metabolic syndrome represent a broad dysregulation of the body's endocrine
 signalling related to metabolism [citation needed].
 This dysregulation could easily manifest as a greater degree of variation
 in the DNA methylation patterns of affected tissues.
 In contrast, Type 1 disbetes has a more specific cause and effect, so a
 less variable methylation signature is expected.
\end_layout

\begin_layout Standard
This preliminary anlaysis suggests that some degree of differential methylation
 exists between TX 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 SVA 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 SVA 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 Standard
\begin_inset Flex TODO Note (inline)
status open

\begin_layout Plain Layout
Some work was already being done with the existing fRMA vectors.
 Do I mention that here?
\end_layout

\end_inset


\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 fRMA 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 inproduce 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
 fRMA algorithm but rather a limitation of the implementation in the frmaTools
 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 subsamplin
g step is eliminated, meaning that different subsamplings 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.
 SVA 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 SVA 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 SVA 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 SVA 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 SVA 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
Globin-blocking for more effective blood RNA-seq analysis in primate animal
 model
\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 (Macaca fascicularis).
\end_layout

\end_inset


\end_layout

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

\begin_layout Plain Layout
Chapter author list: https://tex.stackexchange.com/questions/156862/displaying-aut
hor-for-each-chapter-in-book Every chapter gets an author list, which may
 or may not be part of a citation to a published/preprinted paper.
\end_layout

\end_inset


\end_layout

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

\begin_layout Plain Layout
Preprint then cite the paper
\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 messenger RNA.
 Globin reduction is a standard technique used to improve the expression
 results obtained by DNA microarrays on RNA from blood samples.
 However, with whole transcriptome RNA-sequencing (RNA-seq) quickly replacing
 microarrays for many applications, the impact of globin reduction for RNA-seq
 has not been previously 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 RNA-seq in primate blood samples that uses
 complimentary oligonucleotides to block reverse transcription of the alpha
 and beta globin genes.
 In test samples from cynomolgus monkeys (Macaca fascicularis), this globin
 blocking 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 globin blocking significantly improves the cost-effectiv
eness of mRNA sequencing 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 RNA-seq studies of primate blood samples.
\end_layout

\begin_layout Section
Approach
\end_layout

\begin_layout Standard
\begin_inset Note Note
status open

\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
Increasingly, researchers are turning to high-throughput mRNA sequencing
 technologies (RNA-seq) in preference to expression microarrays for analysis
 of gene expression 
\begin_inset CommandInset citation
LatexCommand cite
key "Mutz2012"
literal "false"

\end_inset

.
 The advantages are even greater for study of model organisms with no well-estab
lished array platforms available, such as the cynomolgus monkey (Macaca
 fascicularis).
 High fractions of globin mRNA are naturally present in mammalian peripheral
 blood samples (up to 70% of total mRNA) 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

.
 The importance of globin reduction for RNA-seq of blood has only been evaluated
 for a deepSAGE protocol on human samples 
\begin_inset CommandInset citation
LatexCommand cite
key "Mastrokolias2012"
literal "false"

\end_inset

.
 In the present report, we evaluated globin reduction using custom blocking
 oligonucleotides for deep RNA-seq of peripheral blood samples from a nonhuman
 primate, cynomolgus monkey, using the Illumina technology platform.
 We demonstrate that globin reduction significantly improves the cost-effectiven
ess of RNA-seq in blood samples.
 Thus, our protocol offers a significant advantage to any investigator planning
 to use RNA-seq for gene expression profiling of nonhuman primate blood
 samples.
 Our method can be generally applied to any species by designing complementary
 oligonucleotide 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 April 2012 and 18 June 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 ml whole blood into 6.9 ml of PAX gene
 additive.
\end_layout

\begin_layout Subsection
Globin Blocking
\end_layout

\begin_layout Standard
Four oligonucleotides were designed to hybridize to the 3’ end of the transcript
s for Cynomolgus HBA1, HBA2 and HBB, with two hybridization sites for HBB
 and 2 sites for HBA (the chosen sites were identical in both HBA genes).
 All oligos were purchased from Sigma and were entirely composed of 2’O-Me
 bases with a C3 spacer positioned at the 3’ ends to prevent any polymerase
 mediated primer extension.
\end_layout

\begin_layout Quote
HBA1/2 site 1: GCCCACUCAGACUUUAUUCAAAG-C3spacer
\end_layout

\begin_layout Quote
HBA1/2 site 2: GGUGCAAGGAGGGGAGGAG-C3spacer
\end_layout

\begin_layout Quote
HBB site 1: AAUGAAAAUAAAUGUUUUUUAUUAG-C3spacer
\end_layout

\begin_layout Quote
HBB site 2: CUCAAGGCCCUUCAUAAUAUCCC-C3spacer
\end_layout

\begin_layout Subsection
RNA-seq Library Preparation 
\end_layout

\begin_layout Standard
Sequencing libraries were prepared with 200ng total RNA from each sample.
 Polyadenylated mRNA was selected from 200 ng aliquots of cynomologus blood-deri
ved total RNA using Ambion Dynabeads Oligo(dT)25 beads (Invitrogen) following
 manufacturer’s recommended protocol.
 PolyA selected RNA was then combined with 8 pmol of HBA1/2 (site 1), 8
 pmol of HBA1/2 (site 2), 12 pmol of HBB (site 1) and 12 pmol of HBB (site
 2) oligonucleotides.
 In addition, 20 pmol of RT primer containing a portion of the Illumina
 adapter sequence (B-oligo-dTV: GAGTTCCTTGGCACCCGAGAATTCCATTTTTTTTTTTTTTTTTTTV)
 and 4 µL of 5X First Strand buffer (250 mM Tris-HCl pH 8.3, 375 mM KCl,
 15mM MgCl2) were added in a total volume of 15 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 µL 0.1 M DTT, 1 µL RNaseOUT, 1 µL
 10mM dNTPs 10% biotin-16 aminoallyl-2’- dUTP and 10% biotin-16 aminoallyl-2’-
 dCTP (TriLink Biotech, San Diego, CA), 1 µL Superscript II (200U/ µL, Thermo-Fi
sher).
 A second “unblocked” library was prepared in the same way for each sample
 but replacing the blocking oligos 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 µL of 10 mM Tris-HCl pH 8.0, and then
 bound to 25 µL of M280 Magnetic Streptavidin beads washed per recommended
 protocol (Thermo-Fisher).
 After 30 minutes of binding, beads were washed one time in 100 µL 0.1N NaOH
 to denature and remove the bound RNA, followed by two 100 µL washes with
 1X TE buffer.
\end_layout

\begin_layout Standard
Subsequent attachment of the 5-prime Illumina A adapter was performed by
 on-bead random primer extension of the following sequence (A-N8 primer:
 TTCAGAGTTCTACAGTCCGACGATCNNNNNNNN).
 Briefly, beads were resuspended in a 20 µL reaction containing 5 µM A-N8
 primer, 40mM Tris-HCl pH 7.5, 20mM MgCl2, 50mM NaCl, 0.325U/µL Sequenase
 2.0 (Affymetrix, Santa Clara, CA), 0.0025U/µL inorganic pyrophosphatase (Affymetr
ix) and 300 µM each dNTP.
 Reaction was incubated at 22°C for 30 minutes, then beads were washed 2
 times with 1X TE buffer (200µL).
\end_layout

\begin_layout Standard
The magnetic streptavidin beads were resuspended in 34 µL nuclease-free
 water and added directly to a PCR tube.
 The two Illumina protocol-specified PCR primers were added at 0.53 µM (Illumina
 TruSeq Universal Primer 1 and Illumina TruSeq barcoded PCR primer 2), along
 with 40 µL 2X KAPA HiFi Hotstart ReadyMix (KAPA, Willmington MA) and thermocycl
ed 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
PCR products were purified with 1X Ampure Beads following manufacturer’s
 recommended 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 bp (corresponding to insert sizes
 of 130 to 230 bps).
 Finished library pools were then sequenced on the Illumina NextSeq500 instrumen
t with 75 base read lengths.
 
\end_layout

\begin_layout Subsection
Read alignment and counting
\end_layout

\begin_layout Standard
Reads were aligned to the cynomolgus genome using STAR 
\begin_inset CommandInset citation
LatexCommand cite
key "Dobin2013,Wilson2013"
literal "false"

\end_inset

.
 Counts of uniquely mapped reads were obtained for every gene in each sample
 with the “featureCounts” function from the Rsubread package, using each
 of the three possibilities for the “strandSpecific” 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 as “hemoglobin subunit alpha-lik
e” (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 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 ncRNA 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 RNA-seq using
 our protocol in standard practice.
 
\end_layout

\begin_layout Subsection
Normalization and Exploratory Data Analysis
\end_layout

\begin_layout Standard
Libraries were normalized by computing scaling factors using the edgeR package’s
 Trimmed Mean of M-values method 
\begin_inset CommandInset citation
LatexCommand cite
key "Robinson2010"
literal "false"

\end_inset

.
 Log2 counts per million values (logCPM) were calculated using the cpm function
 in edgeR for individual samples and aveLogCPM 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 logCPM values
 across all libraries were at least -1.
 Normalizing for gene length was unnecessary because the sequencing protocol
 is 3’-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 blocking on reproducibility, Pearson and
 Spearman correlation coefficients were computed between the logCPM values
 for every pair of libraries within the globin-blocked (GB) and unblocked
 (non-GB) groups, and edgeR's “estimateDisp” function was used to compute
 negative binomial 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 edgeR, by
 first fitting a negative binomial generalized linear model 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 globin blocking on each gene, an additive
 model was fit to the full data with coefficients for globin blocking and
 SampleID.
 To test the effect of globin blocking on detection of differentially expressed
 genes, the GB 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-transplant pair of samples for each animal (N=7 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 ID.
 In all analyses, p-values were adjusted using the Benjamini-Hochberg procedure
 for FDR 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
\begin_inset ERT
status open

\begin_layout Plain Layout


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\begin_layout Plain Layout
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\begin_inset Tabular
<lyxtabular version="3" rows="4" columns="7">
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Percent of Total Reads
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Percent of Genic Reads
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GB
\end_layout

\end_inset
</cell>
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Non-globin Reads
\end_layout

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\begin_inset Text

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Globin Reads
\end_layout

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\begin_inset Text

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All Genic Reads
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\end_inset
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\begin_inset Text

\begin_layout Plain Layout

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All Aligned Reads
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\begin_inset Text

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Non-globin Reads
\end_layout

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

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Globin Reads
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\end_inset
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<row>
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\begin_inset Text

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

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50.4% ± 6.82
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\end_inset
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<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
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\bar no
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3.48% ± 2.94
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\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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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

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\bar no
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89.7% ± 2.40
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\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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\color none
93.5% ± 5.25
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<cell alignment="center" valignment="top" topline="true" leftline="true" rightline="true" usebox="none">
\begin_inset Text

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\emph off
\bar no
\strikeout off
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\color none
6.49% ± 5.25
\end_layout

\end_inset
</cell>
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<row>
<cell alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" usebox="none">
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No
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26.3% ± 8.95
\end_layout

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44.6% ± 16.6
\end_layout

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70.1% ± 9.38
\end_layout

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\color none
90.7% ± 5.16
\end_layout

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38.8% ± 17.1
\end_layout

\end_inset
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\color none
61.2% ± 17.1
\end_layout

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

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\begin_layout Plain Layout
\begin_inset Caption Standard

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\series bold
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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

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

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status open

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\backslash
end{landscape}
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}
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\begin_layout Standard
The objective of the present study was to validate a new protocol for deep
 RNA-seq 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 globin blocking protocol, 37 blood samples,
 16 from pre-transplant and 21 from post-transplant time points, were each
 prepped once with and once without globin blocking oligos, and were then
 sequenced on an Illumina NextSeq500 instrument.
 The number of reads aligning to each gene in the cynomolgus genome was
 counted.
 Table 1 summarizes the distribution of read fractions among the GB and
 non-GB libraries.
 In the libraries with no globin blocking, 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 GB libraries, globin reads made up only 3.48% and reads assigned
 to all other genes increased to 50.4%.
 Thus, globin blocking 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
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, globin blocking 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 logCPM across all genes between the GB 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 TMM normalization correctly
 identifies this 2-fold difference as biologically irrelevant and removes
 it.
\end_layout

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wide false
sideways false
status collapsed

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\align center
\begin_inset Graphics
	filename graphics/Globin Paper/figure1 - globin-fractions.pdf
	lyxscale 50
	width 75col%

\end_inset


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\begin_layout Plain Layout

\series bold
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Fraction of genic reads in each sample aligned to non-globin genes, with
 and without globin blocking (GB).
\end_layout

\end_inset


\begin_inset CommandInset label
LatexCommand label
name "fig:Fraction-of-genic-reads"

\end_inset

Fraction of genic reads in each sample aligned to non-globin genes, with
 and without globin blocking (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
 plots.
 Points are randomly spread vertically to avoid excessive overlapping.
\end_layout

\end_inset


\end_layout

\end_inset


\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 GB 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 GB 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

).
 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 globin blocking, even though the average yield improvement
 for globin blocking is only 2-fold, because every sample has a chance of
 being 90% globin and 10% useful reads.
 Hence, the more consistent behavior of GB 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

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Remove redundant titles from figures
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wide false
sideways false
status collapsed

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename graphics/Globin Paper/figure2 - aveLogCPM-colored.pdf
	lyxscale 50
	height 60theight%

\end_inset


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\begin_layout Plain Layout
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\series bold
\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

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 globin-blocked (GB) and non-GB groups and the
 average abundance for each gene in both groups, measured in log2 counts
 per million reads counted, was computed using the aveLogCPM function.
 The distribution of average gene logCPM values was plotted for both groups
 using a kernel density plot to approximate a continuous distribution.
 The logCPM GB distributions are marked in red, non-GB in blue.
 The black vertical line denotes the chosen detection threshold of -1.
 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

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\end_layout

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\end_layout

\begin_layout Standard
Since globin blocking 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 logCPM 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 GB 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 GB samples is about 2-fold lower.
 This greater separation between signal and noise peaks in the GB samples
 means that low-expression genes should be more easily detected and more
 precisely quantified than in the non-GB samples.
\end_layout

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\begin_inset Float figure
wide false
sideways false
status collapsed

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename graphics/Globin Paper/figure3 - detection.pdf
	lyxscale 50
	width 70col%

\end_inset


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\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout

\series bold
\begin_inset Argument 1
status collapsed

\begin_layout Plain Layout
Gene detections as a function of abundance thresholds in globin-blocked
 (GB) and non-GB samples.
\end_layout

\end_inset


\begin_inset CommandInset label
LatexCommand label
name "fig:Gene-detections"

\end_inset

Gene detections as a function of abundance thresholds in globin-blocked
 (GB) and non-GB samples.

\series default
 Average abundance (logCPM, 
\begin_inset Formula $\log_{2}$
\end_inset

 counts per million reads counted) 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 -2 to 3, the number of genes detected
 at or above that logCPM threshold was plotted for each group.
\end_layout

\end_inset


\end_layout

\begin_layout Plain Layout

\end_layout

\end_inset


\end_layout

\begin_layout Standard
Based on these distributions, we selected a detection threshold of -1, 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 GB libraries and non-GB libraries separately and re-comput
ing normalization factors independently within each group, 14535 genes were
 detected in the GB libraries while only 12460 were detected in the non-GB
 libraries.
 Thus, GB allowed the detection of 2000 extra genes that were buried under
 the noise floor without GB.
 This pattern of at least 2000 additional genes detected with GB 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 Subsection
Globin blocking does not add significant additional noise or decrease sample
 quality
\end_layout

\begin_layout Standard
One potential worry is that the globin blocking 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
\begin_inset Float figure
wide false
sideways false
status collapsed

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename graphics/Globin Paper/figure4 - maplot-colored.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
MA plot showing effects of globin blocking 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 globin blocking 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 -1 were filtered out.
 Each remaining gene was tested for differential abundance with respect
 to globin blocking (GB) using edgeR’s quasi-likelihood F-test, fitting
 a negative binomial generalized linear model to table of read counts in
 each library.
 For each gene, edgeR reported average abundance (logCPM), 
\begin_inset Formula $\log_{2}$
\end_inset

 fold change (logFC), p-value, and Benjamini-Hochberg adjusted false discovery
 rate (FDR).
 Each gene's logFC was plotted against its logCPM, colored by FDR.
 Red points are significant at ≤10% 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

\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
Standardize on 
\begin_inset Quotes eld
\end_inset

log2
\begin_inset Quotes erd
\end_inset

 notation
\end_layout

\end_inset


\end_layout

\begin_layout Standard
The data do indeed show small systematic perturbations in gene levels (Figure
 
\begin_inset CommandInset ref
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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 log fold changes: HBD and
 LOC1021365.
 HBD, delta globin, is most likely targeted by the blocking oligos due to
 high sequence homology with the other globin genes.
 LOC1021365 is the aforementioned ncRNA that is reverse-complementary to
 one of the alpha-like genes and that would be expected to be removed during
 the globin blocking step.
 All other genes appear in a cluster centered vertically at 0, and the vast
 majority of genes in this cluster show an absolute log2(FC) of 0.5 or less.
 Nevertheless, many of these small perturbations are still statistically
 significant, indicating that the globin blocking oligos likely cause very
 small but non-zero systematic perturbations in measured gene expression
 levels.
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Comparison of inter-sample gene abundance correlations with and without
 globin blocking.
\end_layout

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name "fig:gene-abundance-correlations"

\end_inset

Comparison of inter-sample gene abundance correlations with and without
 globin blocking (GB).

\series default
 All libraries were normalized together as described in Figure 2, and genes
 with an average abundance (logCPM, log2 counts per million reads counted)
 less than -1 were filtered out.
 Each gene’s logCPM was computed in each library using the edgeR cpm 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 distribution of GB
 correlations vs.
 that of non-GB correlations.
 The thin dashed line is the identity line, provided for reference.
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To evaluate the possibility of globin blocking causing random perturbations
 and reducing sample quality, we computed the Pearson correlation between
 logCPM values for every pair of samples with and without GB and plotted
 them against each other (Figure 
\begin_inset CommandInset ref
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plural "false"
caps "false"
noprefix "false"

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).
 The plot indicated that the GB libraries have higher sample-to-sample correlati
ons than the non-GB libraries.
 Parametric and nonparametric tests for differences between the correlations
 with and without GB both confirmed that this difference was highly significant
 (2-sided paired t-test: t = 37.2, df = 665, P ≪ 2.2e-16; 2-sided Wilcoxon
 sign-rank test: V = 2195, P ≪ 2.2e-16).
 Performing the same tests on the Spearman correlations gave the same conclusion
 (t-test: t = 26.8, df = 665, P ≪ 2.2e-16; sign-rank test: V = 8781, P ≪ 2.2e-16).
 The edgeR package was used to compute the overall biological coefficient
 of variation (BCV) for GB and non-GB libraries, and found that globin blocking
 resulted in a negligible increase in the BCV (0.417 with GB vs.
 0.400 without).
 The near equality of the BCVs for both sets indicates that the higher correlati
ons in the GB 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 logCPM values 
\begin_inset CommandInset citation
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key "McCarthy2012"
literal "false"

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.
 This improves the precision of expression measurements and more than offsets
 the negligible increase in BCV.
\end_layout

\begin_layout Subsection
More differentially expressed genes are detected with globin blocking
\end_layout

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<lyxtabular version="3" rows="5" columns="5">
<features tabularvalignment="middle">
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\series bold
No Globin Blocking
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\series bold
Up
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\series bold
NS
\end_layout

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\series bold
Down
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Globin-Blocking
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11235
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Comparison of significantly differentially expressed genes with and without
 globin blocking.
\end_layout

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\begin_inset CommandInset label
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name "tab:Comparison-of-significant"

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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

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To compare performance on differential gene expression tests, we took subsets
 of both the GB and non-GB libraries with exactly one pre-transplant and
 one post-transplant sample for each animal that had paired samples available
 for analysis (N=7 animals, N=14 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 GB libraries and non-GB libraries, in each case
 using an FDR of 10% as the threshold of significance.
 Out of 12954 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 GB set only; 296
 were differentially expressed in the non-GB set only; 2 genes were called
 significantly up in the GB set but significantly down in the non-GB set;
 and the remaining 11235 were not called differentially expressed in either
 set.
 These data are summarized in Table 
\begin_inset CommandInset ref
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reference "tab:Comparison-of-significant"
plural "false"
caps "false"
noprefix "false"

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.
 The differences in BCV calculated by EdgeR for these subsets of samples
 were negligible (BCV = 0.302 for GB and 0.297 for non-GB).
\end_layout

\begin_layout Standard
The key point is that the GB 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 GB samples.
 However, given that both datasets are derived from the same biological
 samples and have nearly equal BCVs, it is more likely that the larger number
 of DE calls in the GB samples are genuine detections that were enabled
 by the higher sequencing depth and measurement precision of the GB samples.
 Note that the same set of genes was considered in both subsets, so the
 larger number of differentially expressed gene calls in the GB data set
 reflects a greater sensitivity to detect significant differential gene
 expression and not simply the larger total number of detected genes in
 GB 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 the with 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 globin blocking 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 do deep RNA-seq 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 RNA-seq or how much improvement in efficiency or sensitivity to detect
 differential gene expression would be achieved for the added cost and work.
 
\end_layout

\begin_layout Standard
We only found one report that demonstrated that globin reduction significantly
 improved the effective read yields for sequencing of human peripheral blood
 cell RNA using a DeepSAGE protocol 
\begin_inset CommandInset citation
LatexCommand cite
key "Mastrokolias2012"
literal "false"

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.
 The approach to DeepSAGE involves two different restriction enzymes that
 purify and then tag small fragments of transcripts at specific locations
 and thus, significantly reduces the complexity of the transcriptome.
 Therefore, we could not determine how DeepSAGE results would translate
 to the common strategy in the field for assaying the entire transcript
 population by whole-transcriptome 3’-end RNA-seq.
 Furthermore, if globin reduction is necessary, we also 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 globin blocking oligos 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 GB and non-GB protocols are not possible without additional
 normalization.
 
\end_layout

\begin_layout Standard
More importantly, globin blocking not only nearly doubles the yield of usable
 reads, it also increases inter-sample correlation and sensitivity to detect
 differential gene expression relative to the same set of samples profiled
 without blocking.
 In addition, globin blocking does not add a significant amount of random
 noise to the data.
 Globin blocking thus represents a cost-effective way to squeeze more data
 and statistical power out of the same blood samples and the same amount
 of sequencing.
 In conclusion, globin reduction greatly increases the yield of useful RNA-seq
 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 oligonucleotides is recommended for all deep RNA-seq
 of cynomolgus and other nonhuman primate blood samples.
\end_layout

\begin_layout Section
Future Directions
\end_layout

\begin_layout Standard
One drawback of the globin blocking 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 RNA-seq 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 globin blocking method in place, the way is now clear for this
 experiment to proceed.
\end_layout

\begin_layout Chapter
Future Directions
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\end_document