#LyX 2.3 created this file. For more info see http://www.lyx.org/
\lyxformat 544
\begin_document
\begin_header
\save_transient_properties true
\origin unavailable
\textclass extbook
\begin_preamble
% List all used files in log output
\listfiles
%% Add TOC, List of Figures, etc. to TOC
\usepackage{tocbibind}
% Add a DRAFT watermark
\usepackage{draftwatermark}
\usepackage{accsupp}
\SetWatermarkLightness{0.97}
\SetWatermarkScale{1}
% Make watermark not copyable (in Adobe Reader)
\SetWatermarkText{\BeginAccSupp{method=escape,ActualText={}}DRAFT\EndAccSupp{}}
% Set up required header format
\usepackage{fancyhdr}
\pagestyle{fancy}
\renewcommand{\headrulewidth}{0pt}
\rhead{}
\lhead{}
\chead{}
\rfoot{}
\lfoot{}
% Make page number not copyable (in Adobe Reader)
\cfoot{\BeginAccSupp{method=escape,ActualText={}}\thepage\EndAccSupp{}} % Page number bottom center
% Allow FloatBarrier command
\usepackage{placeins}
% Allow landscape pages
\usepackage{pdflscape}
% Allow doing things after the end of the current page
% (to avoid landscape figures breaking up text)
\usepackage{afterpage}
% Consider: force floats after placement in text
% https://tex.stackexchange.com/questions/15706/force-floats-to-be-typeset-after-their-occurrence-in-the-source-text
% This one breaks subfigs so it's disabled
% https://tex.stackexchange.com/questions/65680/automatically-bold-first-sentence-of-a-floats-caption
\usepackage[automake=immediate,nonumberlist,nohypertypes={abbreviation}]{glossaries-extra}
\setabbreviationstyle{long-short}
\loadglsentries{abbrevs.tex}
\makeglossaries
% arara: xelatex
% arara: biber
% arara: makeglossaries
% arara: xelatex
\end_preamble
\use_default_options true
\begin_modules
todonotes
logicalmkup
\end_modules
\maintain_unincluded_children false
\begin_local_layout
Format 66
InsetLayout "Flex:Glossary Term"
LyxType custom
LabelString gls
LatexType command
LatexName gls*
InToc true
CustomPars false
End
InsetLayout "Flex:Glossary Term (Capital)"
LyxType custom
LabelString Gls
LatexType command
LatexName Gls*
InToc true
CustomPars false
End
InsetLayout "Flex:Glossary Term (pl)"
LyxType custom
LabelString glspl
LatexType command
LatexName glspl*
InToc true
CustomPars false
End
InsetLayout "Flex:Glossary Term (Capital, pl)"
LyxType custom
LabelString Glspl
LatexType command
LatexName Glspl*
InToc true
CustomPars false
End
InsetLayout "Flex:Glossary Term (glstext)"
LyxType custom
LabelString glstext
LatexType command
LatexName glstext*
InToc true
CustomPars false
End
InsetLayout "Flex:Glossary Term (Glstext)"
LyxType custom
LabelString Glstext
LatexType command
LatexName Glstext*
InToc true
CustomPars false
End
InsetLayout "Flex:Glossary Term (glsfirst)"
LyxType custom
LabelString glsfirst
LatexType command
LatexName glsfirst*
InToc true
CustomPars false
End
InsetLayout "Flex:Glossary Term (Glsfirst)"
LyxType custom
LabelString Glsfirst
LatexType command
LatexName Glsfirst*
InToc true
CustomPars false
End
InsetLayout "Flex:Glossary Term (glsdesc)"
LyxType custom
LabelString glsdesc
LatexType command
LatexName glsdesc*
InToc true
CustomPars false
End
InsetLayout "Flex:Glossary Term (Glsdesc)"
LyxType custom
LabelString Glsdesc
LatexType command
LatexName Glsdesc*
InToc true
CustomPars false
End
\end_local_layout
\language english
\language_package default
\inputencoding utf8
\fontencoding default
\font_roman "default" "default"
\font_sans "default" "default"
\font_typewriter "default" "default"
\font_math "auto" "auto"
\font_default_family default
\use_non_tex_fonts false
\font_sc false
\font_osf false
\font_sf_scale 100 100
\font_tt_scale 100 100
\use_microtype false
\use_dash_ligatures true
\graphics default
\default_output_format pdf4
\output_sync 0
\bibtex_command biber
\index_command default
\paperfontsize 12
\spacing double
\use_hyperref true
\pdf_author "Ryan C. Thompson"
\pdf_bookmarks true
\pdf_bookmarksnumbered true
\pdf_bookmarksopen true
\pdf_bookmarksopenlevel 1
\pdf_breaklinks true
\pdf_pdfborder true
\pdf_colorlinks false
\pdf_backref false
\pdf_pdfusetitle true
\papersize letterpaper
\use_geometry true
\use_package amsmath 1
\use_package amssymb 1
\use_package cancel 1
\use_package esint 1
\use_package mathdots 1
\use_package mathtools 1
\use_package mhchem 1
\use_package stackrel 1
\use_package stmaryrd 1
\use_package undertilde 1
\cite_engine biblatex
\cite_engine_type numerical
\biblio_style plain
\biblio_options sorting=none
\biblatex_bibstyle numeric
\biblatex_citestyle numeric
\use_bibtopic false
\use_indices false
\paperorientation portrait
\suppress_date false
\justification true
\use_refstyle 1
\use_minted 0
\index Index
\shortcut idx
\color #008000
\end_index
\leftmargin 1.5in
\topmargin 1in
\rightmargin 1in
\bottommargin 1in
\secnumdepth 3
\tocdepth 3
\paragraph_separation indent
\paragraph_indentation default
\is_math_indent 0
\math_numbering_side default
\quotes_style english
\dynamic_quotes 0
\papercolumns 1
\papersides 1
\paperpagestyle default
\tracking_changes false
\output_changes false
\html_math_output 0
\html_css_as_file 0
\html_be_strict false
\end_header
\begin_body
\begin_layout Standard
\begin_inset ERT
status collapsed
\begin_layout Plain Layout
\backslash
pdfbookmark{Title page}{title}
\end_layout
\end_inset
\end_layout
\begin_layout Title
Bioinformatic analysis of complex, high-throughput genomic and epigenomic
data in the context of CD4
\begin_inset Formula $^{+}$
\end_inset
T-cell differentiation and diagnosis and treatment of transplant rejection
\end_layout
\begin_layout Author
A thesis presented
\begin_inset Newline newline
\end_inset
by
\begin_inset Newline newline
\end_inset
Ryan C.
Thompson
\begin_inset Newline newline
\end_inset
to
\begin_inset Newline newline
\end_inset
The Scripps Research Institute Graduate Program
\begin_inset Newline newline
\end_inset
in partial fulfillment of the requirements for the degree of
\begin_inset Newline newline
\end_inset
Doctor of Philosophy in the subject of Biology
\begin_inset Newline newline
\end_inset
for
\begin_inset Newline newline
\end_inset
The Scripps Research Institute
\begin_inset Newline newline
\end_inset
La Jolla, California
\end_layout
\begin_layout Date
October 2019
\end_layout
\begin_layout Standard
\begin_inset Note Note
status open
\begin_layout Plain Layout
To remove TODOs and watermark: Add
\begin_inset Quotes eld
\end_inset
final
\begin_inset Quotes erd
\end_inset
to the document class custom options.
\end_layout
\end_inset
\end_layout
\begin_layout Standard
\begin_inset ERT
status open
\begin_layout Plain Layout
\backslash
frontmatter
\end_layout
\end_inset
\begin_inset Note Note
status open
\begin_layout Plain Layout
Use roman numeral page numbers
\end_layout
\end_inset
\end_layout
\begin_layout Standard
\begin_inset Newpage newpage
\end_inset
\end_layout
\begin_layout Standard
\align center
\begin_inset ERT
status collapsed
\begin_layout Plain Layout
\backslash
phantomsection
\end_layout
\begin_layout Plain Layout
\backslash
addcontentsline{toc}{chapter}{Copyright notice}
\end_layout
\end_inset
\end_layout
\begin_layout Standard
\align center
\begin_inset ERT
status collapsed
\begin_layout Plain Layout
\backslash
vspace*{
\backslash
stretch{1}}
\end_layout
\end_inset
\end_layout
\begin_layout Standard
\align center
© 2019 by Ryan C.
Thompson
\end_layout
\begin_layout Standard
\align center
All rights reserved.
\end_layout
\begin_layout Standard
\align center
\begin_inset ERT
status collapsed
\begin_layout Plain Layout
\backslash
vspace*{
\backslash
stretch{2}}
\end_layout
\end_inset
\end_layout
\begin_layout Standard
\align center
\begin_inset Note Note
status open
\begin_layout Plain Layout
\begin_inset Newpage newpage
\end_inset
\end_layout
\begin_layout Plain Layout
\align center
\begin_inset ERT
status collapsed
\begin_layout Plain Layout
\backslash
phantomsection
\end_layout
\begin_layout Plain Layout
\backslash
addcontentsline{toc}{chapter}{Thesis acceptance form}
\end_layout
\end_inset
\end_layout
\begin_layout Plain Layout
\align center
[Thesis acceptance form]
\end_layout
\end_inset
\end_layout
\begin_layout Standard
\begin_inset Newpage newpage
\end_inset
\end_layout
\begin_layout Standard
\align center
\begin_inset ERT
status collapsed
\begin_layout Plain Layout
\backslash
phantomsection
\end_layout
\begin_layout Plain Layout
\backslash
addcontentsline{toc}{chapter}{Dedication}
\end_layout
\end_inset
\end_layout
\begin_layout Standard
\align center
\begin_inset ERT
status collapsed
\begin_layout Plain Layout
\backslash
vspace*{
\backslash
stretch{1}}
\end_layout
\end_inset
\end_layout
\begin_layout Standard
\align center
For Dan, who helped me through the hard times again and again.
\begin_inset Newline newline
\end_inset
He is, and will always be, fondly remembered and sorely missed.
\end_layout
\begin_layout Standard
\align center
\begin_inset ERT
status collapsed
\begin_layout Plain Layout
\backslash
vspace*{
\backslash
stretch{2}}
\end_layout
\end_inset
\end_layout
\begin_layout Standard
\begin_inset Newpage newpage
\end_inset
\end_layout
\begin_layout Standard
\align center
\begin_inset ERT
status collapsed
\begin_layout Plain Layout
\backslash
phantomsection
\end_layout
\begin_layout Plain Layout
\backslash
addcontentsline{toc}{chapter}{Acknowledgements}
\end_layout
\end_inset
\end_layout
\begin_layout Section*
\begin_inset ERT
status collapsed
\begin_layout Plain Layout
\backslash
hspace*{
\backslash
stretch{1}}
\end_layout
\end_inset
Acknowledgements
\begin_inset ERT
status collapsed
\begin_layout Plain Layout
\backslash
hspace*{
\backslash
stretch{1}}
\end_layout
\end_inset
\end_layout
\begin_layout Standard
My path through graduate school has been a long and winding one, and I am
grateful to all the mentors I have had through the years – Drs.
Terry Gaasterland, Daniel Salomon, and Andrew Su – all of whose encouragement
and support have been vital to my development into the scientist I am today.
I am also thankful for my collaborators in the Salomon lab: Drs.
Sarah Lamere, Sunil Kurian, Thomas Whisenant, Padmaja Natarajan, Katie
Podshivalova, and Heather Kiyomi Komori; as well as the many other lab
members I have worked with in small ways over the years.
In addition, Steven Head, Dr.
Phillip Ordoukhanian, and Terri Gelbart from the Scripps Genomics core
have also been instrumental in supporting my work.
And of course, I am thankful for the guidance and expertise provided by
my committee, Drs.
Nicholas Schork, Ali Torkamani, Michael Petrascheck, and Luc Teyton.
\end_layout
\begin_layout Standard
Finally, I wish to thank my parents, for instilling in me a love of science
and learning from an early age and encouraging me to pursue that love as
a career as I grew up.
I am truly lucky to have such a loving and supportive family.
\end_layout
\begin_layout Standard
\begin_inset Newpage newpage
\end_inset
\end_layout
\begin_layout Standard
\begin_inset CommandInset toc
LatexCommand tableofcontents
\end_inset
\end_layout
\begin_layout Standard
\begin_inset FloatList table
\end_inset
\end_layout
\begin_layout Standard
\begin_inset FloatList figure
\end_inset
\end_layout
\begin_layout Standard
\begin_inset Note Note
status collapsed
\begin_layout Plain Layout
To create a new abbreviation:
\end_layout
\begin_layout Enumerate
Add an entry to abbrevs.tex
\end_layout
\begin_layout Enumerate
Wrap every occurrence of the term in Insert -> Custom Insets -> Glossary
Term (use appropriate variants for caiptal, plural, etc.), using Edit ->
Find & Replace (Advanced).
Skip section headers and float captions.
\end_layout
\begin_layout Plain Layout
\begin_inset CommandInset href
LatexCommand href
target "https://ctan.org/pkg/glossaries?lang=en"
literal "false"
\end_inset
\begin_inset CommandInset href
LatexCommand href
target "https://ctan.org/pkg/glossaries-extra"
literal "false"
\end_inset
\end_layout
\end_inset
\end_layout
\begin_layout Standard
\begin_inset ERT
status collapsed
\begin_layout Plain Layout
\backslash
renewcommand*{
\backslash
glossaryname}{List of Abbreviations}%
\end_layout
\begin_layout Plain Layout
\backslash
printglossaries
\end_layout
\end_inset
\end_layout
\begin_layout List of TODOs
\end_layout
\begin_layout Chapter*
Abstract
\begin_inset ERT
status collapsed
\begin_layout Plain Layout
\backslash
addcontentsline{toc}{chapter}{Abstract}
\end_layout
\end_inset
\end_layout
\begin_layout Standard
\begin_inset Note Note
status collapsed
\begin_layout Plain Layout
It is included as an integral part of the thesis and should immediately
precede the introduction.
\end_layout
\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.
\end_layout
\begin_layout Itemize
Include pertinent place names, names of persons (in full), and other proper
nouns.
These are useful in automated retrieval.
\end_layout
\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.
\end_layout
\begin_layout Itemize
Do not include graphs, charts, tables, or illustrations in your abstract.
\end_layout
\end_inset
\end_layout
\begin_layout Standard
Transplant rejection mediated by adaptive immune response is the major challenge
to long-term graft survival.
Rejection is treated with immune suppressive drugs, but early diagnosis
is essential for effective treatment.
Memory lymphocytes are known to resist immune suppression, but the precise
regulatory mechanisms underlying immune memory are still poorly understood.
High-throughput genomic assays such as microarrays, RNA-seq, and ChIP-seq
are heavily used in the study of immunology and transplant rejection.
Here we present 3 analyses of such assays in this context.
First, we re-analyze a large data set consisting of H3K4me2, H3K4me3, and
H3K27me3 ChIP-seq data and RNA-seq data in naïve and memory CD4
\begin_inset Formula $^{+}$
\end_inset
T-cells using modern bioinformatics methods designed to address deficiencies
in the data and extend the analysis in several new directions.
All 3 histone marks are found to occur in broad regions and are enriched
near promoters, but the radius of promoter enrichment is found to be larger
for H3K27me3.
We observe that both gene expression and promoter histone methylation in
naïve and memory cells converges on a common signature 14 days after activation
, consistent with differentiation of naïve cells into memory cells.
The location of histone modifications within the promoter is also found
to be important, with asymmetric associations with gene expression for
peaks located the same distance up- or downstream of the TSS.
Second, we demonstrate the effectiveness of fRMA as a single-channel normalizat
ion for using expression arrays to diagnose transplant rejection in a clinical
diagnostic setting, and we develop a custom fRMA normalization for a previously
unsupported array platform.
For methylation arrays, we adapt methods designed for RNA-seq to improve
the sensitivity of differential methylation analysis by modeling the heterosked
asticity inherent in the data.
Finally, we present and validate a novel method for RNA-seq of cynomolgus
monkey blood samples using complementary oligonucleotides to prevent wasteful
over-sequencing of globin genes.
These results all demonstrate the usefulness of a toolbox full of flexible
and modular analysis methods in analyzing complex high-throughput assays
in contexts ranging from basic science to translational medicine.
\end_layout
\begin_layout Standard
\begin_inset Note Note
status collapsed
\begin_layout Chapter*
Notes to draft readers
\end_layout
\begin_layout Plain Layout
Thank you so much for agreeing to read my thesis and give me feedback on
it.
What you are currently reading is a rough draft, in need of many revisions.
You can always find the latest version at
\begin_inset CommandInset href
LatexCommand href
target "https://mneme.dedyn.io/~ryan/Thesis/thesis.pdf"
literal "false"
\end_inset
.
the PDF at this link is updated periodically with my latest revisions,
but you can just download the current version and give me feedback on that.
Don't worry about keeping up with the updates.
\end_layout
\begin_layout Plain Layout
As for what feedback I'm looking for, first of all, don't waste your time
marking spelling mistakes and such.
I haven't run a spell checker on it yet, so let me worry about that.
Also, I'm aware that many abbreviations are not properly introduced the
first time they are used, so don't worry about that either.
However, if you see any glaring formatting issues, such as a figure being
too large and getting cut off at the edge of the page, please note them.
In addition, if any of the text in the figures is too small, please note
that as well.
\end_layout
\begin_layout Plain Layout
Beyond that, what I'm mainly interested in is feedback on the content.
For example: does the introduction flow logically, and does it provide
enough background to understand the other chapters? Does each chapter make
it clear what work and analyses I have done? Do the figures clearly communicate
the results I'm trying to show? Do you feel that the claims in the results
and discussion sections are well-supported? There's no need to suggest
improvements; just note areas that you feel need improvement.
Additionally, if you notice any un-cited claims in any chapter, please
flag them for my attention.
Similarly, if you discover any factual errors, please note them as well.
\end_layout
\begin_layout Plain Layout
You can provide your feedback in whatever way is most convenient to you.
You could mark up this PDF with highlights and notes, then send it back
to me.
Or you could collect your comments in a separate text file and send that
to me, or whatever else you like.
However, if you send me your feedback in a separate document, please note
a section/figure/table number for each comment, and
\emph on
also
\emph default
send me the exact PDF that you read so I can reference it while reading
your comments, since as mentioned above, the current version I'm working
on will have changed by that point (which might include shuffling sections
and figures around, changing their numbers).
One last thing: you'll see a bunch of text in orange boxes throughout the
PDF.
These are notes to myself about things that need to be fixed later, so
if you see a problem noted in an orange box, that means I'm already aware
of it, and there's no need to comment on it.
\end_layout
\begin_layout Plain Layout
My thesis is due Thursday, October 10th, so in order to be useful to me,
I'll need your feedback at least several days before that, ideally by Monday,
October 7th.
If you have limited time and are unable to get through the whole thesis,
please focus your efforts on Chapters 1 and 2, since those are the roughest
and most in need of revision.
Chapter 3 is fairly short and straightforward, and Chapter 4 is an adaptation
of a paper that's already been through a few rounds of revision, so they
should be a lot tighter.
If you can't spare any time between now and then, or if something unexpected
comes up, I understand.
Just let me know.
\end_layout
\begin_layout Plain Layout
Thanks again for your help, and happy reading!
\end_layout
\end_inset
\end_layout
\begin_layout Standard
\begin_inset ERT
status open
\begin_layout Plain Layout
\backslash
mainmatter
\end_layout
\end_inset
\begin_inset Note Note
status open
\begin_layout Plain Layout
Switch from roman numerals to arabic for page numbers.
\end_layout
\end_inset
\end_layout
\begin_layout Chapter
Introduction
\end_layout
\begin_layout Standard
\begin_inset ERT
status collapsed
\begin_layout Plain Layout
\backslash
glsresetall
\end_layout
\end_inset
\begin_inset Note Note
status collapsed
\begin_layout Plain Layout
Reintroduce all abbreviations
\end_layout
\end_inset
\end_layout
\begin_layout Section
\begin_inset CommandInset label
LatexCommand label
name "sec:Biological-motivation"
\end_inset
Biological motivation
\end_layout
\begin_layout Standard
\begin_inset Flex TODO Note (inline)
status open
\begin_layout Plain Layout
Find some figures to include even if permission is not obtained.
Try to obtain permission, and if it cannot be obtained, remove/replace
them later.
\end_layout
\end_inset
\end_layout
\begin_layout Standard
\begin_inset Flex TODO Note (inline)
status open
\begin_layout Plain Layout
Rethink the subsection organization after the intro is written.
\end_layout
\end_inset
\end_layout
\begin_layout Subsection
Rejection is the major long-term threat to organ and tissue allografts
\end_layout
\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 – a donor – is required.
This is referred to as an allograft
\begin_inset CommandInset citation
LatexCommand cite
key "Valenzuela2017"
literal "false"
\end_inset
.
\end_layout
\begin_layout Standard
Because an allograft comes from a donor of the same species who is genetically
distinct from the recipient (with rare exceptions), genetic variants in
protein-coding regions affect the polypeptide sequences encoded by the
affected genes, resulting in protein products in the allograft that differ
from the equivalent proteins produced by the graft recipient's own tissue.
As a result, without intervention, the recipient's immune system will eventuall
y identify the graft as foreign tissue and begin attacking it.
This is called an alloimmune response, and if left unchecked, it eventually
results in failure and death of the graft, a process referred to as transplant
rejection
\begin_inset CommandInset citation
LatexCommand cite
key "Murphy2012"
literal "false"
\end_inset
.
Rejection is the primary obstacle to long-term health and survival of an
allograft
\begin_inset CommandInset citation
LatexCommand cite
key "Valenzuela2017"
literal "false"
\end_inset
.
Like any adaptive immune response, an alloimmune response generally occurs
via two broad mechanisms: cellular immunity, in which CD8
\begin_inset Formula $^{+}$
\end_inset
T-cells recognizing graft-specific antigens induce apoptosis in the graft
cells; and humoral immunity, in which B-cells produce antibodies that bind
to graft proteins and direct an immune response against the graft
\begin_inset CommandInset citation
LatexCommand cite
key "Murphy2012"
literal "false"
\end_inset
.
In either case, alloimmunity and rejection show most of the typical hallmarks
of an adaptive immune response, in particular mediation by CD4
\begin_inset Formula $^{+}$
\end_inset
T-cells and formation of immune memory.
\end_layout
\begin_layout Subsection
Diagnosis and treatment of allograft rejection is a major challenge
\end_layout
\begin_layout Standard
To prevent rejection, allograft recipients are treated with immune suppressive
drugs
\begin_inset CommandInset citation
LatexCommand cite
key "Kowalski2003,Murphy2012"
literal "false"
\end_inset
.
The goal is to achieve sufficient suppression of the immune system to prevent
rejection of the graft without compromising the ability of the immune system
to raise a normal response against infection.
As such, a delicate balance must be struck: insufficient immune suppression
may lead to rejection and ultimately loss of the graft; excessive suppression
leaves the patient vulnerable to life-threatening opportunistic infections
\begin_inset CommandInset citation
LatexCommand cite
key "Murphy2012"
literal "false"
\end_inset
.
Because every patient's matabolism is different, achieving this delicate
balance requires drug dosage to be tailored for each patient.
Furthermore, dosage must be tuned over time, as the immune system's activity
varies over time and in response to external stimuli with no fixed pattern.
In order to properly adjust the dosage of immune suppression drugs, it
is necessary to monitor the health of the transplant and increase the dosage
if evidence of rejection or alloimmune activity is observed.
\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
\begin_inset CommandInset citation
LatexCommand cite
key "Israeli2007"
literal "false"
\end_inset
.
The current gold standard test for graft rejection is a tissue biopsy,
examined for visible signs of rejection by a trained histologist
\begin_inset CommandInset citation
LatexCommand cite
key "Kurian2014"
literal "false"
\end_inset
.
When a patient shows symptoms of possible rejection, a
\begin_inset Quotes eld
\end_inset
for cause
\begin_inset Quotes erd
\end_inset
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
\begin_inset Quotes eld
\end_inset
sub-clinical
\begin_inset Quotes erd
\end_inset
rejection.
In light of this, is is now common to perform
\begin_inset Quotes eld
\end_inset
protocol biopsies
\begin_inset Quotes erd
\end_inset
at specific times after transplantation of a graft, even if no symptoms
of rejection are apparent, in addition to
\begin_inset Quotes eld
\end_inset
for cause
\begin_inset Quotes erd
\end_inset
biopsies
\begin_inset CommandInset citation
LatexCommand cite
key "Salomon2002,Wilkinson2006,Patel2018,Zachariah2018"
literal "false"
\end_inset
.
\end_layout
\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
\begin_inset CommandInset citation
LatexCommand cite
key "Kurian2014"
literal "false"
\end_inset
.
In marginal cases, two different histologists may give two different diagnoses
to the same biopsy.
Second, a biopsy can only evaluate if rejection is occurring in the section
of the graft from which the tissue was extracted.
If rejection is localized to one section of the graft and the tissue is
extracted from a different section, a false negative diagnosis may result.
Most importantly, extraction of tissue from a graft is invasive and is
treated as an injury by the body, which results in inflammation that in
turn promotes increased immune system activity.
Hence, the invasiveness of biopsies severely limits the frequency with
which they can safely be performed
\begin_inset CommandInset citation
LatexCommand cite
key "Patel2018"
literal "false"
\end_inset
.
Typically, protocol biopsies are not scheduled more than about once per
month
\begin_inset CommandInset citation
LatexCommand cite
key "Wilkinson2006"
literal "false"
\end_inset
.
A less invasive diagnostic test for rejection would bring manifold benefits.
Such a test would enable more frequent testing and therefore earlier detection
of rejection events.
In addition, having a larger pool of historical data for a given patient
would make it easier to evaluate when a given test is outside the normal
parameters for that specific patient, rather than relying on normal ranges
for the population as a whole.
Lastly, the accumulated data from more frequent tests would be a boon to
the transplant research community.
Beyond simply providing more data overall, the better time granularity
of the tests will enable studying the progression of a rejection event
on the scale of days to weeks, rather than months.
\end_layout
\begin_layout Subsection
Memory cells are resistant to immune suppression
\end_layout
\begin_layout Standard
One of the defining features of the adaptive immune system is immune memory:
the ability of the immune system to recognize a previously encountered
foreign antigen and respond more quickly and more strongly to that antigen
in subsequent encounters
\begin_inset CommandInset citation
LatexCommand cite
key "Murphy2012"
literal "false"
\end_inset
.
When the immune system first encounters a new antigen, the T-cells that
respond are known as naïve cells – T-cells that have never detected their
target antigens before.
Once activated by their specific antigen presented by an antigen-presenting
cell in the proper co-stimulatory context, naïve cells differentiate into
effector cells that carry out their respective functions in targeting and
destroying the source of the foreign antigen.
The
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
TCR
\end_layout
\end_inset
is cell-surface protein complex produced by T-cells that is responsible
for recognizing the T-cell's specific antigen, presented on a
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
MHC
\end_layout
\end_inset
, the cell-surface protein complex used by an
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
APC
\end_layout
\end_inset
to present antigens to the T-cell.
However, a naïve T-cell that recognizes its antigen also requires a co-stimulat
ory signal, delivered through other interactions between
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
APC
\end_layout
\end_inset
surface proteins and T-cell surface proteins such as CD28.
Without proper co-stimulation, a T-cell that recognizes its antigen either
dies or enters an unresponsive state known as anergy, in which the T-cell
becomes much more resistant to subsequent activation even with proper co-stimul
ation.
The dependency of activation on co-stimulation is an important feature
of naïve lymphocytes that limits
\begin_inset Quotes eld
\end_inset
false positive
\begin_inset Quotes erd
\end_inset
immune responses against self antigens, because
\begin_inset Flex Glossary Term (pl)
status open
\begin_layout Plain Layout
APC
\end_layout
\end_inset
usually only express the proper co-stimulation after the innate immune
system detects signs of an active infection, such as the presence of common
bacterial cell components or inflamed tissue.
\end_layout
\begin_layout Standard
After the foreign antigen is cleared, most effector cells die since they
are no longer needed, but some differentiate into memory cells and remain
alive indefinitely.
Like naïve cells, memory cells respond to detection of their specific antigen
by differentiating into effector cells, ready to fight an infection
\begin_inset CommandInset citation
LatexCommand cite
key "Murphy2012"
literal "false"
\end_inset
.
However, the memory response to antigen is qualitatively different: memory
cells are more sensitive to detection of their antigen, and a lower concentrati
on of antigen is suffiicient to activate them
\begin_inset CommandInset citation
LatexCommand cite
key "Rogers2000,London2000,Berard2002"
literal "false"
\end_inset
.
In addition, memory cells are much less dependent on co-stimulation for
activation: they can activate without certain co-stimulatory signals that
are required by naïve cells, and the signals they do require are only required
at lower levels in order to cause activation
\begin_inset CommandInset citation
LatexCommand cite
key "London2000"
literal "false"
\end_inset
.
Furthermore, mechanisms that induce tolerance (non-response to antigen)
in naïve cells are much less effective on memory cells
\begin_inset CommandInset citation
LatexCommand cite
key "London2000"
literal "false"
\end_inset
.
Lastly, once activated, memory cells proliferate and differentiate into
effector cells more quickly than naïve cells do
\begin_inset CommandInset citation
LatexCommand cite
key "Berard2002"
literal "false"
\end_inset
.
In combination, these changes in lymphocyte behavior upon differentiation
into memory cells account for the much quicker and stronger response of
the immune system to subsequent exposure to a previously-encountered antigen.
\end_layout
\begin_layout Standard
In the context of a pathogenic infection, immune memory is a major advantage,
allowing an organism to rapidly fight off a previously encountered pathogen
much more quickly and effectively than the first time it was encountered
\begin_inset CommandInset citation
LatexCommand cite
key "Murphy2012"
literal "false"
\end_inset
.
However, if effector cells that recognize an antigen from an allograft
are allowed to differentiate into memory cells, preventing rejection of
the graft becomes much more difficult.
Many immune suppression drugs work by interfering with the co-stimulation
that naïve cells require in order to mount an immune response.
Since memory cells do not require the same degree of co-stimulation, these
drugs are not effective at suppressing an immune response that is mediated
by memory cells.
Secondly, because memory cells are able to mount a stronger and faster
response to an antigen, all else being equal stronger immune suppression
is required to prevent an immune response mediated by memory cells.
\end_layout
\begin_layout Standard
However, immune suppression affects the entire immune system, not just cells
recognizing a specific antigen, so increasing the dosage of immune suppression
drugs also increases the risk of complications from a compromised immune
system, such as opportunistic infections
\begin_inset CommandInset citation
LatexCommand cite
key "Murphy2012"
literal "false"
\end_inset
.
While the differences in cell surface markers between naïve and memory
cells have been fairly well characterized, the internal regulatory mechanisms
that allow memory cells to respond more quickly and without co-stimulation
are still poorly understood.
In order to develop methods of immune suppression that either prevent the
formation of memory cells or work more effectively against memory cells,
a more complete understanding of the mechanisms of immune memory formation
and regulation is required.
\end_layout
\begin_layout Subsection
Infusion of allogenic mesenchymal stem cells modulates the alloimmune response
\end_layout
\begin_layout Standard
One promising experimental treatment for transplant rejection involves the
infusion of allogenic
\begin_inset Flex Glossary Term (pl)
status open
\begin_layout Plain Layout
MSC
\end_layout
\end_inset
.
\begin_inset Flex Glossary Term (pl)
status open
\begin_layout Plain Layout
MSC
\end_layout
\end_inset
have been shown to have immune modulatory effects, both in general and
specifically in the case of immune responses against allografts
\begin_inset CommandInset citation
LatexCommand cite
key "LeBlanc2003,Aggarwal2005,Bartholomew2009,Berman2010"
literal "false"
\end_inset
.
Furthermore, allogenic
\begin_inset Flex Glossary Term (pl)
status open
\begin_layout Plain Layout
MSC
\end_layout
\end_inset
themselves are immune-evasive and are rejected by the recipient's immune
system more slowly than most allogenic tissues
\begin_inset CommandInset citation
LatexCommand cite
key "Ankrum2014,Berglund2017"
literal "false"
\end_inset
.
In addition, treating
\begin_inset Flex Glossary Term (pl)
status open
\begin_layout Plain Layout
MSC
\end_layout
\end_inset
in culture with
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
IFNg
\end_layout
\end_inset
is shown to enhance their immunosuppressive properties and homogenize their
cellulat phenotype, making them more amenable to development into a well-contro
lled treatment
\begin_inset CommandInset citation
LatexCommand cite
key "Majumdar2003,Ryan2007"
literal "false"
\end_inset
.
The mechanisms by which
\begin_inset Flex Glossary Term (pl)
status open
\begin_layout Plain Layout
MSC
\end_layout
\end_inset
modulate the immune system are still poorly understood.
Despite this, there is signifcant interest in using
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
IFNg
\end_layout
\end_inset
-activated
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
MSC
\end_layout
\end_inset
infusion as a supplementary immune suppressive treatment for allograft
transplantation.
\end_layout
\begin_layout Standard
Note that despite the name, none of the above properties of
\begin_inset Flex Glossary Term (pl)
status open
\begin_layout Plain Layout
MSC
\end_layout
\end_inset
are believed to involve their ability as stem cells to differentiate into
multiple different mature cell types, but rather the intercellular signals
they produce
\begin_inset CommandInset citation
LatexCommand cite
key "Ankrum2014"
literal "false"
\end_inset
.
\end_layout
\begin_layout Standard
\begin_inset Flex TODO Note (inline)
status open
\begin_layout Plain Layout
An overview of high-throughput assays would have been nice to have, but
it's a bit late now.
\end_layout
\end_inset
\end_layout
\begin_layout Section
\begin_inset CommandInset label
LatexCommand label
name "sec:Overview-of-bioinformatic"
\end_inset
Overview of bioinformatic analysis methods
\end_layout
\begin_layout Standard
The studies presented in this work all involve the analysis of high-throughput
genomic and epigenomic assay data.
Assays like microarrays and
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
HTS
\end_layout
\end_inset
are powerful methods for interrogating gene expression and epigenetic state
across the entire genome.
However, these data present many unique analysis challenges, and proper
analysis requires identifying and exploiting genome-wide trends in the
data to make up for the small sample sizes.
A wide array of software tools is available to analyze these data.
This section presents an overview of the most important methods and tools
used throughout the following analyses, including what problems they solve,
what assumptions they make, and a basic description of how they work.
\end_layout
\begin_layout Subsection
\begin_inset Flex Code
status open
\begin_layout Plain Layout
Limma
\end_layout
\end_inset
: The standard linear modeling framework for genomics
\end_layout
\begin_layout Standard
Linear models are a generalization of the
\begin_inset Formula $t$
\end_inset
-test and ANOVA to arbitrarily complex experimental designs
\begin_inset CommandInset citation
LatexCommand cite
key "chambers:1992"
literal "false"
\end_inset
.
In a typical linear model, there is one dependent variable observation
per sample and a large number of samples.
For example, in a linear model of height as a function of age and sex,
there is one height measurement per person.
However, when analyzing genomic data, each sample consists of observations
of thousands of dependent variables.
For example, in a
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
RNA-seq
\end_layout
\end_inset
experiment, the dependent variables may be the count of
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
RNA-seq
\end_layout
\end_inset
reads for each annotated gene, and there are tens of thousands of genes
in the human genome.
Since many assays measure other things than gene expression, the abstract
term
\begin_inset Quotes eld
\end_inset
feature
\begin_inset Quotes erd
\end_inset
is used to refer to each dependent variable being measured, which may include
any genomic element, such as genes, promoters, peaks, enhancers, exons,
etc.
\end_layout
\begin_layout Standard
The simplest approach to analyzing such data would be to fit the same model
independently to each feature.
However, this is undesirable for most genomics data sets.
Genomics assays like
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
HTS
\end_layout
\end_inset
are expensive, and often the process of generating the samples is also
quite expensive and time-consuming.
This expense limits the sample sizes typically employed in genomics experiments
, so a typical genomic data set has far more features being measured than
observations (samples) per feature.
As a result, the statistical power of the linear model for each individual
feature is likewise limited by the small number of samples.
However, because thousands of features from the same set of samples are
analyzed together, there is an opportunity to improve the statistical power
of the analysis by exploiting shared patterns of variation across features.
This is the core feature of
\begin_inset Flex Code
status open
\begin_layout Plain Layout
limma
\end_layout
\end_inset
, a linear modeling framework designed for genomic data.
\begin_inset Flex Code
status open
\begin_layout Plain Layout
Limma
\end_layout
\end_inset
is typically used to analyze expression microarray data, and more recently
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
RNA-seq
\end_layout
\end_inset
data, but it can also be used to analyze any other data for which linear
modeling is appropriate.
\end_layout
\begin_layout Standard
The central challenge when fitting a linear model is to estimate the variance
of the data accurately.
Out of all parameters required to evaluate statistical significance of
an effect, the variance is the most difficult to estimate when sample sizes
are small.
A single shared variance could be estimated for all of the features together,
and this estimate would be very stable, in contrast to the individual feature
variance estimates.
However, this would require the assumption that all features have equal
variance, which is known to be false for most genomic data sets (for example,
some genes' expression is known to be more variable than others').
\begin_inset Flex Code
status open
\begin_layout Plain Layout
Limma
\end_layout
\end_inset
offers a compromise between these two extremes by using a method called
empirical Bayes moderation to
\begin_inset Quotes eld
\end_inset
squeeze
\begin_inset Quotes erd
\end_inset
the distribution of estimated variances toward a single common value that
represents the variance of an average feature in the data (Figure
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:ebayes-example"
plural "false"
caps "false"
noprefix "false"
\end_inset
)
\begin_inset CommandInset citation
LatexCommand cite
key "Smyth2004"
literal "false"
\end_inset
.
While the individual feature variance estimates are not stable, the common
variance estimate for the entire data set is quite stable, so using a combinati
on of the two yields a variance estimate for each feature with greater precision
than the individual feature variances.
The trade-off for this improvement is that squeezing each estimated variance
toward the common value introduces some bias – the variance will be underestima
ted for features with high variance and overestimated for features with
low variance.
Essentially,
\begin_inset Flex Code
status open
\begin_layout Plain Layout
limma
\end_layout
\end_inset
assumes that extreme variances are less common than variances close to
the common value.
The squeezed variance estimates from this empirical Bayes procedure are
shown empirically to yield greater statistical power than either the individual
feature variances or the single common value.
\end_layout
\begin_layout Standard
\begin_inset Float figure
wide false
sideways false
status collapsed
\begin_layout Plain Layout
\align center
\begin_inset Graphics
filename graphics/Intro/eBayes-CROP-RASTER.png
lyxscale 25
width 100col%
groupId colwidth-raster
\end_inset
\end_layout
\begin_layout Plain Layout
\begin_inset Caption Standard
\begin_layout Plain Layout
\begin_inset Argument 1
status collapsed
\begin_layout Plain Layout
Example of empirical Bayes squeezing of per-gene variances.
\end_layout
\end_inset
\begin_inset CommandInset label
LatexCommand label
name "fig:ebayes-example"
\end_inset
\series bold
Example of empirical Bayes squeezing of per-gene variances.
\series default
A smooth trend line (red) is fitted to the individual gene variances (light
blue) as a function of average gene abundance (logCPM).
Then the individual gene variances are
\begin_inset Quotes eld
\end_inset
squeezed
\begin_inset Quotes erd
\end_inset
toward the trend (dark blue).
\end_layout
\end_inset
\end_layout
\begin_layout Plain Layout
\end_layout
\end_inset
\end_layout
\begin_layout Standard
On top of this core framework,
\begin_inset Flex Code
status open
\begin_layout Plain Layout
limma
\end_layout
\end_inset
also implements many other enhancements that, further relax the assumptions
of the model and extend the scope of what kinds of data it can analyze.
Instead of squeezing toward a single common variance value,
\begin_inset Flex Code
status open
\begin_layout Plain Layout
limma
\end_layout
\end_inset
can model the common variance as a function of a covariate, such as average
expression
\begin_inset CommandInset citation
LatexCommand cite
key "Law2014"
literal "false"
\end_inset
.
This is essential for
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
RNA-seq
\end_layout
\end_inset
data, where higher gene counts yield more precise expression measurements
and therefore smaller variances than low-count genes.
While linear models typically assume that all samples have equal variance,
\begin_inset Flex Code
status open
\begin_layout Plain Layout
limma
\end_layout
\end_inset
is able to relax this assumption by identifying and down-weighting samples
that diverge more strongly from the linear model across many features
\begin_inset CommandInset citation
LatexCommand cite
key "Ritchie2006,Liu2015"
literal "false"
\end_inset
.
In addition,
\begin_inset Flex Code
status open
\begin_layout Plain Layout
limma
\end_layout
\end_inset
is also able to fit simple mixed models incorporating one random effect
in addition to the fixed effects represented by an ordinary linear model
\begin_inset CommandInset citation
LatexCommand cite
key "Smyth2005a"
literal "false"
\end_inset
.
Once again,
\begin_inset Flex Code
status open
\begin_layout Plain Layout
limma
\end_layout
\end_inset
shares information between features to obtain a robust estimate for the
random effect correlation.
\end_layout
\begin_layout Subsection
\begin_inset Flex Code
status open
\begin_layout Plain Layout
edgeR
\end_layout
\end_inset
provides
\begin_inset Flex Code
status open
\begin_layout Plain Layout
limma
\end_layout
\end_inset
-like analysis features for read count data
\end_layout
\begin_layout Standard
Although
\begin_inset Flex Code
status open
\begin_layout Plain Layout
limma
\end_layout
\end_inset
can be applied to read counts from
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
RNA-seq
\end_layout
\end_inset
data, it is less suitable for counts from
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
ChIP-seq
\end_layout
\end_inset
and other sources, which tend to be much smaller and therefore violate
the assumption of a normal distribution more severely.
For all count-based data, the
\begin_inset Flex Code
status open
\begin_layout Plain Layout
edgeR
\end_layout
\end_inset
package works similarly to
\begin_inset Flex Code
status open
\begin_layout Plain Layout
limma
\end_layout
\end_inset
, but uses a
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
GLM
\end_layout
\end_inset
instead of a linear model.
Relative to a linear model, a
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
GLM
\end_layout
\end_inset
gains flexibility by relaxing several assumptions, the most important of
which is the assumption of normally distributed errors.
This allows the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
GLM
\end_layout
\end_inset
in
\begin_inset Flex Code
status open
\begin_layout Plain Layout
edgeR
\end_layout
\end_inset
to model the counts directly using a
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
NB
\end_layout
\end_inset
distribution rather than modeling the normalized log counts using a normal
distribution as
\begin_inset Flex Code
status open
\begin_layout Plain Layout
limma
\end_layout
\end_inset
does
\begin_inset CommandInset citation
LatexCommand cite
key "Chen2014,McCarthy2012,Robinson2010a"
literal "false"
\end_inset
.
\end_layout
\begin_layout Standard
The
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
NB
\end_layout
\end_inset
distribution is a good fit for count data because it can be derived as
a gamma-distributed mixture of Poisson distributions.
The reads in an
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
RNA-seq
\end_layout
\end_inset
sample are assumed to be sampled from a much larger population, such that
the sampling process does not significantly affect the proportions.
Under this assumption, a gene's read count in an
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
RNA-seq
\end_layout
\end_inset
sample is distributed as
\begin_inset Formula $\mathrm{Binomial}(n,p)$
\end_inset
, where
\begin_inset Formula $n$
\end_inset
is the total number of reads sequenced from the sample and
\begin_inset Formula $p$
\end_inset
is the proportion of total fragments in the sample derived from that gene.
When
\begin_inset Formula $n$
\end_inset
is large and
\begin_inset Formula $p$
\end_inset
is small, a
\begin_inset Formula $\mathrm{Binomial}(n,p)$
\end_inset
distribution is well-approximated by
\begin_inset Formula $\mathrm{Poisson}(np)$
\end_inset
.
Hence, if multiple sequencing runs are performed on the same
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
RNA-seq
\end_layout
\end_inset
sample (with the same gene mixing proportions each time), each gene's read
count is expected to follow a Poisson distribution.
If the abundance of a gene,
\begin_inset Formula $p,$
\end_inset
varies across biological replicates according to a gamma distribution,
and
\begin_inset Formula $n$
\end_inset
is held constant, then the result is a gamma-distributed mixture of Poisson
distributions, which is equivalent to the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
NB
\end_layout
\end_inset
distribution.
The assumption of a gamma distribution for the mixing weights is arbitrary,
motivated by the convenience of the numerically tractable
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
NB
\end_layout
\end_inset
distribution and the need to select
\emph on
some
\emph default
distribution, since the true shape of the distribution of biological variance
is unknown.
\end_layout
\begin_layout Standard
Thus,
\begin_inset Flex Code
status open
\begin_layout Plain Layout
edgeR
\end_layout
\end_inset
's use of the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
NB
\end_layout
\end_inset
is equivalent to an
\emph on
a priori
\emph default
assumption that the variation in gene abundances between replicates follows
a gamma distribution.
The gamma shape parameter in the context of the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
NB
\end_layout
\end_inset
is called the dispersion, and the square root of this dispersion is referred
to as the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
BCV
\end_layout
\end_inset
, since it represents the variability in abundance that was present in the
biological samples prior to the Poisson
\begin_inset Quotes eld
\end_inset
noise
\begin_inset Quotes erd
\end_inset
that was generated by the random sampling of reads in proportion to feature
abundances.
Like
\begin_inset Flex Code
status open
\begin_layout Plain Layout
limma
\end_layout
\end_inset
,
\begin_inset Flex Code
status open
\begin_layout Plain Layout
edgeR
\end_layout
\end_inset
estimates the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
BCV
\end_layout
\end_inset
for each feature using an empirical Bayes procedure that represents a compromis
e between per-feature dispersions and a single pooled dispersion estimate
shared across all features.
For differential abundance testing,
\begin_inset Flex Code
status open
\begin_layout Plain Layout
edgeR
\end_layout
\end_inset
offers a likelihood ratio test based on the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
NB
\end_layout
\end_inset
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
GLM
\end_layout
\end_inset
.
However, this test assumes the dispersion parameter is known exactly rather
than estimated from the data, which can result in overstating the significance
of differential abundance results.
More recently, a quasi-likelihood test has been introduced that properly
factors the uncertainty in dispersion estimation into the estimates of
statistical significance, and this test is recommended over the likelihood
ratio test in most cases
\begin_inset CommandInset citation
LatexCommand cite
key "Lund2012"
literal "false"
\end_inset
.
\end_layout
\begin_layout Subsection
Calling consensus peaks from ChIP-seq data
\end_layout
\begin_layout Standard
Unlike
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
RNA-seq
\end_layout
\end_inset
data, in which gene annotations provide a well-defined set of discrete
genomic regions in which to count reads,
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
ChIP-seq
\end_layout
\end_inset
reads can potentially occur anywhere in the genome.
However, most genome regions will not contain significant
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
ChIP-seq
\end_layout
\end_inset
read coverage, and analyzing every position in the entire genome is statistical
ly and computationally infeasible, so it is necessary to identify regions
of interest inside which
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
ChIP-seq
\end_layout
\end_inset
reads will be counted and analyzed.
One option is to define a set of interesting regions
\emph on
a priori
\emph default
, for example by defining a promoter region for each annotated gene.
However, it is also possible to use the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
ChIP-seq
\end_layout
\end_inset
data itself to identify regions with
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
ChIP-seq
\end_layout
\end_inset
read coverage significantly above the background level, known as peaks.
\end_layout
\begin_layout Standard
The challenge in peak calling is that the immunoprecipitation step is not
100% selective, so some fraction of reads are
\emph on
not
\emph default
derived from DNA fragments that were bound by the immunoprecipitated protein.
These are referred to as background reads.
Biases in amplification and sequencing, as well as the aforementioned Poisson
randomness of the sequencing itself, can cause fluctuations in the background
level of reads that resemble peaks, and the true peaks must be distinguished
from these.
It is common to sequence the input DNA to the ChIP-seq reaction alongside
the immunoprecipitated product in order to aid in estimating the fluctuations
in background level across the genome.
\end_layout
\begin_layout Standard
There are generally two kinds of peaks that can be identified: narrow peaks
and broadly enriched regions.
Proteins that bind specific sites in the genome (such as many transcription
factors) typically show most of their
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
ChIP-seq
\end_layout
\end_inset
read coverage at these specific sites and very little coverage anywhere
else.
Because the footprint of the protein is consistent wherever it binds, each
peak has a consistent width, typically tens to hundreds of base pairs,
representing the length of DNA that it binds to.
Algorithms like
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
MACS
\end_layout
\end_inset
exploit this pattern to identify specific loci at which such
\begin_inset Quotes eld
\end_inset
narrow peaks
\begin_inset Quotes erd
\end_inset
occur by looking for the characteristic peak shape in the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
ChIP-seq
\end_layout
\end_inset
coverage rising above the surrounding background coverage
\begin_inset CommandInset citation
LatexCommand cite
key "Zhang2008"
literal "false"
\end_inset
.
In contrast, some proteins, chief among them histones, do not bind only
at a small number of specific sites, but rather bind potentially almost
everywhere in the entire genome.
When looking at histone marks, adjacent histones tend to be similarly marked,
and a given mark may be present on an arbitrary number of consecutive histones
along the genome.
Hence, there is no consistent
\begin_inset Quotes eld
\end_inset
footprint size
\begin_inset Quotes erd
\end_inset
for
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
ChIP-seq
\end_layout
\end_inset
peaks based on histone marks, and peaks typically span many histones.
Hence, typical peaks span many hundreds or even thousands of base pairs.
Instead of identifying specific loci of strong enrichment, algorithms like
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
SICER
\end_layout
\end_inset
assume that peaks are represented in the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
ChIP-seq
\end_layout
\end_inset
data by modest enrichment above background occurring across broad regions,
and they attempt to identify the extent of those regions
\begin_inset CommandInset citation
LatexCommand cite
key "Zang2009"
literal "false"
\end_inset
.
\end_layout
\begin_layout Standard
Regardless of the type of peak identified, it is important to identify peaks
that occur consistently across biological replicates.
The
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
ENCODE
\end_layout
\end_inset
project has developed a method called
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
IDR
\end_layout
\end_inset
for this purpose
\begin_inset CommandInset citation
LatexCommand cite
key "Li2006"
literal "false"
\end_inset
.
The
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
IDR
\end_layout
\end_inset
is defined as the probability that a peak identified in one biological
replicate will
\emph on
not
\emph default
also be identified in a second replicate.
Where the more familiar false discovery rate measures the degree of corresponde
nce between a data-derived ranked list and the (unknown) true list of significan
t features,
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
IDR
\end_layout
\end_inset
instead measures the degree of correspondence between two ranked lists
derived from different data.
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
IDR
\end_layout
\end_inset
assumes that the highest-ranked features are
\begin_inset Quotes eld
\end_inset
signal
\begin_inset Quotes erd
\end_inset
peaks that tend to be listed in the same order in both lists, while the
lowest-ranked features are essentially noise peaks, listed in random order
with no correspondence between the lists.
\begin_inset Flex Glossary Term (Capital)
status open
\begin_layout Plain Layout
IDR
\end_layout
\end_inset
attempts to locate the
\begin_inset Quotes eld
\end_inset
crossover point
\begin_inset Quotes erd
\end_inset
between the signal and the noise by determining how far down the list the
rank consistency breaks down into randomness (Figure
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:Example-IDR"
plural "false"
caps "false"
noprefix "false"
\end_inset
).
\end_layout
\begin_layout Standard
\begin_inset Float figure
wide false
sideways false
status open
\begin_layout Plain Layout
\align center
\begin_inset Graphics
filename graphics/CD4-csaw/IDR/D4659vsD5053_epic-PAGE1-CROP-RASTER.png
lyxscale 25
width 100col%
groupId colwidth-raster
\end_inset
\end_layout
\begin_layout Plain Layout
\begin_inset Caption Standard
\begin_layout Plain Layout
\begin_inset Argument 1
status collapsed
\begin_layout Plain Layout
Example IDR consistency plot.
\end_layout
\end_inset
\begin_inset CommandInset label
LatexCommand label
name "fig:Example-IDR"
\end_inset
\series bold
Example IDR consistency plot.
\series default
Peak calls in two replicates are ranked from highest score (top and right)
to lowest score (bottom and left).
IDR identifies reproducible peaks, which rank highly in both replicates
(light blue), separating them from
\begin_inset Quotes eld
\end_inset
noise
\begin_inset Quotes erd
\end_inset
peak calls whose ranking is not reproducible between replicates (dark blue).
\end_layout
\end_inset
\end_layout
\begin_layout Plain Layout
\end_layout
\end_inset
\end_layout
\begin_layout Standard
In addition to other considerations, if called peaks are to be used as regions
of interest for differential abundance analysis, then care must be taken
to call peaks in a way that is blind to differential abundance between
experimental conditions, or else the statistical significance calculations
for differential abundance will overstate their confidence in the results.
The
\begin_inset Flex Code
status open
\begin_layout Plain Layout
csaw
\end_layout
\end_inset
package provides guidelines for calling peaks in this way: peaks are called
based on a combination of all
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
ChIP-seq
\end_layout
\end_inset
reads from all experimental conditions, so that the identified peaks are
based on the average abundance across all conditions, which is independent
of any differential abundance between conditions
\begin_inset CommandInset citation
LatexCommand cite
key "Lun2015a"
literal "false"
\end_inset
.
\end_layout
\begin_layout Subsection
Normalization of high-throughput data is non-trivial and application-dependent
\end_layout
\begin_layout Standard
High-throughput data sets invariably require some kind of normalization
before further analysis can be conducted.
In general, the goal of normalization is to remove effects in the data
that are caused by technical factors that have nothing to do with the biology
being studied.
\end_layout
\begin_layout Standard
For Affymetrix expression arrays, the standard normalization algorithm used
in most analyses is
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
RMA
\end_layout
\end_inset
\begin_inset CommandInset citation
LatexCommand cite
key "Irizarry2003a"
literal "false"
\end_inset
.
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
RMA
\end_layout
\end_inset
is designed with the assumption that some fraction of probes on each array
will be artifactual and takes advantage of the fact that each gene is represent
ed by multiple probes by implementing normalization and summarization steps
that are robust against outlier probes.
However,
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
RMA
\end_layout
\end_inset
uses the probe intensities of all arrays in the data set in the normalization
of each individual array, meaning that the normalized expression values
in each array depend on every array in the data set, and will necessarily
change each time an array is added or removed from the data set.
If this is undesirable,
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
fRMA
\end_layout
\end_inset
implements a variant of
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
RMA
\end_layout
\end_inset
where the relevant distributional parameters are learned from a large reference
set of diverse public array data sets and then
\begin_inset Quotes eld
\end_inset
frozen
\begin_inset Quotes erd
\end_inset
, so that each array is effectively normalized against this frozen reference
set rather than the other arrays in the data set under study
\begin_inset CommandInset citation
LatexCommand cite
key "McCall2010"
literal "false"
\end_inset
.
Other available array normalization methods considered include dChip,
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
GRSN
\end_layout
\end_inset
, and
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
SCAN
\end_layout
\end_inset
\begin_inset CommandInset citation
LatexCommand cite
key "Li2001,Pelz2008,Piccolo2012"
literal "false"
\end_inset
.
\end_layout
\begin_layout Standard
In contrast,
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
HTS
\end_layout
\end_inset
data present very different normalization challenges.
The simplest case is
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
RNA-seq
\end_layout
\end_inset
in which read counts are obtained for a set of gene annotations, yielding
a matrix of counts with rows representing genes and columns representing
samples.
Because
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
RNA-seq
\end_layout
\end_inset
approximates a process of sampling from a population with replacement,
each gene's count is only interpretable as a fraction of the total reads
for that sample.
For that reason,
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
RNA-seq
\end_layout
\end_inset
abundances are often reported as
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
CPM
\end_layout
\end_inset
.
Furthermore, if the abundance of a single gene increases, then in order
for its fraction of the total reads to increase, all other genes' fractions
must decrease to accommodate it.
This effect is known as composition bias, and it is an artifact of the
read sampling process that has nothing to do with the biology of the samples
and must therefore be normalized out.
The most commonly used methods to normalize for composition bias in
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
RNA-seq
\end_layout
\end_inset
data seek to equalize the average gene abundance across samples, under
the assumption that the average gene is likely not changing
\begin_inset CommandInset citation
LatexCommand cite
key "Robinson2010,Anders2010"
literal "false"
\end_inset
.
The effect of such normalizations is to center the distribution of
\begin_inset Flex Glossary Term (pl)
status open
\begin_layout Plain Layout
logFC
\end_layout
\end_inset
at zero.
Note that if a true global difference in gene expression is present in
the data, this difference will be normalized out as well, since it is indisting
uishable from composition bias.
In other words,
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
RNA-seq
\end_layout
\end_inset
cannot measure absolute gene expression, only gene expression as a fraction
of total reads.
\end_layout
\begin_layout Standard
In
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
ChIP-seq
\end_layout
\end_inset
data, normalization is not as straightforward.
The
\begin_inset Flex Code
status open
\begin_layout Plain Layout
csaw
\end_layout
\end_inset
package implements several different normalization strategies and provides
guidance on when to use each one
\begin_inset CommandInset citation
LatexCommand cite
key "Lun2015a"
literal "false"
\end_inset
.
Briefly, a typical
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
ChIP-seq
\end_layout
\end_inset
sample has a bimodal distribution of read counts: a low-abundance mode
representing background regions and a high-abundance mode representing
signal regions.
This offers two mutually incompatible normalization strategies: equalizing
background coverage or equalizing signal coverage (Figure
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:chipseq-norm-example"
plural "false"
caps "false"
noprefix "false"
\end_inset
).
If the experiment is well controlled and
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
ChIP
\end_layout
\end_inset
efficiency is known to be consistent across all samples, then normalizing
the background coverage to be equal across all samples is a reasonable
strategy.
If this is not a safe assumption, then the preferred strategy is to normalize
the signal regions in a way similar to
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
RNA-seq
\end_layout
\end_inset
data by assuming that the average signal region is not changing abundance
between samples.
Beyond this, if a
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
ChIP-seq
\end_layout
\end_inset
experiment has a more complicated structure that doesn't show the typical
bimodal count distribution, it may be necessary to implement a normalization
as a smooth function of abundance.
However, this strategy makes a much stronger assumption about the data:
that the average
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
logFC
\end_layout
\end_inset
is zero across all abundance levels.
Hence, the simpler scaling normalization based on background or signal
regions are generally preferred whenever possible.
\end_layout
\begin_layout Standard
\begin_inset Float figure
wide false
sideways false
status open
\begin_layout Plain Layout
\align center
\begin_inset Graphics
filename graphics/CD4-csaw/ChIP-seq/H3K4me2-sample-MAplot-bins-CROP.png
lyxscale 25
width 100col%
groupId colwidth-raster
\end_inset
\end_layout
\begin_layout Plain Layout
\begin_inset Caption Standard
\begin_layout Plain Layout
\begin_inset Argument 1
status collapsed
\begin_layout Plain Layout
Example MA plot of ChIP-seq read counts in 10kb bins for two arbitrary samples.
\end_layout
\end_inset
\begin_inset CommandInset label
LatexCommand label
name "fig:chipseq-norm-example"
\end_inset
\series bold
Example MA plot of ChIP-seq read counts in 10kb bins for two arbitrary samples.
\series default
The distribution of bins is bimodal along the x axis (average abundance),
with the left mode representing
\begin_inset Quotes eld
\end_inset
background
\begin_inset Quotes erd
\end_inset
regions with no protein binding and the right mode representing bound regions.
The modes are also separated on the y axis (logFC), motivating two conflicting
normalization strategies: background normalization (red) and signal normalizati
on (blue and green, two similar signal normalizations).
\end_layout
\end_inset
\end_layout
\end_inset
\end_layout
\begin_layout Subsection
ComBat and SVA for correction of known and unknown batch effects
\end_layout
\begin_layout Standard
In addition to well-understood effects that can be easily normalized out,
a data set often contains confounding biological effects that must be accounted
for in the modeling step.
For instance, in an experiment with pre-treatment and post-treatment samples
of cells from several different donors, donor variability represents a
known batch effect.
The most straightforward correction for known batches is to estimate the
mean for each batch independently and subtract out the differences, so
that all batches have identical means for each feature.
However, as with variance estimation, estimating the differences in batch
means is not necessarily robust at the feature level, so the ComBat method
adds empirical Bayes squeezing of the batch mean differences toward a common
value, analogous to
\begin_inset Flex Code
status open
\begin_layout Plain Layout
limma
\end_layout
\end_inset
's empirical Bayes squeezing of feature variance estimates
\begin_inset CommandInset citation
LatexCommand cite
key "Johnson2007"
literal "false"
\end_inset
.
Effectively, ComBat assumes that modest differences between batch means
are real batch effects, but extreme differences between batch means are
more likely to be the result of outlier observations that happen to line
up with the batches rather than a genuine batch effect.
The result is a batch correction that is more robust against outliers than
simple subtraction of mean differences.
\end_layout
\begin_layout Standard
In some data sets, unknown batch effects may be present due to inherent
variability in the data, either caused by technical or biological effects.
Examples of unknown batch effects include variations in enrichment efficiency
between
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
ChIP-seq
\end_layout
\end_inset
samples, variations in populations of different cell types, and the effects
of uncontrolled environmental factors on gene expression in humans or live
animals.
In an ordinary linear model context, unknown batch effects cannot be inferred
and must be treated as random noise.
However, in high-throughput experiments, once again information can be
shared across features to identify patterns of un-modeled variation that
are repeated in many features.
One attractive strategy would be to perform
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
SVD
\end_layout
\end_inset
on the matrix of linear model residuals (which contain all the un-modeled
variation in the data) and take the first few singular vectors as batch
effects.
While this can be effective, it makes the unreasonable assumption that
all batch effects are completely uncorrelated with any of the effects being
modeled.
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
SVA
\end_layout
\end_inset
starts with this approach, but takes some additional steps to identify
batch effects in the full data that are both highly correlated with the
singular vectors in the residuals and least correlated with the effects
of interest
\begin_inset CommandInset citation
LatexCommand cite
key "Leek2007"
literal "false"
\end_inset
.
Since the final batch effects are estimated from the full data, moderate
correlations between the batch effects and effects of interest are allowed,
which gives
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
SVA
\end_layout
\end_inset
much more freedom to estimate the true extent of the batch effects compared
to simple residual
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
SVD
\end_layout
\end_inset
.
Once the surrogate variables are estimated, they can be included as coefficient
s in the linear model in a similar fashion to known batch effects in order
to subtract out their effects on each feature's abundance.
\end_layout
\begin_layout Subsection
Interpreting p-value distributions and estimating false discovery rates
\end_layout
\begin_layout Standard
When testing thousands of genes for differential expression or performing
thousands of statistical tests for other kinds of genomic data, the result
is thousands of p-values.
By construction, p-values have a
\begin_inset Formula $\mathrm{Uniform}(0,1)$
\end_inset
distribution under the null hypothesis.
This means that if all null hypotheses are true in a large number
\begin_inset Formula $N$
\end_inset
of tests, then for any significance threshold
\begin_inset Formula $T$
\end_inset
, approximately
\begin_inset Formula $N*T$
\end_inset
p-values would be called
\begin_inset Quotes eld
\end_inset
significant
\begin_inset Quotes erd
\end_inset
at that threshold even though the null hypotheses are all true.
These are called false discoveries.
\end_layout
\begin_layout Standard
When only a fraction of null hypotheses are true, the p-value distribution
will be a mixture of a uniform component representing the null hypotheses
that are true and a non-uniform component representing the null hypotheses
that are not true (Figure
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:Example-pval-hist"
plural "false"
caps "false"
noprefix "false"
\end_inset
).
The fraction belonging to the uniform component is referred to as
\begin_inset Formula $\pi_{0}$
\end_inset
, which ranges from 1 (all null hypotheses true) to 0 (all null hypotheses
false).
Furthermore, the non-uniform component must be biased toward zero, since
any evidence against the null hypothesis pushes the p-value for a test
toward zero.
We can exploit this fact to estimate the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
FDR
\end_layout
\end_inset
for any significance threshold by estimating the degree to which the density
of p-values left of that threshold exceeds what would be expected for a
uniform distribution.
In genomics, the most commonly used
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
FDR
\end_layout
\end_inset
estimation method, and the one used in this work, is that of
\begin_inset ERT
status open
\begin_layout Plain Layout
\backslash
glsdisp{BH}{Benjamini and Hochberg}
\end_layout
\end_inset
\begin_inset CommandInset citation
LatexCommand cite
key "Benjamini1995"
literal "false"
\end_inset
.
This is a conservative method that effectively assumes
\begin_inset Formula $\pi_{0}=1$
\end_inset
.
Hence it gives an estimated upper bound for the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
FDR
\end_layout
\end_inset
at any significance threshold, rather than a point estimate.
\end_layout
\begin_layout Standard
\begin_inset Float figure
wide false
sideways false
status collapsed
\begin_layout Plain Layout
\align center
\begin_inset Graphics
filename graphics/Intro/med-pval-hist-colored-CROP.pdf
lyxscale 50
width 100col%
groupId colfullwidth
\end_inset
\end_layout
\begin_layout Plain Layout
\begin_inset Caption Standard
\begin_layout Plain Layout
\begin_inset Argument 1
status collapsed
\begin_layout Plain Layout
Example p-value histogram.
\end_layout
\end_inset
\begin_inset CommandInset label
LatexCommand label
name "fig:Example-pval-hist"
\end_inset
\series bold
Example p-value histogram.
\series default
The distribution of p-values from a large number of independent tests (such
as differential expression tests for each gene in the genome) is a mixture
of a uniform component representing the null hypotheses that are true (blue
shading) and a zero-biased component representing the null hypotheses that
are false (red shading).
The FDR for any column in the histogram is the fraction of that column
that is blue.
The line
\begin_inset Formula $y=\pi_{0}$
\end_inset
represents the theoretical uniform component of this p-value distribution,
while the line
\begin_inset Formula $y=1$
\end_inset
represents the uniform component when all null hypotheses are true.
Note that in real data, the true status of each hypothesis is unknown,
so only the overall shape of the distribution is known.
\end_layout
\end_inset
\end_layout
\end_inset
\end_layout
\begin_layout Standard
We can also estimate
\begin_inset Formula $\pi_{0}$
\end_inset
for the entire distribution of p-values, which can give an idea of the
overall signal size in the data without setting any significance threshold
or making any decisions about which specific null hypotheses to reject.
As
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
FDR
\end_layout
\end_inset
estimation, there are many methods proposed for estimating
\begin_inset Formula $\pi_{0}$
\end_inset
.
The one used in this work is the Phipson method of averaging local
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
FDR
\end_layout
\end_inset
values
\begin_inset CommandInset citation
LatexCommand cite
key "Phipson2013Thesis"
literal "false"
\end_inset
.
Once
\begin_inset Formula $\pi_{0}$
\end_inset
is estimated, the number of null hypotheses that are false can be estimated
as
\begin_inset Formula $(1-\pi_{0})*N$
\end_inset
.
\end_layout
\begin_layout Standard
Conversely, a p-value distribution that is neither uniform nor zero-biased
is evidence of a modeling failure.
Such a distribution would imply that there is less than zero evidence against
the null hypothesis, which is not possible (in a frequentist setting).
Attempting to estimate
\begin_inset Formula $\pi_{0}$
\end_inset
from such a distribution would yield an estimate greater than 1, a nonsensical
result.
The usual cause of a poorly-behaving p-value distribution is a model assumption
that is violated by the data, such as assuming equal variance between groups
(homoskedasticity) when the variance of each group is not equal (heteroskedasti
city) or failing to model a strong confounding batch effect.
In particular, such a p-value distribution is
\emph on
not
\emph default
consistent with a simple lack of signal in the data, as this should result
in a uniform distribution.
Hence, observing such a p-value distribution should prompt a search for
violated model assumptions.
\end_layout
\begin_layout Standard
\begin_inset Note Note
status open
\begin_layout Subsection
Factor analysis: PCA, PCoA, MOFA
\end_layout
\begin_layout Plain Layout
\begin_inset Flex TODO Note (inline)
status open
\begin_layout Plain Layout
Not sure if this merits a subsection here.
\end_layout
\end_inset
\end_layout
\begin_layout Itemize
Batch-corrected
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
PCA
\end_layout
\end_inset
is informative, but careful application is required to avoid bias
\end_layout
\end_inset
\end_layout
\begin_layout Section
Structure of the thesis
\end_layout
\begin_layout Standard
This thesis presents 3 instances of using high-throughput genomic and epigenomic
assays to investigate hypotheses or solve problems relating to the study
of transplant rejection.
In Chapter
\begin_inset CommandInset ref
LatexCommand ref
reference "chap:CD4-ChIP-seq"
plural "false"
caps "false"
noprefix "false"
\end_inset
,
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
ChIP-seq
\end_layout
\end_inset
and
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
RNA-seq
\end_layout
\end_inset
are used to investigate the dynamics of promoter histone methylation as
it relates to gene expression in T-cell activation and memory.
Chapter
\begin_inset CommandInset ref
LatexCommand ref
reference "chap:Improving-array-based-diagnostic"
plural "false"
caps "false"
noprefix "false"
\end_inset
looks at several array-based assays with the potential to diagnose transplant
rejection and shows that analyses of this array data are greatly improved
by paying careful attention to normalization and preprocessing.
Chapter
\begin_inset CommandInset ref
LatexCommand ref
reference "chap:Globin-blocking-cyno"
plural "false"
caps "false"
noprefix "false"
\end_inset
presents a custom method for improving
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
RNA-seq
\end_layout
\end_inset
of non-human primate blood samples by preventing reverse transcription
of unwanted globin transcripts.
Finally, Chapter
\begin_inset CommandInset ref
LatexCommand ref
reference "chap:Conclusions"
plural "false"
caps "false"
noprefix "false"
\end_inset
summarizes the overarching lessons and strategies learned through these
analyses that can be applied to all future analyses of high-throughput
genomic assays.
\end_layout
\begin_layout Chapter
\begin_inset CommandInset label
LatexCommand label
name "chap:CD4-ChIP-seq"
\end_inset
Reproducible genome-wide epigenetic analysis of H3K4 and H3K27 methylation
in naïve and memory CD4
\begin_inset Formula $^{+}$
\end_inset
T-cell activation
\end_layout
\begin_layout Standard
\size large
Ryan C.
Thompson, Sarah A.
Lamere, Daniel R.
Salomon
\end_layout
\begin_layout Standard
\begin_inset ERT
status collapsed
\begin_layout Plain Layout
\backslash
glsresetall
\end_layout
\end_inset
\begin_inset Note Note
status open
\begin_layout Plain Layout
This causes all abbreviations to be reintroduced.
\end_layout
\end_inset
\end_layout
\begin_layout Section
Introduction
\end_layout
\begin_layout Standard
CD4
\begin_inset Formula $^{+}$
\end_inset
T-cells are central to all adaptive immune responses, as well as immune
memory
\begin_inset CommandInset citation
LatexCommand cite
key "Murphy2012"
literal "false"
\end_inset
.
After an infection is cleared, a subset of the naïve CD4
\begin_inset Formula $^{+}$
\end_inset
T-cells that responded to that infection differentiate into memory CD4
\begin_inset Formula $^{+}$
\end_inset
T-cells, which are responsible for responding to the same pathogen in the
future.
Memory CD4
\begin_inset Formula $^{+}$
\end_inset
T-cells are functionally distinct, able to respond to an infection more
quickly and without the co-stimulation required by naïve CD4
\begin_inset Formula $^{+}$
\end_inset
T-cells.
However, the molecular mechanisms underlying this functional distinction
are not well-understood.
Epigenetic regulation via histone modification is thought to play an important
role, but while many studies have looked at static snapshots of histone
methylation in T-cells, few studies have looked at the dynamics of histone
regulation after T-cell activation, nor the differences in histone methylation
between naïve and memory T-cells.
H3K4me2, H3K4me3 and H3K27me3 are three histone marks thought to be major
epigenetic regulators of gene expression.
The goal of the present study is to investigate the role of these histone
marks in CD4
\begin_inset Formula $^{+}$
\end_inset
T-cell activation kinetics and memory differentiation.
In static snapshots, H3K4me2 and H3K4me3 are often observed in the promoters
of highly transcribed genes, while H3K27me3 is more often observed in promoters
of inactive genes with little to no transcription occurring.
As a result, the two H3K4 marks have been characterized as
\begin_inset Quotes eld
\end_inset
activating
\begin_inset Quotes erd
\end_inset
marks, while H3K27me3 has been characterized as
\begin_inset Quotes eld
\end_inset
deactivating
\begin_inset Quotes erd
\end_inset
.
Despite these characterizations, the actual causal relationship between
these histone modifications and gene transcription is complex and likely
involves positive and negative feedback loops between the two.
\end_layout
\begin_layout Section
Approach
\end_layout
\begin_layout Standard
In order to investigate the relationship between gene expression and these
histone modifications in the context of naïve and memory CD4
\begin_inset Formula $^{+}$
\end_inset
T-cell activation, a previously published data set of
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
RNA-seq
\end_layout
\end_inset
data and
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
ChIP-seq
\end_layout
\end_inset
data was re-analyzed using up-to-date methods designed to address the specific
analysis challenges posed by this data set.
The data set contains naïve and memory CD4
\begin_inset Formula $^{+}$
\end_inset
T-cell samples in a time course before and after activation.
Like the original analysis, this analysis looks at the dynamics of these
histone marks and compares them to gene expression dynamics at the same
time points during activation, as well as compares them between naïve and
memory cells, in hope of discovering evidence of new mechanistic details
in the interplay between them.
The original analysis of this data treated each gene promoter as a monolithic
unit and mostly assumed that
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
ChIP-seq
\end_layout
\end_inset
reads or peaks occurring anywhere within a promoter were equivalent, regardless
of where they occurred relative to the gene structure.
For an initial analysis of the data, this was a necessary simplifying assumptio
n.
The current analysis aims to relax this assumption, first by directly analyzing
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
ChIP-seq
\end_layout
\end_inset
peaks for differential modification, and second by taking a more granular
look at the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
ChIP-seq
\end_layout
\end_inset
read coverage within promoter regions to ask whether the location of histone
modifications relative to the gene's
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
TSS
\end_layout
\end_inset
is an important factor, as opposed to simple proximity.
\end_layout
\begin_layout Section
Methods
\end_layout
\begin_layout Standard
A reproducible workflow was written to analyze the raw
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
ChIP-seq
\end_layout
\end_inset
and
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
RNA-seq
\end_layout
\end_inset
data from previous studies (
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
GEO
\end_layout
\end_inset
accession number
\begin_inset CommandInset href
LatexCommand href
name "GSE73214"
target "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE73214"
literal "false"
\end_inset
)
\begin_inset CommandInset citation
LatexCommand cite
key "gh-cd4-csaw,LaMere2015,LaMere2016,LaMere2017"
literal "true"
\end_inset
.
Briefly, this data consists of
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
RNA-seq
\end_layout
\end_inset
and
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
ChIP-seq
\end_layout
\end_inset
from CD4
\begin_inset Formula $^{+}$
\end_inset
T-cells from 4 donors.
From each donor, naïve and memory CD4
\begin_inset Formula $^{+}$
\end_inset
T-cells were isolated separately.
Then cultures of both cells were activated with CD3/CD28 beads, and samples
were taken at 4 time points: Day 0 (pre-activation), Day 1 (early activation),
Day 5 (peak activation), and Day 14 (post-activation).
For each combination of cell type and time point, RNA was isolated and
sequenced, and
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
ChIP-seq
\end_layout
\end_inset
was performed for each of 3 histone marks: H3K4me2, H3K4me3, and H3K27me3.
The
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
ChIP-seq
\end_layout
\end_inset
input DNA was also sequenced for each sample.
The result was 32 samples for each assay.
\end_layout
\begin_layout Subsection
RNA-seq differential expression analysis
\end_layout
\begin_layout Standard
\begin_inset Note Note
status collapsed
\begin_layout Plain Layout
\begin_inset Float figure
wide false
sideways false
status open
\begin_layout Plain Layout
\align center
\begin_inset Float figure
wide false
sideways false
status collapsed
\begin_layout Plain Layout
\align center
\begin_inset Graphics
filename graphics/CD4-csaw/rnaseq-compare/ensmebl-vs-entrez-star-CROP.png
lyxscale 25
width 35col%
groupId rna-comp-subfig
\end_inset
\end_layout
\begin_layout Plain Layout
\begin_inset Caption Standard
\begin_layout Plain Layout
STAR quantification, Entrez vs Ensembl gene annotation
\end_layout
\end_inset
\end_layout
\end_inset
\begin_inset space \qquad{}
\end_inset
\begin_inset Float figure
wide false
sideways false
status collapsed
\begin_layout Plain Layout
\align center
\begin_inset Graphics
filename graphics/CD4-csaw/rnaseq-compare/ensmebl-vs-entrez-shoal-CROP.png
lyxscale 25
width 35col%
groupId rna-comp-subfig
\end_inset
\end_layout
\begin_layout Plain Layout
\begin_inset Caption Standard
\begin_layout Plain Layout
Salmon+Shoal quantification, Entrez vs Ensembl gene annotation
\end_layout
\end_inset
\end_layout
\end_inset
\end_layout
\begin_layout Plain Layout
\align center
\begin_inset Float figure
wide false
sideways false
status collapsed
\begin_layout Plain Layout
\align center
\begin_inset Graphics
filename graphics/CD4-csaw/rnaseq-compare/star-vs-hisat2-CROP.png
lyxscale 25
width 35col%
groupId rna-comp-subfig
\end_inset
\end_layout
\begin_layout Plain Layout
\begin_inset Caption Standard
\begin_layout Plain Layout
STAR vs HISAT2 quantification, Ensembl gene annotation
\end_layout
\end_inset
\end_layout
\end_inset
\begin_inset space \qquad{}
\end_inset
\begin_inset Float figure
wide false
sideways false
status collapsed
\begin_layout Plain Layout
\align center
\begin_inset Graphics
filename graphics/CD4-csaw/rnaseq-compare/star-vs-salmon-CROP.png
lyxscale 25
width 35col%
groupId rna-comp-subfig
\end_inset
\end_layout
\begin_layout Plain Layout
\begin_inset Caption Standard
\begin_layout Plain Layout
Salmon vs STAR quantification, Ensembl gene annotation
\end_layout
\end_inset
\end_layout
\end_inset
\end_layout
\begin_layout Plain Layout
\align center
\begin_inset Float figure
wide false
sideways false
status collapsed
\begin_layout Plain Layout
\align center
\begin_inset Graphics
filename graphics/CD4-csaw/rnaseq-compare/salmon-vs-kallisto-CROP.png
lyxscale 25
width 35col%
groupId rna-comp-subfig
\end_inset
\end_layout
\begin_layout Plain Layout
\begin_inset Caption Standard
\begin_layout Plain Layout
Salmon vs Kallisto quantification, Ensembl gene annotation
\end_layout
\end_inset
\end_layout
\end_inset
\begin_inset space \qquad{}
\end_inset
\begin_inset Float figure
wide false
sideways false
status collapsed
\begin_layout Plain Layout
\align center
\begin_inset Graphics
filename graphics/CD4-csaw/rnaseq-compare/salmon-vs-shoal-CROP.png
lyxscale 25
width 35col%
groupId rna-comp-subfig
\end_inset
\end_layout
\begin_layout Plain Layout
\begin_inset Caption Standard
\begin_layout Plain Layout
Salmon+Shoal vs Salmon alone, Ensembl gene annotation
\end_layout
\end_inset
\end_layout
\end_inset
\end_layout
\begin_layout Plain Layout
\begin_inset Caption Standard
\begin_layout Plain Layout
\begin_inset CommandInset label
LatexCommand label
name "fig:RNA-norm-comp"
\end_inset
RNA-seq comparisons
\end_layout
\end_inset
\end_layout
\end_inset
\end_layout
\end_inset
\end_layout
\begin_layout Standard
Sequence reads were retrieved from the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
SRA
\end_layout
\end_inset
\begin_inset CommandInset citation
LatexCommand cite
key "Leinonen2011"
literal "false"
\end_inset
.
Five different alignment and quantification methods were tested for the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
RNA-seq
\end_layout
\end_inset
data
\begin_inset CommandInset citation
LatexCommand cite
key "Dobin2012,Kim2019,Liao2014,Pimentel2016,Patro2017,gh-shoal,gh-hg38-ref"
literal "false"
\end_inset
.
Each quantification was tested with both Ensembl transcripts and GENCODE
known gene annotations
\begin_inset CommandInset citation
LatexCommand cite
key "Zerbino2018,Harrow2012"
literal "false"
\end_inset
.
Comparisons of downstream results from each combination of quantification
method and reference revealed that all quantifications gave broadly similar
results for most genes, with non being obviously superior.
Salmon quantification with regularization by shoal with the Ensembl annotation
was chosen as the method theoretically most likely to partially mitigate
some of the batch effect in the data
\begin_inset CommandInset citation
LatexCommand cite
key "Patro2017,gh-shoal"
literal "false"
\end_inset
.
\end_layout
\begin_layout Standard
Due to an error in sample preparation, the RNA from the samples for days
0 and 5 were sequenced using a different kit than those for days 1 and
14.
This induced a substantial batch effect in the data due to differences
in sequencing biases between the two kits, and this batch effect is unfortunate
ly confounded with the time point variable (Figure
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:RNA-PCA-no-batchsub"
plural "false"
caps "false"
noprefix "false"
\end_inset
).
To do the best possible analysis with this data, this batch effect was
subtracted out from the data using ComBat
\begin_inset CommandInset citation
LatexCommand cite
key "Johnson2007"
literal "false"
\end_inset
, ignoring the time point variable due to the confounding with the batch
variable.
The result is a marked improvement, but the unavoidable confounding with
time point means that certain real patterns of gene expression will be
indistinguishable from the batch effect and subtracted out as a result.
Specifically, any
\begin_inset Quotes eld
\end_inset
zig-zag
\begin_inset Quotes erd
\end_inset
pattern, such as a gene whose expression goes up on day 1, down on day
5, and back up again on day 14, will be attenuated or eliminated entirely.
In the context of a T-cell activation time course, it is unlikely that
many genes of interest will follow such an expression pattern, so this
loss was deemed an acceptable cost for correcting the batch effect.
\end_layout
\begin_layout Standard
\begin_inset Float figure
wide false
sideways false
status collapsed
\begin_layout Plain Layout
\align center
\begin_inset Float figure
wide false
sideways false
status open
\begin_layout Plain Layout
\align center
\begin_inset Graphics
filename graphics/CD4-csaw/RNA-seq/PCA-no-batchsub-CROP.png
lyxscale 25
width 75col%
groupId rna-pca-subfig
\end_inset
\end_layout
\begin_layout Plain Layout
\begin_inset Caption Standard
\begin_layout Plain Layout
\begin_inset CommandInset label
LatexCommand label
name "fig:RNA-PCA-no-batchsub"
\end_inset
Before batch correction
\end_layout
\end_inset
\end_layout
\end_inset
\end_layout
\begin_layout Plain Layout
\align center
\begin_inset Float figure
wide false
sideways false
status open
\begin_layout Plain Layout
\align center
\begin_inset Graphics
filename graphics/CD4-csaw/RNA-seq/PCA-combat-batchsub-CROP.png
lyxscale 25
width 75col%
groupId rna-pca-subfig
\end_inset
\end_layout
\begin_layout Plain Layout
\begin_inset Caption Standard
\begin_layout Plain Layout
\begin_inset CommandInset label
LatexCommand label
name "fig:RNA-PCA-ComBat-batchsub"
\end_inset
After batch correction with ComBat
\end_layout
\end_inset
\end_layout
\end_inset
\end_layout
\begin_layout Plain Layout
\begin_inset Caption Standard
\begin_layout Plain Layout
\begin_inset Argument 1
status collapsed
\begin_layout Plain Layout
PCoA plots of RNA-seq data showing effect of batch correction.
\end_layout
\end_inset
\begin_inset CommandInset label
LatexCommand label
name "fig:RNA-PCA"
\end_inset
\series bold
PCoA plots of RNA-seq data showing effect of batch correction.
\series default
The uncorrected data (a) shows a clear separation between samples from the
two batches (red and blue) dominating the first principal coordinate.
After correction with ComBat (b), the two batches now have approximately
the same center, and the first two principal coordinates both show separation
between experimental conditions rather than batches.
(Note that time points are shown in hours rather than days in these plots.)
\end_layout
\end_inset
\end_layout
\end_inset
\end_layout
\begin_layout Standard
However, removing the systematic component of the batch effect still leaves
the noise component.
The gene quantifications from the first batch are substantially noisier
than those in the second batch.
This analysis corrected for this by using
\begin_inset Flex Code
status open
\begin_layout Plain Layout
limma
\end_layout
\end_inset
's sample weighting method to assign lower weights to the noisy samples
of batch 1 (Figure
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:RNA-seq-weights-vs-covars"
plural "false"
caps "false"
noprefix "false"
\end_inset
)
\begin_inset CommandInset citation
LatexCommand cite
key "Ritchie2006,Liu2015"
literal "false"
\end_inset
.
The resulting analysis gives an accurate assessment of statistical significance
for all comparisons, which unfortunately means a loss of statistical power
for comparisons involving samples in batch 1.
\end_layout
\begin_layout Standard
In any case, the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
RNA-seq
\end_layout
\end_inset
counts were first normalized using
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
TMM
\end_layout
\end_inset
\begin_inset CommandInset citation
LatexCommand cite
key "Robinson2010"
literal "false"
\end_inset
, converted to normalized
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
logCPM
\end_layout
\end_inset
with quality weights using
\begin_inset Flex Code
status open
\begin_layout Plain Layout
voomWithQualityWeights
\end_layout
\end_inset
\begin_inset CommandInset citation
LatexCommand cite
key "Law2014,Liu2015"
literal "false"
\end_inset
, and batch-corrected at this point using ComBat.
A linear model was fit to the batch-corrected, quality-weighted data for
each gene using
\begin_inset Flex Code
status open
\begin_layout Plain Layout
limma
\end_layout
\end_inset
, and each gene was tested for differential expression using
\begin_inset Flex Code
status open
\begin_layout Plain Layout
limma
\end_layout
\end_inset
's empirical Bayes moderated
\begin_inset Formula $t$
\end_inset
-test
\begin_inset CommandInset citation
LatexCommand cite
key "Smyth2005,Law2014,Phipson2016"
literal "false"
\end_inset
.
P-values were corrected for multiple testing using the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
BH
\end_layout
\end_inset
procedure for
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
FDR
\end_layout
\end_inset
control
\begin_inset CommandInset citation
LatexCommand cite
key "Benjamini1995"
literal "false"
\end_inset
.
\end_layout
\begin_layout Standard
\begin_inset Float figure
wide false
sideways false
status open
\begin_layout Plain Layout
\align center
\begin_inset Graphics
filename graphics/CD4-csaw/RNA-seq/weights-vs-covars-nobcv-CROP.png
lyxscale 25
width 100col%
groupId colwidth-raster
\end_inset
\end_layout
\begin_layout Plain Layout
\begin_inset Caption Standard
\begin_layout Plain Layout
\begin_inset Argument 1
status collapsed
\begin_layout Plain Layout
RNA-seq sample weights, grouped by experimental and technical covariates.
\end_layout
\end_inset
\begin_inset CommandInset label
LatexCommand label
name "fig:RNA-seq-weights-vs-covars"
\end_inset
\series bold
RNA-seq sample weights, grouped by experimental and technical covariates.
\series default
Inverse variance weights were estimated for each sample using
\begin_inset Flex Code
status open
\begin_layout Plain Layout
limma
\end_layout
\end_inset
's
\begin_inset Flex Code
status open
\begin_layout Plain Layout
arrayWeights
\end_layout
\end_inset
function (part of
\begin_inset Flex Code
status open
\begin_layout Plain Layout
voomWithQualityWeights
\end_layout
\end_inset
).
The samples were grouped by each known covariate and the distribution of
weights was plotted for each group.
\end_layout
\end_inset
\end_layout
\end_inset
\end_layout
\begin_layout Subsection
ChIP-seq analyses
\end_layout
\begin_layout Standard
\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
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
SRA
\end_layout
\end_inset
\begin_inset CommandInset citation
LatexCommand cite
key "Leinonen2011"
literal "false"
\end_inset
.
\begin_inset Flex Glossary Term (Capital)
status open
\begin_layout Plain Layout
ChIP-seq
\end_layout
\end_inset
(and input) reads were aligned to the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
GRCh38
\end_layout
\end_inset
genome assembly using Bowtie 2
\begin_inset CommandInset citation
LatexCommand cite
key "Langmead2012,Schneider2017,gh-hg38-ref"
literal "false"
\end_inset
.
Artifact regions were annotated using a custom implementation of the
\begin_inset Flex Code
status open
\begin_layout Plain Layout
GreyListChIP
\end_layout
\end_inset
algorithm, and these
\begin_inset Quotes eld
\end_inset
greylists
\begin_inset Quotes erd
\end_inset
were merged with the published
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
ENCODE
\end_layout
\end_inset
blacklists
\begin_inset CommandInset citation
LatexCommand cite
key "greylistchip,Dunham2012,Amemiya2019,gh-cd4-csaw"
literal "false"
\end_inset
.
Any read or called peak overlapping one of these regions was regarded as
artifactual and excluded from downstream analyses.
Figure
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:CCF-master"
plural "false"
caps "false"
noprefix "false"
\end_inset
shows the improvement after blacklisting in the strand cross-correlation
plots, a common quality control plot for
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
ChIP-seq
\end_layout
\end_inset
data
\begin_inset CommandInset citation
LatexCommand cite
key "Kharchenko2008,Lun2015a"
literal "false"
\end_inset
.
Peaks were called using
\begin_inset Flex Code
status open
\begin_layout Plain Layout
epic
\end_layout
\end_inset
, an implementation of the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
SICER
\end_layout
\end_inset
algorithm
\begin_inset CommandInset citation
LatexCommand cite
key "Zang2009,gh-epic"
literal "false"
\end_inset
.
Peaks were also called separately using
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
MACS
\end_layout
\end_inset
, but
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
MACS
\end_layout
\end_inset
was determined to be a poor fit for the data, and these peak calls are
not used in any further analyses
\begin_inset CommandInset citation
LatexCommand cite
key "Zhang2008"
literal "false"
\end_inset
.
Consensus peaks were determined by applying the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
IDR
\end_layout
\end_inset
framework
\begin_inset CommandInset citation
LatexCommand cite
key "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
\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/csaw/CCF-plots-noBL-PAGE2-CROP.pdf
lyxscale 75
width 47col%
groupId ccf-subfig
\end_inset
\end_layout
\begin_layout Plain Layout
\begin_inset Caption Standard
\begin_layout Plain Layout
\series bold
\begin_inset CommandInset label
LatexCommand label
name "fig:CCF-without-blacklist"
\end_inset
Cross-correlation plots without removing blacklisted reads.
\series default
Without blacklisting, many artifactual peaks are visible in the cross-correlatio
ns of the ChIP-seq samples, and the peak at the true fragment size (147
\begin_inset space ~
\end_inset
bp) is frequently overshadowed by the artifactual peak at the read length
(100
\begin_inset space ~
\end_inset
bp).
\end_layout
\end_inset
\end_layout
\end_inset
\begin_inset space \hfill{}
\end_inset
\begin_inset Float figure
wide false
sideways false
status collapsed
\begin_layout Plain Layout
\align center
\begin_inset Graphics
filename graphics/CD4-csaw/csaw/CCF-plots-PAGE2-CROP.pdf
lyxscale 75
width 47col%
groupId ccf-subfig
\end_inset
\end_layout
\begin_layout Plain Layout
\begin_inset Caption Standard
\begin_layout Plain Layout
\series bold
\begin_inset CommandInset label
LatexCommand label
name "fig:CCF-with-blacklist"
\end_inset
Cross-correlation plots with blacklisted reads removed.
\series default
After blacklisting, most ChIP-seq samples have clean-looking periodic cross-cor
relation plots, with the largest peak around 147
\begin_inset space ~
\end_inset
bp, the expected size for a fragment of DNA from a single nucleosome, and
little to no peak at the read length, 100
\begin_inset space ~
\end_inset
bp.
\end_layout
\end_inset
\end_layout
\end_inset
\end_layout
\begin_layout Plain Layout
\begin_inset Flex TODO Note (inline)
status open
\begin_layout Plain Layout
Figure font too small
\end_layout
\end_inset
\end_layout
\begin_layout Plain Layout
\begin_inset Caption Standard
\begin_layout Plain Layout
\begin_inset Argument 1
status collapsed
\begin_layout Plain Layout
Strand cross-correlation plots for ChIP-seq data, before and after blacklisting.
\end_layout
\end_inset
\begin_inset CommandInset label
LatexCommand label
name "fig:CCF-master"
\end_inset
\series bold
Strand cross-correlation plots for ChIP-seq data, before and after blacklisting.
\series default
The number of reads starting at each position in the genome was counted
separately for the plus and minus strands, and then the correlation coefficient
between the read start counts for both strands (cross-correlation) was
computed after shifting the plus strand counts forward by a specified interval
(the delay).
This was repeated for every delay value from 0 to 1000, and the cross-correlati
on values were plotted as a function of the delay.
In good quality samples, cross-correlation is maximized when the delay
equals the fragment size; in poor quality samples, cross-correlation is
often maximized when the delay equals the read length, an artifactual peak
whose cause is not fully understood.
\end_layout
\end_inset
\end_layout
\end_inset
\end_layout
\begin_layout Standard
\begin_inset ERT
status open
\begin_layout Plain Layout
\backslash
end{landscape}
\end_layout
\begin_layout Plain Layout
}
\end_layout
\end_inset
\end_layout
\begin_layout Standard
Promoters were defined by computing the distance from each annotated
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
TSS
\end_layout
\end_inset
to the nearest called peak and examining the distribution of distances,
observing that peaks for each histone mark were enriched within a certain
distance of the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
TSS
\end_layout
\end_inset
.
(Note: this analysis was performed using the original peak calls and expression
values from
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
GEO
\end_layout
\end_inset
\begin_inset CommandInset citation
LatexCommand cite
key "LaMere2016"
literal "false"
\end_inset
.) For H3K4me2 and H3K4me3, this distance was about 1
\begin_inset space ~
\end_inset
kbp, while for H3K27me3 it was 2.5
\begin_inset space ~
\end_inset
kbp.
These distances were used as an
\begin_inset Quotes eld
\end_inset
effective promoter radius
\begin_inset Quotes erd
\end_inset
for each mark.
The promoter region for each gene was defined as the region of the genome
within this distance upstream or downstream of the gene's annotated
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
TSS
\end_layout
\end_inset
.
For genes with multiple annotated
\begin_inset Flex Glossary Term (pl)
status open
\begin_layout Plain Layout
TSS
\end_layout
\end_inset
, a promoter region was defined for each
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
TSS
\end_layout
\end_inset
individually, and any promoters that overlapped (due to multiple
\begin_inset Flex Glossary Term (pl)
status open
\begin_layout Plain Layout
TSS
\end_layout
\end_inset
being closer than 2 times the radius) were merged into one large promoter.
Thus, some genes had multiple promoters defined, which were each analyzed
separately for differential modification.
\end_layout
\begin_layout Standard
Reads in promoters, peaks, and sliding windows across the genome were counted
and normalized using
\begin_inset Flex Code
status open
\begin_layout Plain Layout
csaw
\end_layout
\end_inset
and analyzed for differential modification using
\begin_inset Flex Code
status open
\begin_layout Plain Layout
edgeR
\end_layout
\end_inset
\begin_inset CommandInset citation
LatexCommand cite
key "Lun2014,Lun2015a,Lund2012,Phipson2016"
literal "false"
\end_inset
.
Unobserved confounding factors in the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
ChIP-seq
\end_layout
\end_inset
data were corrected using
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
SVA
\end_layout
\end_inset
\begin_inset CommandInset citation
LatexCommand cite
key "Leek2007,Leek2014"
literal "false"
\end_inset
.
Principal coordinate plots of the promoter count data for each histone
mark before and after subtracting surrogate variable effects are shown
in Figure
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:PCoA-ChIP"
plural "false"
caps "false"
noprefix "false"
\end_inset
.
\end_layout
\begin_layout Standard
\begin_inset Float figure
wide false
sideways false
status collapsed
\begin_layout Plain Layout
\begin_inset Float figure
wide false
sideways false
status open
\begin_layout Plain Layout
\align center
\begin_inset Graphics
filename graphics/CD4-csaw/ChIP-seq/H3K4me2-PCA-raw-CROP.png
lyxscale 25
width 45col%
groupId pcoa-subfig
\end_inset
\end_layout
\begin_layout Plain Layout
\begin_inset Caption Standard
\begin_layout Plain Layout
\series bold
\begin_inset CommandInset label
LatexCommand label
name "fig:PCoA-H3K4me2-bad"
\end_inset
H3K4me2, no correction
\end_layout
\end_inset
\end_layout
\end_inset
\begin_inset space \hfill{}
\end_inset
\begin_inset Float figure
wide false
sideways false
status open
\begin_layout Plain Layout
\align center
\begin_inset Graphics
filename graphics/CD4-csaw/ChIP-seq/H3K4me2-PCA-SVsub-CROP.png
lyxscale 25
width 45col%
groupId pcoa-subfig
\end_inset
\end_layout
\begin_layout Plain Layout
\begin_inset Caption Standard
\begin_layout Plain Layout
\series bold
\begin_inset CommandInset label
LatexCommand label
name "fig:PCoA-H3K4me2-good"
\end_inset
H3K4me2, SVs subtracted
\end_layout
\end_inset
\end_layout
\end_inset
\end_layout
\begin_layout Plain Layout
\begin_inset Float figure
wide false
sideways false
status collapsed
\begin_layout Plain Layout
\align center
\begin_inset Graphics
filename graphics/CD4-csaw/ChIP-seq/H3K4me3-PCA-raw-CROP.png
lyxscale 25
width 45col%
groupId pcoa-subfig
\end_inset
\end_layout
\begin_layout Plain Layout
\begin_inset Caption Standard
\begin_layout Plain Layout
\series bold
\begin_inset CommandInset label
LatexCommand label
name "fig:PCoA-H3K4me3-bad"
\end_inset
H3K4me3, no correction
\end_layout
\end_inset
\end_layout
\end_inset
\begin_inset space \hfill{}
\end_inset
\begin_inset Float figure
wide false
sideways false
status collapsed
\begin_layout Plain Layout
\align center
\begin_inset Graphics
filename graphics/CD4-csaw/ChIP-seq/H3K4me3-PCA-SVsub-CROP.png
lyxscale 25
width 45col%
groupId pcoa-subfig
\end_inset
\end_layout
\begin_layout Plain Layout
\begin_inset Caption Standard
\begin_layout Plain Layout
\series bold
\begin_inset CommandInset label
LatexCommand label
name "fig:PCoA-H3K4me3-good"
\end_inset
H3K4me3, SVs subtracted
\end_layout
\end_inset
\end_layout
\end_inset
\end_layout
\begin_layout Plain Layout
\begin_inset Float figure
wide false
sideways false
status collapsed
\begin_layout Plain Layout
\align center
\begin_inset Graphics
filename graphics/CD4-csaw/ChIP-seq/H3K27me3-PCA-raw-CROP.png
lyxscale 25
width 45col%
groupId pcoa-subfig
\end_inset
\end_layout
\begin_layout Plain Layout
\begin_inset Caption Standard
\begin_layout Plain Layout
\series bold
\begin_inset CommandInset label
LatexCommand label
name "fig:PCoA-H3K27me3-bad"
\end_inset
H3K27me3, no correction
\end_layout
\end_inset
\end_layout
\end_inset
\begin_inset space \hfill{}
\end_inset
\begin_inset Float figure
wide false
sideways false
status collapsed
\begin_layout Plain Layout
\align center
\begin_inset Graphics
filename graphics/CD4-csaw/ChIP-seq/H3K27me3-PCA-SVsub-CROP.png
lyxscale 25
width 45col%
groupId pcoa-subfig
\end_inset
\end_layout
\begin_layout Plain Layout
\begin_inset Caption Standard
\begin_layout Plain Layout
\series bold
\begin_inset CommandInset label
LatexCommand label
name "fig:PCoA-H3K27me3-good"
\end_inset
H3K27me3, SVs subtracted
\end_layout
\end_inset
\end_layout
\end_inset
\end_layout
\begin_layout Plain Layout
\begin_inset Flex TODO Note (inline)
status collapsed
\begin_layout Plain Layout
Figure font too small
\end_layout
\end_inset
\end_layout
\begin_layout Plain Layout
\begin_inset Caption Standard
\begin_layout Plain Layout
\begin_inset Argument 1
status collapsed
\begin_layout Plain Layout
PCoA plots of ChIP-seq sliding window data, before and after subtracting
surrogate variables.
\end_layout
\end_inset
\begin_inset CommandInset label
LatexCommand label
name "fig:PCoA-ChIP"
\end_inset
\series bold
PCoA plots of ChIP-seq sliding window data, before and after subtracting
surrogate variables (SVs).
\series default
For each histone mark, a PCoA plot of the first 2 principal coordinates
was created before and after subtraction of SV effects.
Time points are shown by color and cell type by shape, and samples from
the same time point and cell type are enclosed in a shaded area to aid
in visial recognition (this shaded area has no meaning on the plot).
Samples of the same cell type from the same donor are connected with a
line in time point order, showing the
\begin_inset Quotes eld
\end_inset
trajectory
\begin_inset Quotes erd
\end_inset
of each donor's samples over time.
\end_layout
\end_inset
\end_layout
\end_inset
\end_layout
\begin_layout Standard
To investigate whether the location of a peak within the promoter region
was important,
\begin_inset Quotes eld
\end_inset
relative coverage profiles
\begin_inset Quotes erd
\end_inset
were generated.
First, 500-bp sliding windows were tiled around each annotated
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
TSS
\end_layout
\end_inset
: one window centered on the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
TSS
\end_layout
\end_inset
itself, and 10 windows each upstream and downstream, thus covering a 10.5-kb
region centered on the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
TSS
\end_layout
\end_inset
with 21 windows.
Reads in each window for each
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
TSS
\end_layout
\end_inset
were counted in each sample, and the counts were normalized and converted
to
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
logCPM
\end_layout
\end_inset
as in the differential modification analysis.
Then, the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
logCPM
\end_layout
\end_inset
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 analysis of cross-dataset variation patterns
\end_layout
\begin_layout Standard
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
MOFA
\end_layout
\end_inset
was run on all the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
ChIP-seq
\end_layout
\end_inset
windows overlapping consensus peaks for each histone mark, as well as the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
RNA-seq
\end_layout
\end_inset
data, in order to identify patterns of coordinated variation across all
data sets
\begin_inset CommandInset citation
LatexCommand cite
key "Argelaguet2018"
literal "false"
\end_inset
.
The results are summarized in Figure
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:MOFA-master"
plural "false"
caps "false"
noprefix "false"
\end_inset
.
\begin_inset Flex Glossary Term (Capital, pl)
status open
\begin_layout Plain Layout
LF
\end_layout
\end_inset
1, 4, and 5 were determined to explain the most variation consistently
across all data sets (Figure
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:mofa-varexplained"
plural "false"
caps "false"
noprefix "false"
\end_inset
), and scatter plots of these factors show that they also correlate best
with the experimental factors (Figure
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:mofa-lf-scatter"
plural "false"
caps "false"
noprefix "false"
\end_inset
).
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
LF
\end_layout
\end_inset
2 captures the batch effect in the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
RNA-seq
\end_layout
\end_inset
data.
Removing the effect of
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
LF
\end_layout
\end_inset
2 using
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
MOFA
\end_layout
\end_inset
theoretically yields a batch correction that does not depend on knowing
the experimental factors.
When this was attempted, the resulting batch correction was comparable
to ComBat (see Figure
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:RNA-PCA-ComBat-batchsub"
plural "false"
caps "false"
noprefix "false"
\end_inset
), indicating that the ComBat-based batch correction has little room for
improvement given the problems with the data set.
\end_layout
\begin_layout Standard
\begin_inset ERT
status open
\begin_layout Plain Layout
\backslash
afterpage{
\end_layout
\begin_layout Plain Layout
\backslash
begin{landscape}
\end_layout
\end_inset
\end_layout
\begin_layout Standard
\begin_inset Float figure
wide false
sideways false
status open
\begin_layout Plain Layout
\begin_inset Float figure
wide false
sideways false
status collapsed
\begin_layout Plain Layout
\align center
\begin_inset Graphics
filename graphics/CD4-csaw/MOFA-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 LF learned by MOFA, the variance explained by that factor in each
data set (
\begin_inset Quotes eld
\end_inset
view
\begin_inset Quotes erd
\end_inset
) is shown by the shading of the cells in the lower section.
The upper section shows the total fraction of each data set's variance
that is explained by all LFs combined.
\end_layout
\end_inset
\end_layout
\end_inset
\begin_inset space \hfill{}
\end_inset
\begin_inset Float figure
wide false
sideways false
status collapsed
\begin_layout Plain Layout
\align center
\begin_inset Graphics
filename graphics/CD4-csaw/MOFA-LF-scatter-small.png
lyxscale 25
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
were plotted against each other in order to reveal patterns of variation
that are shared across all data sets.
These plots can be interpreted similarly to PCA and PCoA plots.
\end_layout
\end_inset
\end_layout
\end_inset
\end_layout
\begin_layout Plain Layout
\begin_inset Flex TODO Note (inline)
status open
\begin_layout Plain Layout
Figure font a bit too small
\end_layout
\end_inset
\end_layout
\begin_layout Plain Layout
\begin_inset Caption Standard
\begin_layout Plain Layout
\begin_inset Argument 1
status collapsed
\begin_layout Plain Layout
MOFA latent factors identify shared patterns of variation.
\end_layout
\end_inset
\begin_inset CommandInset label
LatexCommand label
name "fig:MOFA-master"
\end_inset
\series bold
MOFA latent factors identify shared patterns of variation.
\series default
MOFA was used to estimate latent factors (LFs) that explain substantial
variation in the RNA-seq data and the ChIP-seq data (a).
Then specific LFs of interest were selected and plotted (b).
\end_layout
\end_inset
\end_layout
\end_inset
\end_layout
\begin_layout Standard
\begin_inset ERT
status open
\begin_layout Plain Layout
\backslash
end{landscape}
\end_layout
\begin_layout Plain Layout
}
\end_layout
\end_inset
\end_layout
\begin_layout Standard
\begin_inset Note Note
status collapsed
\begin_layout Plain Layout
\begin_inset Float figure
wide false
sideways false
status open
\begin_layout Plain Layout
\align center
\begin_inset Graphics
filename graphics/CD4-csaw/MOFA-batch-correct-CROP.png
lyxscale 25
width 100col%
groupId colwidth-raster
\end_inset
\end_layout
\begin_layout Plain Layout
\begin_inset Caption Standard
\begin_layout Plain Layout
\series bold
\begin_inset CommandInset label
LatexCommand label
name "fig:mofa-batchsub"
\end_inset
Result of RNA-seq batch-correction using MOFA latent factors
\end_layout
\end_inset
\end_layout
\end_inset
\end_layout
\end_inset
\end_layout
\begin_layout Section
Results
\end_layout
\begin_layout 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 Subsection
Interpretation of RNA-seq analysis is limited by a major confounding factor
\end_layout
\begin_layout Standard
Genes called as present in the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
RNA-seq
\end_layout
\end_inset
data were tested for differential expression between all time points and
cell types.
The counts of differentially expressed genes are shown in Table
\begin_inset CommandInset ref
LatexCommand ref
reference "tab:Estimated-and-detected-rnaseq"
plural "false"
caps "false"
noprefix "false"
\end_inset
.
Notably, all the results for Day 0 and Day 5 have substantially fewer genes
called differentially expressed than any of the results for other time
points.
This is an unfortunate result of the difference in sample quality between
the two batches of
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
RNA-seq
\end_layout
\end_inset
data.
All the samples in Batch 1, which includes all the samples from Days 0
and 5, have substantially more variability than the samples in Batch 2,
which includes the other time points.
This is reflected in the substantially higher weights assigned to Batch
2 (Figure
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:RNA-seq-weights-vs-covars"
plural "false"
caps "false"
noprefix "false"
\end_inset
).
\begin_inset Float table
wide false
sideways false
status collapsed
\begin_layout Plain Layout
\align center
\begin_inset Tabular
|
\begin_inset Text
\begin_layout Plain Layout
Test
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
Est.
non-null
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
\begin_inset Formula $\mathrm{FDR}\le10\%$
\end_inset
\end_layout
\end_inset
|
|
\begin_inset Text
\begin_layout Plain Layout
Naïve Day 0 vs Day 1
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
5992
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
1613
\end_layout
\end_inset
|
|
\begin_inset Text
\begin_layout Plain Layout
Naïve Day 0 vs Day 5
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
3038
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
32
\end_layout
\end_inset
|
|
\begin_inset Text
\begin_layout Plain Layout
Naïve Day 0 vs Day 14
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
1870
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
190
\end_layout
\end_inset
|
|
\begin_inset Text
\begin_layout Plain Layout
Memory Day 0 vs Day 1
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
3195
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
411
\end_layout
\end_inset
|
|
\begin_inset Text
\begin_layout Plain Layout
Memory Day 0 vs Day 5
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
2688
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
18
\end_layout
\end_inset
|
|
\begin_inset Text
\begin_layout Plain Layout
Memory Day 0 vs Day 14
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
1911
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
227
\end_layout
\end_inset
|
|
\begin_inset Text
\begin_layout Plain Layout
Day 0 Naïve vs Memory
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
0
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
2
\end_layout
\end_inset
|
|
\begin_inset Text
\begin_layout Plain Layout
Day 1 Naïve vs Memory
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
9167
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
5532
\end_layout
\end_inset
|
|
\begin_inset Text
\begin_layout Plain Layout
Day 5 Naïve vs Memory
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
0
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
0
\end_layout
\end_inset
|
|
\begin_inset Text
\begin_layout Plain Layout
Day 14 Naïve vs Memory
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
6446
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
2319
\end_layout
\end_inset
|
\end_inset
\end_layout
\begin_layout Plain Layout
\begin_inset Caption Standard
\begin_layout Plain Layout
\begin_inset Argument 1
status collapsed
\begin_layout Plain Layout
Estimated and detected differentially expressed genes.
\end_layout
\end_inset
\begin_inset CommandInset label
LatexCommand label
name "tab:Estimated-and-detected-rnaseq"
\end_inset
\series bold
Estimated and detected differentially expressed genes.
\series default
\begin_inset Quotes eld
\end_inset
Test
\begin_inset Quotes erd
\end_inset
: Which sample groups were compared;
\begin_inset Quotes eld
\end_inset
Est non-null
\begin_inset Quotes erd
\end_inset
: Estimated number of differentially expressed genes, using the method of
averaging local FDR values
\begin_inset CommandInset citation
LatexCommand cite
key "Phipson2013Thesis"
literal "false"
\end_inset
;
\begin_inset Quotes eld
\end_inset
\begin_inset Formula $\mathrm{FDR}\le10\%$
\end_inset
\begin_inset Quotes erd
\end_inset
: Number of significantly differentially expressed genes at an FDR threshold
of 10%.
The total number of genes tested was 16707.
\end_layout
\end_inset
\end_layout
\end_inset
\begin_inset Note Note
status collapsed
\begin_layout Plain Layout
If float lost issues, reposition randomly until success.
\end_layout
\end_inset
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 naïve and memory samples at Day 0,
despite the fact that only 2 genes were significantly differentially expressed
for this comparison.
Similarly, the small numbers of genes detected for the Day 0 vs Day 5 compariso
ns do not reflect the large separation between these time points in Figure
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:rna-pca-final"
plural "false"
caps "false"
noprefix "false"
\end_inset
.
In addition, the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
MOFA
\end_layout
\end_inset
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
LF
\end_layout
\end_inset
plots in Figure
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:mofa-lf-scatter"
plural "false"
caps "false"
noprefix "false"
\end_inset
.
This suggests that there is indeed a differential expression signal present
in the data for these comparisons, but the large variability in the Batch
1 samples obfuscates this signal at the individual gene level.
As a result, it is impossible to make any meaningful statements about the
\begin_inset Quotes eld
\end_inset
size
\begin_inset Quotes erd
\end_inset
of the gene signature for any time point, since the number of significant
genes as well as the estimated number of differentially expressed genes
depends so strongly on the variations in sample quality in addition to
the size of the differential expression signal in the data.
Gene-set enrichment analyses are similarly impractical.
However, analyses looking at genome-wide patterns of expression are still
practical.
\end_layout
\begin_layout Standard
\begin_inset Float figure
wide false
sideways false
status collapsed
\begin_layout Plain Layout
\align center
\begin_inset Graphics
filename graphics/CD4-csaw/RNA-seq/PCA-final-12-CROP.png
lyxscale 25
width 100col%
groupId colwidth-raster
\end_inset
\end_layout
\begin_layout Plain Layout
\begin_inset Caption Standard
\begin_layout Plain Layout
\begin_inset Argument 1
status collapsed
\begin_layout Plain Layout
PCoA plot of RNA-seq samples after ComBat batch correction.
\end_layout
\end_inset
\begin_inset CommandInset label
LatexCommand label
name "fig:rna-pca-final"
\end_inset
\series bold
PCoA plot of RNA-seq samples after ComBat batch correction.
\series default
Each point represents an individual sample.
Samples with the same combination of cell type and time point are encircled
with a shaded region to aid in visual identification of the sample groups.
Samples of the same cell type from the same donor are connected by lines
to indicate the
\begin_inset Quotes eld
\end_inset
trajectory
\begin_inset Quotes erd
\end_inset
of each donor's cells over time in PCoA space.
\end_layout
\end_inset
\end_layout
\end_inset
\end_layout
\begin_layout Subsection
H3K4 and H3K27 methylation occur in broad regions and are enriched near
promoters
\end_layout
\begin_layout Standard
\begin_inset Float table
wide false
sideways false
status open
\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
|
\begin_inset Text
\begin_layout Plain Layout
Histone Mark
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
# Peaks
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
Mean peak width
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
genome coverage
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
FRiP
\end_layout
\end_inset
|
|
\begin_inset Text
\begin_layout Plain Layout
H3K4me2
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
14,965
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
3,970
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
1.92%
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
14.2%
\end_layout
\end_inset
|
|
\begin_inset Text
\begin_layout Plain Layout
H3K4me3
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
6,163
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
2,946
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
0.588%
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
6.57%
\end_layout
\end_inset
|
|
\begin_inset Text
\begin_layout Plain Layout
H3K27me3
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
18,139
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
18,967
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
11.1%
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
22.5%
\end_layout
\end_inset
|
\end_inset
\end_layout
\begin_layout Plain Layout
\begin_inset Caption Standard
\begin_layout Plain Layout
\begin_inset Argument 1
status collapsed
\begin_layout Plain Layout
Summary of peak-calling statistics.
\end_layout
\end_inset
\begin_inset CommandInset label
LatexCommand label
name "tab:peak-calling-summary"
\end_inset
\series bold
Summary of peak-calling statistics.
\series default
For each histone mark, the number of peaks called using SICER at an IDR
threshold of 0.05, the mean width of those peaks, the fraction of the genome
covered by peaks, and the fraction of reads in peaks (FRiP).
\end_layout
\end_inset
\end_layout
\end_inset
\end_layout
\begin_layout Standard
Table
\begin_inset CommandInset ref
LatexCommand ref
reference "tab:peak-calling-summary"
plural "false"
caps "false"
noprefix "false"
\end_inset
gives a summary of the peak calling statistics for each histone mark.
Consistent with previous observations, all 3 histone marks occur in broad
regions spanning many consecutive nucleosomes, rather than in sharp peaks
as would be expected for a transcription factor or other molecule that
binds to specific sites.
This conclusion is further supported by Figure
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:CCF-with-blacklist"
plural "false"
caps "false"
noprefix "false"
\end_inset
, in which a clear nucleosome-sized periodicity is visible in the cross-correlat
ion value for each sample, indicating that each time a given mark is present
on one histone, it is also likely to be found on adjacent histones as well.
H3K27me3 enrichment in particular is substantially more broad than either
H3K4 mark, with a mean peak width of almost 19,000 bp.
This is also reflected in the periodicity observed in Figure
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:CCF-with-blacklist"
plural "false"
caps "false"
noprefix "false"
\end_inset
, which remains strong much farther out for H3K27me3 than the other marks,
showing H3K27me3 especially tends to be found on long runs of consecutive
histones.
\end_layout
\begin_layout Standard
\begin_inset Flex TODO Note (inline)
status open
\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 to
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
TSS
\end_layout
\end_inset
positions relative to the remainder of the genome.
Interestingly, the
\begin_inset Quotes eld
\end_inset
radius
\begin_inset Quotes erd
\end_inset
within which this enrichment occurs is not the same for every histone mark
(Table
\begin_inset CommandInset ref
LatexCommand ref
reference "tab:effective-promoter-radius"
plural "false"
caps "false"
noprefix "false"
\end_inset
).
For H3K4me2 and H3K4me3, peaks are most enriched within 1
\begin_inset space ~
\end_inset
kbp of
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
TSS
\end_layout
\end_inset
positions, while for H3K27me3, enrichment is broader, extending to 2.5
\begin_inset space ~
\end_inset
kbp.
These
\begin_inset Quotes eld
\end_inset
effective promoter radii
\begin_inset Quotes erd
\end_inset
remain approximately the same across all combinations of experimental condition
(cell type, time point, and donor), so they appear to be a property of
the histone mark itself.
Hence, these radii were used to define the promoter regions for each histone
mark in all further analyses.
\end_layout
\begin_layout Standard
\begin_inset Float figure
wide false
sideways false
status open
\begin_layout Plain Layout
\align center
\begin_inset Graphics
filename graphics/CD4-csaw/Promoter-Peak-Distance-Profile-PAGE1-CROP.pdf
lyxscale 50
width 80col%
\end_inset
\end_layout
\begin_layout Plain Layout
\begin_inset Flex TODO Note (inline)
status open
\begin_layout Plain Layout
Future direction idea: Need a control: shuffle all peaks and repeat, N times.
\end_layout
\end_inset
\end_layout
\begin_layout Plain Layout
\begin_inset Caption Standard
\begin_layout Plain Layout
\begin_inset Argument 1
status collapsed
\begin_layout Plain Layout
Enrichment of peaks in promoter neighborhoods.
\end_layout
\end_inset
\begin_inset CommandInset label
LatexCommand label
name "fig:near-promoter-peak-enrich"
\end_inset
\series bold
Enrichment of peaks in promoter neighborhoods.
\series default
This plot shows the distribution of distances from each annotated transcription
start site in the genome to the nearest called peak.
Each line represents one combination of histone mark, cell type, and time
point.
Distributions are smoothed using kernel density estimation.
TSSs that occur
\emph on
within
\emph default
peaks were excluded from this plot to avoid a large spike at zero that
would overshadow the rest of the distribution.
(Note: this figure was generated using the original peak calls and expression
values from
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
GEO
\end_layout
\end_inset
\begin_inset CommandInset citation
LatexCommand cite
key "LaMere2016"
literal "false"
\end_inset
.)
\end_layout
\end_inset
\end_layout
\end_inset
\end_layout
\begin_layout Standard
\begin_inset Float table
wide false
sideways false
status collapsed
\begin_layout Plain Layout
\align center
\begin_inset Tabular
|
\begin_inset Text
\begin_layout Plain Layout
Histone mark
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
Effective promoter radius
\end_layout
\end_inset
|
|
\begin_inset Text
\begin_layout Plain Layout
H3K4me2
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
1 kbp
\end_layout
\end_inset
|
|
\begin_inset Text
\begin_layout Plain Layout
H3K4me3
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
1 kbp
\end_layout
\end_inset
|
|
\begin_inset Text
\begin_layout Plain Layout
H3K27me3
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
2.5 kbp
\end_layout
\end_inset
|
\end_inset
\end_layout
\begin_layout Plain Layout
\begin_inset Caption Standard
\begin_layout Plain Layout
\begin_inset Argument 1
status collapsed
\begin_layout Plain Layout
Effective promoter radius for each histone mark.
\end_layout
\end_inset
\begin_inset CommandInset label
LatexCommand label
name "tab:effective-promoter-radius"
\end_inset
\series bold
Effective promoter radius for each histone mark.
\series default
These values represent the approximate distance from transcription start
site positions within which an excess of peaks are found, as shown in Figure
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:near-promoter-peak-enrich"
plural "false"
caps "false"
noprefix "false"
\end_inset
.
\end_layout
\end_inset
\end_layout
\end_inset
\end_layout
\begin_layout Standard
\begin_inset Flex TODO Note (inline)
status open
\begin_layout Plain Layout
Consider also showing figure for distance to nearest peak center, and reference
median peak size once that is known.
\end_layout
\end_inset
\end_layout
\begin_layout Subsection
Correlations between gene expression and promoter methylation follow expected
genome-wide trends
\end_layout
\begin_layout Standard
H3K4me2 and H3K4me2 have previously been reported as activating marks whose
presence in a gene's promoter is associated with higher gene expression,
while H3K27me3 has been reported as inactivating
\begin_inset CommandInset citation
LatexCommand cite
key "LaMere2016,LaMere2017"
literal "false"
\end_inset
.
The data are consistent with this characterization: genes whose promoters
(as defined by the radii for each histone mark listed in
\begin_inset CommandInset ref
LatexCommand ref
reference "tab:effective-promoter-radius"
plural "false"
caps "false"
noprefix "false"
\end_inset
) overlap with a H3K4me2 or H3K4me3 peak tend to have higher expression
than those that don't, while H3K27me3 is likewise associated with lower
gene expression, as shown in
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:fpkm-by-peak"
plural "false"
caps "false"
noprefix "false"
\end_inset
.
This pattern holds across all combinations of cell type and time point
(Welch's
\emph on
t
\emph default
-test, all
\begin_inset Formula $p\textrm{-values}\ll2.2\times10^{-16}$
\end_inset
).
The difference in average
\begin_inset Formula $\log_{2}$
\end_inset
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
FPKM
\end_layout
\end_inset
values when a peak overlaps the promoter is about
\begin_inset Formula $+5.67$
\end_inset
for H3K4me2,
\begin_inset Formula $+5.76$
\end_inset
for H3K4me2, and
\begin_inset Formula $-4.00$
\end_inset
for H3K27me3.
\end_layout
\begin_layout Standard
\begin_inset ERT
status open
\begin_layout Plain Layout
\backslash
afterpage{
\end_layout
\begin_layout Plain Layout
\backslash
begin{landscape}
\end_layout
\end_inset
\end_layout
\begin_layout Standard
\begin_inset Float figure
wide false
sideways false
status collapsed
\begin_layout Plain Layout
\align center
\begin_inset Graphics
filename graphics/CD4-csaw/FPKM-by-Peak-Violin-Plots-CROP.pdf
lyxscale 50
height 80theight%
\end_inset
\end_layout
\begin_layout Plain Layout
\begin_inset Caption Standard
\begin_layout Plain Layout
\begin_inset Argument 1
status collapsed
\begin_layout Plain Layout
Expression distributions of genes with and without promoter peaks.
\end_layout
\end_inset
\begin_inset CommandInset label
LatexCommand label
name "fig:fpkm-by-peak"
\end_inset
\series bold
Expression distributions of genes with and without promoter peaks.
\series default
For each histone mark in each experimental condition, the average RNA-seq
abundance (
\begin_inset Formula $\log_{2}$
\end_inset
FPKM) of each gene across all 4 donors was calculated.
Genes were grouped based on whether or not a peak was called in their promoters
in that condition, and the distribution of abundance values was plotted
for the no-peak and peak groups.
(Note: this figure was generated using the original peak calls and expression
values from
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
GEO
\end_layout
\end_inset
\begin_inset CommandInset citation
LatexCommand cite
key "LaMere2016"
literal "false"
\end_inset
.)
\end_layout
\end_inset
\end_layout
\end_inset
\end_layout
\begin_layout Standard
\begin_inset ERT
status open
\begin_layout Plain Layout
\backslash
end{landscape}
\end_layout
\begin_layout Plain Layout
}
\end_layout
\end_inset
\end_layout
\begin_layout Subsection
Gene expression and promoter histone methylation patterns show convergence
between naïve and memory cells at day 14
\end_layout
\begin_layout Standard
We hypothesized that if naïve cells had differentiated into memory cells
by Day 14, then their patterns of expression and histone modification should
converge with those of memory cells at Day 14.
Figure
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:PCoA-promoters"
plural "false"
caps "false"
noprefix "false"
\end_inset
shows the patterns of variation in all 3 histone marks in the promoter
regions of the genome using
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
PCoA
\end_layout
\end_inset
.
All 3 marks show a noticeable convergence between the naïve and memory
samples at day 14, visible as an overlapping of the day 14 groups on each
plot.
This is consistent with the counts of significantly differentially modified
promoters and estimates of the total numbers of differentially modified
promoters shown in Table
\begin_inset CommandInset ref
LatexCommand ref
reference "tab:Number-signif-promoters"
plural "false"
caps "false"
noprefix "false"
\end_inset
.
For all histone marks, evidence of differential modification between naïve
and memory samples was detected at every time point except day 14.
The day 14 convergence pattern is also present in the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
RNA-seq
\end_layout
\end_inset
data (Figure
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:RNA-PCA-group"
plural "false"
caps "false"
noprefix "false"
\end_inset
), albeit in the 2nd and 3rd principal coordinates, indicating that it is
not the most dominant pattern driving gene expression.
Taken together, the data show that promoter histone methylation for these
3 histone marks and RNA expression for naïve and memory cells are most
similar at day 14, the furthest time point after activation.
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
MOFA
\end_layout
\end_inset
was also able to capture this day 14 convergence pattern in
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
LF
\end_layout
\end_inset
5 (Figure
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:mofa-lf-scatter"
plural "false"
caps "false"
noprefix "false"
\end_inset
), which accounts for shared variation across all 3 histone marks and the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
RNA-seq
\end_layout
\end_inset
data, confirming that this convergence is a coordinated pattern across
all 4 data sets.
While this observation does not prove that the naïve cells have differentiated
into memory cells at Day 14, it is consistent with that hypothesis.
\end_layout
\begin_layout Standard
\begin_inset Float figure
placement p
wide false
sideways false
status collapsed
\begin_layout Plain Layout
\align center
\begin_inset Float figure
wide false
sideways false
status open
\begin_layout Plain Layout
\align center
\begin_inset Graphics
filename graphics/CD4-csaw/ChIP-seq/H3K4me2-promoter-PCA-group-CROP.png
lyxscale 25
width 45col%
groupId pcoa-prom-subfig
\end_inset
\end_layout
\begin_layout Plain Layout
\begin_inset Caption Standard
\begin_layout Plain Layout
\begin_inset CommandInset label
LatexCommand label
name "fig:PCoA-H3K4me2-prom"
\end_inset
PCoA plot of H3K4me2 promoters, after subtracting surrogate variables.
\end_layout
\end_inset
\end_layout
\end_inset
\begin_inset space \hfill{}
\end_inset
\begin_inset Float figure
wide false
sideways false
status open
\begin_layout Plain Layout
\align center
\begin_inset Graphics
filename graphics/CD4-csaw/ChIP-seq/H3K4me3-promoter-PCA-group-CROP.png
lyxscale 25
width 45col%
groupId pcoa-prom-subfig
\end_inset
\end_layout
\begin_layout Plain Layout
\begin_inset Caption Standard
\begin_layout Plain Layout
\begin_inset CommandInset label
LatexCommand label
name "fig:PCoA-H3K4me3-prom"
\end_inset
PCoA plot of H3K4me3 promoters, after subtracting surrogate variables.
\end_layout
\end_inset
\end_layout
\end_inset
\end_layout
\begin_layout Plain Layout
\align center
\begin_inset Float figure
wide false
sideways false
status open
\begin_layout Plain Layout
\align center
\begin_inset Graphics
filename graphics/CD4-csaw/ChIP-seq/H3K27me3-promoter-PCA-group-CROP.png
lyxscale 25
width 45col%
groupId pcoa-prom-subfig
\end_inset
\end_layout
\begin_layout Plain Layout
\begin_inset Caption Standard
\begin_layout Plain Layout
\begin_inset CommandInset label
LatexCommand label
name "fig:PCoA-H3K27me3-prom"
\end_inset
PCoA plot of H3K27me3 promoters, after subtracting surrogate variables.
\end_layout
\end_inset
\end_layout
\end_inset
\begin_inset space \hfill{}
\end_inset
\begin_inset Float figure
wide false
sideways false
status open
\begin_layout Plain Layout
\align center
\begin_inset Graphics
filename graphics/CD4-csaw/RNA-seq/PCA-final-23-CROP.png
lyxscale 25
width 45col%
groupId pcoa-prom-subfig
\end_inset
\end_layout
\begin_layout Plain Layout
\begin_inset Caption Standard
\begin_layout Plain Layout
\begin_inset CommandInset label
LatexCommand label
name "fig:RNA-PCA-group"
\end_inset
RNA-seq PCoA, after ComBat batch correction, showing principal coordinates
2 and 3.
\end_layout
\end_inset
\end_layout
\end_inset
\end_layout
\begin_layout Plain Layout
\begin_inset Flex TODO Note (inline)
status open
\begin_layout Plain Layout
Figure font too small
\end_layout
\end_inset
\end_layout
\begin_layout Plain Layout
\begin_inset Caption Standard
\begin_layout Plain Layout
\begin_inset Argument 1
status collapsed
\begin_layout Plain Layout
PCoA plots for promoter ChIP-seq and expression RNA-seq data
\end_layout
\end_inset
\begin_inset CommandInset label
LatexCommand label
name "fig:PCoA-promoters"
\end_inset
\series bold
PCoA plots for promoter ChIP-seq and expression RNA-seq data.
\series default
Each point represents an individual sample.
Samples with the same combination of cell type and time point are encircled
with a shaded region to aid in visual identification of the sample groups.
Samples of the same cell type from the same donor are connected by lines
to indicate the
\begin_inset Quotes eld
\end_inset
trajectory
\begin_inset Quotes erd
\end_inset
of each donor's cells over time in PCoA space.
\end_layout
\end_inset
\end_layout
\end_inset
\end_layout
\begin_layout Standard
\begin_inset ERT
status open
\begin_layout Plain Layout
\backslash
afterpage{
\end_layout
\begin_layout Plain Layout
\backslash
begin{landscape}
\end_layout
\end_inset
\end_layout
\begin_layout Standard
\begin_inset Float table
wide false
sideways false
status collapsed
\begin_layout Plain Layout
\align center
\begin_inset Tabular
|
\begin_inset Text
\begin_layout Plain Layout
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
Number of significant promoters
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
Est.
differentially modified promoters
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
\end_layout
\end_inset
|
|
\begin_inset Text
\begin_layout Plain Layout
Time Point
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
H3K4me2
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
H3K4me3
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
H3K27me3
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
H3K4me2
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
H3K4me3
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
H3K27me3
\end_layout
\end_inset
|
|
\begin_inset Text
\begin_layout Plain Layout
Day 0
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
4553
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
927
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
6
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
9967
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
4149
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
2404
\end_layout
\end_inset
|
|
\begin_inset Text
\begin_layout Plain Layout
Day 1
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
567
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
278
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
1570
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
4370
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
2145
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
6598
\end_layout
\end_inset
|
|
\begin_inset Text
\begin_layout Plain Layout
Day 5
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
2313
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
139
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
490
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
9450
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
1148
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
4141
\end_layout
\end_inset
|
|
\begin_inset Text
\begin_layout Plain Layout
Day 14
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
0
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
0
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
0
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
0
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
0
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
0
\end_layout
\end_inset
|
\end_inset
\end_layout
\begin_layout Plain Layout
\begin_inset Caption Standard
\begin_layout Plain Layout
\begin_inset Argument 1
status collapsed
\begin_layout Plain Layout
Number of differentially modified promoters between naïve and memory cells
at each time point after activation.
\end_layout
\end_inset
\begin_inset CommandInset label
LatexCommand label
name "tab:Number-signif-promoters"
\end_inset
\series bold
Number of differentially modified promoters between naïve and memory cells
at each time point after activation.
\series default
This table shows both the number of differentially modified promoters detected
at a 10% FDR threshold (left half), and the total number of differentially
modified promoters estimated using the method of averaging local FDR estimates
\begin_inset CommandInset citation
LatexCommand cite
key "Phipson2016"
literal "false"
\end_inset
(right half).
\end_layout
\end_inset
\end_layout
\end_inset
\end_layout
\begin_layout Standard
\begin_inset ERT
status open
\begin_layout Plain Layout
\backslash
end{landscape}
\end_layout
\begin_layout Plain Layout
}
\end_layout
\end_inset
\end_layout
\begin_layout Subsection
Location of H3K4me2 and H3K4me3 promoter coverage associates with gene expressio
n
\end_layout
\begin_layout Standard
\begin_inset Flex TODO Note (inline)
status open
\begin_layout Plain Layout
Make sure use of coverage/abundance/whatever is consistent.
\end_layout
\end_inset
\end_layout
\begin_layout Standard
\begin_inset Flex TODO Note (inline)
status open
\begin_layout Plain Layout
For the figures in this section and the next, the group labels are arbitrary,
so if time allows, it would be good to manually reorder them in a logical
way, e.g.
most upstream to most downstream.
If this is done, make sure to update the text with the correct group labels.
\end_layout
\end_inset
\end_layout
\begin_layout Standard
To test whether the position of a histone mark relative to a gene's
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
TSS
\end_layout
\end_inset
was important, we looked at the
\begin_inset Quotes eld
\end_inset
landscape
\begin_inset Quotes erd
\end_inset
of
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
ChIP-seq
\end_layout
\end_inset
read coverage in naïve Day 0 samples within 5 kbp of each gene's
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
TSS
\end_layout
\end_inset
by binning reads into 500-bp windows tiled across each promoter
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
logCPM
\end_layout
\end_inset
values were calculated for the bins in each promoter and then the average
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
logCPM
\end_layout
\end_inset
for each promoter's bins was normalized to zero, such that the values represent
coverage relative to other regions of the same promoter rather than being
proportional to absolute read count.
The promoters were then clustered based on the normalized bin abundances
using
\begin_inset Formula $k$
\end_inset
-means clustering with
\begin_inset Formula $K=6$
\end_inset
.
Different values of
\begin_inset Formula $K$
\end_inset
were also tested, but did not substantially change the interpretation of
the data.
\end_layout
\begin_layout Standard
For H3K4me2, plotting the average bin abundances for each cluster reveals
a simple pattern (Figure
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:H3K4me2-neighborhood-clusters"
plural "false"
caps "false"
noprefix "false"
\end_inset
): Cluster 5 represents a completely flat promoter coverage profile, likely
consisting of genes with no H3K4me2 methylation in the promoter.
All the other clusters represent a continuum of peak positions relative
to the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
TSS
\end_layout
\end_inset
.
In order from most upstream to most downstream, they are Clusters 6, 4,
3, 1, and 2.
There do not appear to be any clusters representing coverage patterns other
than lone peaks, such as coverage troughs or double peaks.
Next, all promoters were plotted in a
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
PCA
\end_layout
\end_inset
plot based on the same relative bin abundance data, and colored based on
cluster membership (Figure
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:H3K4me2-neighborhood-pca"
plural "false"
caps "false"
noprefix "false"
\end_inset
).
The
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
PCA
\end_layout
\end_inset
plot shows Cluster 5 (the
\begin_inset Quotes eld
\end_inset
no peak
\begin_inset Quotes erd
\end_inset
cluster) at the center, with the other clusters arranged in a counter-clockwise
arc around it in the order noted above, from most upstream peak to most
downstream.
Notably, the
\begin_inset Quotes eld
\end_inset
clusters
\begin_inset Quotes erd
\end_inset
form a single large
\begin_inset Quotes eld
\end_inset
cloud
\begin_inset Quotes erd
\end_inset
with no apparent separation between them, further supporting the conclusion
that these clusters represent an arbitrary partitioning of a continuous
distribution of promoter coverage landscapes.
While the clusters are a useful abstraction that aids in visualization,
they are ultimately not an accurate representation of the data.
The continuous nature of the distribution also explains why different values
of
\begin_inset Formula $K$
\end_inset
led to similar conclusions.
\end_layout
\begin_layout Standard
\begin_inset ERT
status open
\begin_layout Plain Layout
\backslash
afterpage{
\end_layout
\begin_layout Plain Layout
\backslash
begin{landscape}
\end_layout
\end_inset
\end_layout
\begin_layout Standard
\begin_inset Float figure
wide false
sideways false
status collapsed
\begin_layout Plain Layout
\align center
\begin_inset Float figure
wide false
sideways false
status open
\begin_layout Plain Layout
\align center
\begin_inset Graphics
filename graphics/CD4-csaw/ChIP-seq/H3K4me2-neighborhood-clusters-CROP.png
lyxscale 25
width 30col%
groupId covprof-subfig
\end_inset
\end_layout
\begin_layout Plain Layout
\begin_inset Caption Standard
\begin_layout Plain Layout
\series bold
\begin_inset CommandInset label
LatexCommand label
name "fig:H3K4me2-neighborhood-clusters"
\end_inset
Average relative coverage for each bin in each cluster.
\end_layout
\end_inset
\end_layout
\end_inset
\begin_inset space \hfill{}
\end_inset
\begin_inset Float figure
wide false
sideways false
status open
\begin_layout Plain Layout
\align center
\begin_inset Graphics
filename graphics/CD4-csaw/ChIP-seq/H3K4me2-neighborhood-PCA-CROP.png
lyxscale 25
width 30col%
groupId covprof-subfig
\end_inset
\end_layout
\begin_layout Plain Layout
\begin_inset Caption Standard
\begin_layout Plain Layout
\begin_inset CommandInset label
LatexCommand label
name "fig:H3K4me2-neighborhood-pca"
\end_inset
PCA of relative coverage depth, colored by K-means cluster membership.
\end_layout
\end_inset
\end_layout
\end_inset
\begin_inset space \hfill{}
\end_inset
\begin_inset Float figure
wide false
sideways false
status open
\begin_layout Plain Layout
\align center
\begin_inset Graphics
filename graphics/CD4-csaw/ChIP-seq/H3K4me2-neighborhood-expression-CROP.png
lyxscale 25
width 30col%
groupId covprof-subfig
\end_inset
\end_layout
\begin_layout Plain Layout
\begin_inset Caption Standard
\begin_layout Plain Layout
\begin_inset CommandInset label
LatexCommand label
name "fig:H3K4me2-neighborhood-expression"
\end_inset
Gene expression grouped by promoter coverage clusters.
\end_layout
\end_inset
\end_layout
\end_inset
\end_layout
\begin_layout Plain Layout
\begin_inset Flex TODO Note (inline)
status open
\begin_layout Plain Layout
Figure font too small
\end_layout
\end_inset
\end_layout
\begin_layout Plain Layout
\begin_inset Caption Standard
\begin_layout Plain Layout
\begin_inset Argument 1
status collapsed
\begin_layout Plain Layout
K-means clustering of promoter H3K4me2 relative coverage depth in naïve
day 0 samples.
\end_layout
\end_inset
\begin_inset CommandInset label
LatexCommand label
name "fig:H3K4me2-neighborhood"
\end_inset
\series bold
K-means clustering of promoter H3K4me2 relative coverage depth in naïve
day 0 samples.
\series default
H3K4me2 ChIP-seq reads were binned into 500-bp windows tiled across each
promoter from 5
\begin_inset space ~
\end_inset
kbp upstream to 5
\begin_inset space ~
\end_inset
kbp downstream, and the logCPM values were normalized within each promoter
to an average of 0, yielding relative coverage depths.
These were then grouped using K-means clustering with
\begin_inset Formula $K=6$
\end_inset
,
\series bold
\series default
and the average bin values were plotted for each cluster (a).
The
\begin_inset Formula $x$
\end_inset
-axis is the genomic coordinate of each bin relative to the the transcription
start site, and the
\begin_inset Formula $y$
\end_inset
-axis is the mean relative coverage depth of that bin across all promoters
in the cluster.
Each line represents the average
\begin_inset Quotes eld
\end_inset
shape
\begin_inset Quotes erd
\end_inset
of the promoter coverage for promoters in that cluster.
PCA was performed on the same data, and the first two PCs were plotted,
coloring each point by its K-means cluster identity (b).
For each cluster, the distribution of gene expression values was plotted
(c).
\end_layout
\end_inset
\end_layout
\end_inset
\end_layout
\begin_layout Standard
\begin_inset ERT
status open
\begin_layout Plain Layout
\backslash
end{landscape}
\end_layout
\begin_layout Plain Layout
}
\end_layout
\end_inset
\end_layout
\begin_layout Standard
\begin_inset Flex TODO Note (inline)
status open
\begin_layout Plain Layout
Should have a table of p-values on difference of means between Cluster 5
and the others.
\end_layout
\end_inset
\end_layout
\begin_layout Standard
To investigate the association between relative peak position and gene expressio
n, we plotted the Naïve Day 0 expression for the genes in each cluster (Figure
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:H3K4me2-neighborhood-expression"
plural "false"
caps "false"
noprefix "false"
\end_inset
).
Most genes in Cluster 5, the
\begin_inset Quotes eld
\end_inset
no peak
\begin_inset Quotes erd
\end_inset
cluster, have low expression values.
Taking this as the
\begin_inset Quotes eld
\end_inset
baseline
\begin_inset Quotes erd
\end_inset
distribution when no H3K4me2 methylation is present, we can compare the
other clusters' distributions to determine which peak positions are associated
with elevated expression.
As might be expected, the 3 clusters representing peaks closest to the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
TSS
\end_layout
\end_inset
, Clusters 1, 3, and 4, show the highest average expression distributions.
Specifically, these clusters all have their highest
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
ChIP-seq
\end_layout
\end_inset
abundance within 1kb of the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
TSS
\end_layout
\end_inset
, consistent with the previously determined promoter radius.
In contrast, cluster 6, which represents peaks several kbp upstream of
the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
TSS
\end_layout
\end_inset
, shows a slightly higher average expression than baseline, while Cluster
2, which represents peaks several kbp downstream, doesn't appear to show
any appreciable difference.
Interestingly, the cluster with the highest average expression is Cluster
1, which represents peaks about 1 kbp downstream of the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
TSS
\end_layout
\end_inset
, rather than Cluster 3, which represents peaks centered directly at the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
TSS
\end_layout
\end_inset
.
This suggests that conceptualizing the promoter as a region centered on
the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
TSS
\end_layout
\end_inset
with a certain
\begin_inset Quotes eld
\end_inset
radius
\begin_inset Quotes erd
\end_inset
may be an oversimplification – a peak that is a specific distance from
the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
TSS
\end_layout
\end_inset
may have a different degree of influence depending on whether it is upstream
or downstream of the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
TSS
\end_layout
\end_inset
.
\end_layout
\begin_layout Standard
All observations described above for H3K4me2
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
ChIP-seq
\end_layout
\end_inset
also appear to hold for H3K4me3 as well (Figure
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:H3K4me3-neighborhood"
plural "false"
caps "false"
noprefix "false"
\end_inset
).
This is expected, since there is a high correlation between the positions
where both histone marks occur.
\end_layout
\begin_layout Standard
\begin_inset ERT
status open
\begin_layout Plain Layout
\backslash
afterpage{
\end_layout
\begin_layout Plain Layout
\backslash
begin{landscape}
\end_layout
\end_inset
\end_layout
\begin_layout Standard
\begin_inset Float figure
wide false
sideways false
status collapsed
\begin_layout Plain Layout
\align center
\begin_inset Float figure
wide false
sideways false
status open
\begin_layout Plain Layout
\align center
\begin_inset Graphics
filename graphics/CD4-csaw/ChIP-seq/H3K4me3-neighborhood-clusters-CROP.png
lyxscale 25
width 30col%
groupId covprof-subfig
\end_inset
\end_layout
\begin_layout Plain Layout
\begin_inset Caption Standard
\begin_layout Plain Layout
\begin_inset CommandInset label
LatexCommand label
name "fig:H3K4me3-neighborhood-clusters"
\end_inset
Average relative coverage for each bin in each cluster.
\end_layout
\end_inset
\end_layout
\end_inset
\begin_inset space \hfill{}
\end_inset
\begin_inset Float figure
wide false
sideways false
status open
\begin_layout Plain Layout
\align center
\begin_inset Graphics
filename graphics/CD4-csaw/ChIP-seq/H3K4me3-neighborhood-PCA-CROP.png
lyxscale 25
width 30col%
groupId covprof-subfig
\end_inset
\end_layout
\begin_layout Plain Layout
\begin_inset Caption Standard
\begin_layout Plain Layout
\begin_inset CommandInset label
LatexCommand label
name "fig:H3K4me3-neighborhood-pca"
\end_inset
PCA of relative coverage depth, colored by K-means cluster membership.
\end_layout
\end_inset
\end_layout
\end_inset
\begin_inset space \hfill{}
\end_inset
\begin_inset Float figure
wide false
sideways false
status open
\begin_layout Plain Layout
\align center
\begin_inset Graphics
filename graphics/CD4-csaw/ChIP-seq/H3K4me3-neighborhood-expression-CROP.png
lyxscale 25
width 30col%
groupId covprof-subfig
\end_inset
\end_layout
\begin_layout Plain Layout
\begin_inset Caption Standard
\begin_layout Plain Layout
\begin_inset CommandInset label
LatexCommand label
name "fig:H3K4me3-neighborhood-expression"
\end_inset
Gene expression grouped by promoter coverage clusters.
\end_layout
\end_inset
\end_layout
\end_inset
\end_layout
\begin_layout Plain Layout
\begin_inset Caption Standard
\begin_layout Plain Layout
\begin_inset Argument 1
status collapsed
\begin_layout Plain Layout
K-means clustering of promoter H3K4me3 relative coverage depth in naïve
day 0 samples.
\end_layout
\end_inset
\begin_inset CommandInset label
LatexCommand label
name "fig:H3K4me3-neighborhood"
\end_inset
\series bold
K-means clustering of promoter H3K4me3 relative coverage depth in naïve
day 0 samples.
\series default
H3K4me3 ChIP-seq reads were binned into 500-bp windows tiled across each
promoter from 5
\begin_inset space ~
\end_inset
kbp upstream to 5
\begin_inset space ~
\end_inset
kbp downstream, and the logCPM values were normalized within each promoter
to an average of 0, yielding relative coverage depths.
These were then grouped using K-means clustering with
\begin_inset Formula $K=6$
\end_inset
,
\series bold
\series default
and the average bin values were plotted for each cluster (a).
The
\begin_inset Formula $x$
\end_inset
-axis is the genomic coordinate of each bin relative to the the transcription
start site, and the
\begin_inset Formula $y$
\end_inset
-axis is the mean relative coverage depth of that bin across all promoters
in the cluster.
Each line represents the average
\begin_inset Quotes eld
\end_inset
shape
\begin_inset Quotes erd
\end_inset
of the promoter coverage for promoters in that cluster.
PCA was performed on the same data, and the first two PCs were plotted,
coloring each point by its K-means cluster identity (b).
For each cluster, the distribution of gene expression values was plotted
(c).
\end_layout
\end_inset
\end_layout
\end_inset
\end_layout
\begin_layout Standard
\begin_inset ERT
status open
\begin_layout Plain Layout
\backslash
end{landscape}
\end_layout
\begin_layout Plain Layout
}
\end_layout
\end_inset
\end_layout
\begin_layout Subsection
Patterns of H3K27me3 promoter coverage associate with gene expression
\end_layout
\begin_layout Standard
Unlike both H3K4 marks, whose main patterns of variation appear directly
related to the size and position of a single peak within the promoter,
the patterns of H3K27me3 methylation in promoters are more complex (Figure
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:H3K27me3-neighborhood"
plural "false"
caps "false"
noprefix "false"
\end_inset
).
Once again looking at the relative coverage in a 500-bp wide bins in a
5kb radius around each
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
TSS
\end_layout
\end_inset
, promoters were clustered based on the normalized relative coverage values
in each bin using
\begin_inset Formula $k$
\end_inset
-means clustering with
\begin_inset Formula $K=6$
\end_inset
(Figure
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:H3K27me3-neighborhood-clusters"
plural "false"
caps "false"
noprefix "false"
\end_inset
).
This time, 3
\begin_inset Quotes eld
\end_inset
axes
\begin_inset Quotes erd
\end_inset
of variation can be observed, each represented by 2 clusters with opposing
patterns.
The first axis is greater upstream coverage (Cluster 1) vs.
greater downstream coverage (Cluster 3); the second axis is the coverage
at the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
TSS
\end_layout
\end_inset
itself: peak (Cluster 4) or trough (Cluster 2); lastly, the third axis
represents a trough upstream of the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
TSS
\end_layout
\end_inset
(Cluster 5) vs.
downstream of the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
TSS
\end_layout
\end_inset
(Cluster 6).
Referring to these opposing pairs of clusters as axes of variation is justified
, because they correspond precisely to the first 3
\begin_inset Flex Glossary Term (pl)
status open
\begin_layout Plain Layout
PC
\end_layout
\end_inset
in the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
PCA
\end_layout
\end_inset
plot of the relative coverage values (Figure
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:H3K27me3-neighborhood-pca"
plural "false"
caps "false"
noprefix "false"
\end_inset
).
The
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
PCA
\end_layout
\end_inset
plot reveals that as in the case of H3K4me2, all the
\begin_inset Quotes eld
\end_inset
clusters
\begin_inset Quotes erd
\end_inset
are really just sections of a single connected cloud rather than discrete
clusters.
The cloud is approximately ellipsoid-shaped, with each PC being an axis
of the ellipse, and each cluster consisting of a pyramidal section of the
ellipsoid.
\end_layout
\begin_layout Standard
\begin_inset ERT
status open
\begin_layout Plain Layout
\backslash
afterpage{
\end_layout
\begin_layout Plain Layout
\backslash
begin{landscape}
\end_layout
\end_inset
\end_layout
\begin_layout Standard
\begin_inset Float figure
wide false
sideways false
status open
\begin_layout Plain Layout
\align center
\begin_inset Float figure
wide false
sideways false
status open
\begin_layout Plain Layout
\align center
\begin_inset Graphics
filename graphics/CD4-csaw/ChIP-seq/H3K27me3-neighborhood-clusters-CROP.png
lyxscale 25
width 30col%
groupId covprof-subfig
\end_inset
\end_layout
\begin_layout Plain Layout
\begin_inset Caption Standard
\begin_layout Plain Layout
\begin_inset CommandInset label
LatexCommand label
name "fig:H3K27me3-neighborhood-clusters"
\end_inset
Average relative coverage for each bin in each cluster.
\end_layout
\end_inset
\end_layout
\end_inset
\begin_inset space \hfill{}
\end_inset
\begin_inset Float figure
wide false
sideways false
status open
\begin_layout Plain Layout
\align center
\begin_inset Graphics
filename graphics/CD4-csaw/ChIP-seq/H3K27me3-neighborhood-PCA-CROP.png
lyxscale 25
width 30col%
groupId covprof-subfig
\end_inset
\end_layout
\begin_layout Plain Layout
\begin_inset Caption Standard
\begin_layout Plain Layout
\begin_inset CommandInset label
LatexCommand label
name "fig:H3K27me3-neighborhood-pca"
\end_inset
PCA of relative coverage depth, colored by K-means cluster membership.
\end_layout
\end_inset
\end_layout
\end_inset
\begin_inset space \hfill{}
\end_inset
\begin_inset Float figure
wide false
sideways false
status open
\begin_layout Plain Layout
\align center
\begin_inset Graphics
filename graphics/CD4-csaw/ChIP-seq/H3K27me3-neighborhood-expression-CROP.png
lyxscale 25
width 30col%
groupId covprof-subfig
\end_inset
\end_layout
\begin_layout Plain Layout
\begin_inset Caption Standard
\begin_layout Plain Layout
\begin_inset CommandInset label
LatexCommand label
name "fig:H3K27me3-neighborhood-expression"
\end_inset
Gene expression grouped by promoter coverage clusters.
\end_layout
\end_inset
\end_layout
\end_inset
\end_layout
\begin_layout Plain Layout
\begin_inset 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
\begin_inset Argument 1
status collapsed
\begin_layout Plain Layout
K-means clustering of promoter H3K27me3 relative coverage depth in naïve
day 0 samples.
\end_layout
\end_inset
\begin_inset CommandInset label
LatexCommand label
name "fig:H3K27me3-neighborhood"
\end_inset
\series bold
K-means clustering of promoter H3K27me3 relative coverage depth in naïve
day 0 samples.
\series default
H3K27me3 ChIP-seq reads were binned into 500-bp windows tiled across each
promoter from 5
\begin_inset space ~
\end_inset
kbp upstream to 5
\begin_inset space ~
\end_inset
kbp downstream, and the logCPM values were normalized within each promoter
to an average of 0, yielding relative coverage depths.
These were then grouped using
\begin_inset Formula $k$
\end_inset
-means clustering with
\begin_inset Formula $K=6$
\end_inset
,
\series bold
\series default
and the average bin values were plotted for each cluster (a).
The
\begin_inset Formula $x$
\end_inset
-axis is the genomic coordinate of each bin relative to the the transcription
start site, and the
\begin_inset Formula $y$
\end_inset
-axis is the mean relative coverage depth of that bin across all promoters
in the cluster.
Each line represents the average
\begin_inset Quotes eld
\end_inset
shape
\begin_inset Quotes erd
\end_inset
of the promoter coverage for promoters in that cluster.
PCA was performed on the same data, and the first two PCs were plotted,
coloring each point by its K-means cluster identity (b).
(Note: In (b), Cluster 6 is hidden behind all the other clusters.) For each
cluster, the distribution of gene expression values was plotted (c).
\end_layout
\end_inset
\end_layout
\end_inset
\end_layout
\begin_layout Standard
\begin_inset ERT
status open
\begin_layout Plain Layout
\backslash
end{landscape}
\end_layout
\begin_layout Plain Layout
}
\end_layout
\end_inset
\end_layout
\begin_layout Standard
In Figure
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:H3K27me3-neighborhood-expression"
plural "false"
caps "false"
noprefix "false"
\end_inset
, we can see that Clusters 1 and 2 are the only clusters with higher gene
expression than the others.
For Cluster 2, this is expected, since this cluster represents genes with
depletion of H3K27me3 near the promoter.
Hence, elevated expression in cluster 2 is consistent with the conventional
view of H3K27me3 as a deactivating mark.
However, Cluster 1, the cluster with the most elevated gene expression,
represents genes with elevated coverage upstream of the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
TSS
\end_layout
\end_inset
, or equivalently, decreased coverage downstream, inside the gene body.
The opposite pattern, in which H3K27me3 is more abundant within the gene
body and less abundance in the upstream promoter region, does not show
any elevation in gene expression.
As with H3K4me2, this shows that the location of H3K27 trimethylation relative
to the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
TSS
\end_layout
\end_inset
is potentially an important factor beyond simple proximity.
\end_layout
\begin_layout Standard
\begin_inset Note Note
status open
\begin_layout Plain Layout
\begin_inset Flex TODO Note (inline)
status open
\begin_layout Plain Layout
Show the figures where the negative result ended this line of inquiry.
I need to debug some errors resulting from an R upgrade to do this.
\end_layout
\end_inset
\end_layout
\begin_layout Subsection
Defined pattern analysis
\end_layout
\begin_layout Plain Layout
\begin_inset Flex TODO Note (inline)
status open
\begin_layout Plain Layout
This was where I defined interesting expression patterns and then looked
at initial relative promoter coverage for each expression pattern.
Negative result.
I forgot about this until recently.
Worth including? Remember to also write methods.
\end_layout
\end_inset
\end_layout
\begin_layout Subsection
Promoter CpG islands?
\end_layout
\begin_layout Plain Layout
\begin_inset Flex TODO Note (inline)
status open
\begin_layout Plain Layout
I forgot until recently about the work I did on this.
Worth including? Remember to also write methods.
\end_layout
\end_inset
\end_layout
\end_inset
\end_layout
\begin_layout Section
Discussion
\end_layout
\begin_layout 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
Each histone mark's
\begin_inset Quotes eld
\end_inset
effective promoter extent
\begin_inset Quotes erd
\end_inset
must be determined empirically
\end_layout
\begin_layout Standard
Figure
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:near-promoter-peak-enrich"
plural "false"
caps "false"
noprefix "false"
\end_inset
shows that H3K4me2, H3K4me3, and H3K27me3 are all enriched near promoters,
relative to the rest of the genome, consistent with their conventionally
understood role in regulating gene transcription.
Interestingly, the radius within this enrichment occurs is not the same
for each histone mark.
H3K4me2 and H3K4me3 are enriched within a 1
\begin_inset space ~
\end_inset
kbp radius, while H3K27me3 is enriched within 2.5
\begin_inset space ~
\end_inset
kbp.
Notably, the determined promoter radius was consistent across all experimental
conditions, varying only between different histone marks.
This suggests that the conventional
\begin_inset Quotes eld
\end_inset
one size fits all
\begin_inset Quotes erd
\end_inset
approach of defining a single promoter region for each gene (or each
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
TSS
\end_layout
\end_inset
) and using that same promoter region for analyzing all types of genomic
data within an experiment may not be appropriate, and a better approach
may be to use a separate promoter radius for each kind of data, with each
radius being derived from the data itself.
Furthermore, the apparent asymmetry of upstream and downstream promoter
histone modification with respect to gene expression, seen in Figures
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:H3K4me2-neighborhood"
plural "false"
caps "false"
noprefix "false"
\end_inset
,
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:H3K4me3-neighborhood"
plural "false"
caps "false"
noprefix "false"
\end_inset
, and
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:H3K27me3-neighborhood"
plural "false"
caps "false"
noprefix "false"
\end_inset
, shows that even the concept of a promoter
\begin_inset Quotes eld
\end_inset
radius
\begin_inset Quotes erd
\end_inset
is likely an oversimplification.
At a minimum, nearby enrichment of peaks should be evaluated separately
for both upstream and downstream peaks, and an appropriate
\begin_inset Quotes eld
\end_inset
radius
\begin_inset Quotes erd
\end_inset
should be selected for each direction.
\end_layout
\begin_layout Standard
\begin_inset Flex TODO Note (inline)
status open
\begin_layout Plain Layout
Sarah: I would have to search the literature, but I believe this has been
observed before.
The position relative to the TSS likely has to do with recruitment of the
transcriptional machinery and the space required for that.
\end_layout
\end_inset
\end_layout
\begin_layout Standard
Figures
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:H3K4me2-neighborhood"
plural "false"
caps "false"
noprefix "false"
\end_inset
and
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:H3K4me3-neighborhood"
plural "false"
caps "false"
noprefix "false"
\end_inset
show that the determined promoter radius of 1
\begin_inset space ~
\end_inset
kbp is approximately consistent with the distance from the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
TSS
\end_layout
\end_inset
at which enrichment of H3K4 methylation correlates with increased expression,
showing that this radius, which was determined by a simple analysis of
measuring the distance from each
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
TSS
\end_layout
\end_inset
to the nearest peak, also has functional significance.
For H3K27me3, the correlation between histone modification near the promoter
and gene expression is more complex, involving non-peak variations such
as troughs in coverage at the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
TSS
\end_layout
\end_inset
and asymmetric coverage upstream and downstream, so it is difficult in
this case to evaluate whether the 2.5
\begin_inset space ~
\end_inset
kbp radius determined from TSS-to-peak distances is functionally significant.
However, the two patterns of coverage associated with elevated expression
levels both have interesting features within this radius.
\end_layout
\begin_layout Subsection
Day 14 convergence is consistent with naïve-to-memory differentiation
\end_layout
\begin_layout Standard
\begin_inset Flex TODO Note (inline)
status open
\begin_layout Plain Layout
Look up some more references for these histone marks being involved in memory
differentiation.
(Ask Sarah)
\end_layout
\end_inset
\end_layout
\begin_layout Standard
We observed that all 3 histone marks and the gene expression data all exhibit
evidence of convergence in abundance between naïve and memory cells by
day 14 after activation (Figure
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:PCoA-promoters"
plural "false"
caps "false"
noprefix "false"
\end_inset
, Table
\begin_inset CommandInset ref
LatexCommand ref
reference "tab:Number-signif-promoters"
plural "false"
caps "false"
noprefix "false"
\end_inset
).
The
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
MOFA
\end_layout
\end_inset
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
LF
\end_layout
\end_inset
scatter plots (Figure
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:mofa-lf-scatter"
plural "false"
caps "false"
noprefix "false"
\end_inset
) show that this pattern of convergence is captured in
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
LF
\end_layout
\end_inset
5.
Like all the
\begin_inset Flex Glossary Term (pl)
status open
\begin_layout Plain Layout
LF
\end_layout
\end_inset
in this plot, this factor explains a substantial portion of the variance
in all 4 data sets, indicating a coordinated pattern of variation shared
across all histone marks and gene expression.
This is consistent with the expectation that any naïve CD4
\begin_inset Formula $^{+}$
\end_inset
T-cells remaining at day 14 should have differentiated into memory cells
by that time, and should therefore have a genomic and epigenomic state
similar to memory cells.
This convergence is evidence that these histone marks all play an important
role in the naïve-to-memory differentiation process.
A histone mark that was not involved in naïve-to-memory differentiation
would not be expected to converge in this way after activation.
\end_layout
\begin_layout Standard
In H3K4me2, H3K4me3, and
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
RNA-seq
\end_layout
\end_inset
, this convergence appears to be in progress already by Day 5, shown by
the smaller distance between naïve and memory cells at day 5 along the
\begin_inset Formula $y$
\end_inset
-axes in Figures
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:PCoA-H3K4me2-prom"
plural "false"
caps "false"
noprefix "false"
\end_inset
,
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:PCoA-H3K4me3-prom"
plural "false"
caps "false"
noprefix "false"
\end_inset
, and
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:RNA-PCA-group"
plural "false"
caps "false"
noprefix "false"
\end_inset
.
This agrees with the model proposed by Sarah Lamere based on an prior analysis
of the same data, shown in Figure
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:Lamere2016-Fig8"
plural "false"
caps "false"
noprefix "false"
\end_inset
, which shows the pattern of H3K4 methylation and expression for naïve cells
and memory cells converging at day 5.
This model was developed without the benefit of the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
PCoA
\end_layout
\end_inset
plots in Figure
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:PCoA-promoters"
plural "false"
caps "false"
noprefix "false"
\end_inset
, which have been corrected for confounding factors by ComBat and
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
SVA
\end_layout
\end_inset
.
This shows that proper batch correction assists in extracting meaningful
patterns in the data while eliminating systematic sources of irrelevant
variation in the data, allowing simple automated procedures like
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
PCoA
\end_layout
\end_inset
to reveal interesting behaviors in the data that were previously only detectabl
e by a detailed manual analysis.
While the ideal comparison to demonstrate this convergence would be naïve
cells at day 14 to memory cells at day 0, this is not feasible in this
experimental system, since neither naïve nor memory cells are able to fully
return to their pre-activation state, as shown by the lack of overlap between
days 0 and 14 for either naïve or memory cells in Figure
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:PCoA-promoters"
plural "false"
caps "false"
noprefix "false"
\end_inset
.
\end_layout
\begin_layout Standard
\begin_inset Float figure
wide false
sideways false
status collapsed
\begin_layout Plain Layout
\align center
\begin_inset Graphics
filename graphics/CD4-csaw/LaMere2016_fig8.pdf
lyxscale 50
width 100col%
groupId colfullwidth
\end_inset
\end_layout
\begin_layout Plain Layout
\begin_inset Caption Standard
\begin_layout Plain Layout
\begin_inset Argument 1
status collapsed
\begin_layout Plain Layout
Lamere 2016 Figure 8 “Model for the role of H3K4 methylation during CD4
\begin_inset Formula $^{+}$
\end_inset
T-cell activation.
\begin_inset Quotes erd
\end_inset
\end_layout
\end_inset
\begin_inset CommandInset label
LatexCommand label
name "fig:Lamere2016-Fig8"
\end_inset
\series bold
Lamere 2016 Figure 8
\begin_inset CommandInset citation
LatexCommand cite
key "LaMere2016"
literal "false"
\end_inset
,
\begin_inset Quotes eld
\end_inset
Model for the role of H3K4 methylation during CD4
\begin_inset Formula $\mathbf{^{+}}$
\end_inset
T-cell activation.
\begin_inset Quotes erd
\end_inset
\series default
(Reproduced with permission.)
\end_layout
\end_inset
\end_layout
\end_inset
\end_layout
\begin_layout Subsection
The location of histone modifications within the promoter is important
\end_layout
\begin_layout Standard
When looking at patterns in the relative coverage of each histone mark near
the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
TSS
\end_layout
\end_inset
of each gene, several interesting patterns were apparent.
For H3K4me2 and H3K4me3, the pattern was straightforward: the consistent
pattern across all promoters was a single peak a few kbp wide, with the
main axis of variation being the position of this peak relative to the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
TSS
\end_layout
\end_inset
(Figures
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:H3K4me2-neighborhood"
plural "false"
caps "false"
noprefix "false"
\end_inset
&
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:H3K4me3-neighborhood"
plural "false"
caps "false"
noprefix "false"
\end_inset
).
There were no obvious
\begin_inset Quotes eld
\end_inset
preferred
\begin_inset Quotes erd
\end_inset
positions, but rather a continuous distribution of relative positions ranging
all across the promoter region.
The association with gene expression was also straightforward: peaks closer
to the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
TSS
\end_layout
\end_inset
were more strongly associated with elevated gene expression.
Coverage downstream of the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
TSS
\end_layout
\end_inset
appears to be more strongly associated with elevated expression than coverage
at the same distance upstream, indicating that the
\begin_inset Quotes eld
\end_inset
effective promoter region
\begin_inset Quotes erd
\end_inset
for H3K4me2 and H3K4me3 may be centered downstream of the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
TSS
\end_layout
\end_inset
.
\end_layout
\begin_layout Standard
The relative promoter coverage for H3K27me3 had a more complex pattern,
with two specific patterns of promoter coverage associated with elevated
expression: a sharp depletion of H3K27me3 around the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
TSS
\end_layout
\end_inset
relative to the surrounding area, and a depletion of H3K27me3 downstream
of the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
TSS
\end_layout
\end_inset
relative to upstream (Figure
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:H3K27me3-neighborhood"
plural "false"
caps "false"
noprefix "false"
\end_inset
).
A previous study found that H3K27me3 depletion within the gene body was
associated with elevated gene expression in 4 different cell types in mice
\begin_inset CommandInset citation
LatexCommand cite
key "Young2011"
literal "false"
\end_inset
.
This is consistent with the second pattern described here.
This study also reported that a spike in coverage at the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
TSS
\end_layout
\end_inset
was associated with
\emph on
lower
\emph default
expression, which is indirectly consistent with the first pattern described
here, in the sense that it associates lower H3K27me3 levels near the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
TSS
\end_layout
\end_inset
with higher expression.
\end_layout
\begin_layout Subsection
A reproducible workflow aids in analysis
\end_layout
\begin_layout Standard
The analyses described in this chapter were organized into a reproducible
workflow using the Snakemake workflow management system
\begin_inset CommandInset citation
LatexCommand cite
key "Koster2012"
literal "false"
\end_inset
.
As shown in Figure
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:rulegraph"
plural "false"
caps "false"
noprefix "false"
\end_inset
, the workflow includes many steps with complex dependencies between them.
For example, the step that counts the number of
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
ChIP-seq
\end_layout
\end_inset
reads in 500
\begin_inset space ~
\end_inset
bp windows in each promoter (the starting point for Figures
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:H3K4me2-neighborhood"
plural "false"
caps "false"
noprefix "false"
\end_inset
,
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:H3K4me3-neighborhood"
plural "false"
caps "false"
noprefix "false"
\end_inset
, and
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:H3K27me3-neighborhood"
plural "false"
caps "false"
noprefix "false"
\end_inset
), named
\begin_inset Flex Code
status open
\begin_layout Plain Layout
chipseq_count_tss_neighborhoods
\end_layout
\end_inset
, depends on the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
RNA-seq
\end_layout
\end_inset
abundance estimates in order to select the most-used
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
TSS
\end_layout
\end_inset
for each gene, the aligned
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
ChIP-seq
\end_layout
\end_inset
reads, the index for those reads, and the blacklist of regions to be excluded
from
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
ChIP-seq
\end_layout
\end_inset
analysis.
Each step declares its inputs and outputs, and Snakemake uses these to
determine the dependencies between steps.
Each step is marked as depending on all the steps whose outputs match its
inputs, generating the workflow graph in Figure
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:rulegraph"
plural "false"
caps "false"
noprefix "false"
\end_inset
, which Snakemake uses to determine order in which to execute each step
so that each step is executed only after all of the steps it depends on
have completed, thereby automating the entire workflow from start to finish.
\end_layout
\begin_layout Standard
\begin_inset ERT
status open
\begin_layout Plain Layout
\backslash
afterpage{
\end_layout
\begin_layout Plain Layout
\backslash
begin{landscape}
\end_layout
\end_inset
\end_layout
\begin_layout Standard
\begin_inset Float figure
wide false
sideways false
status collapsed
\begin_layout Plain Layout
\align center
\begin_inset Graphics
filename graphics/CD4-csaw/rulegraphs/rulegraph-all.pdf
lyxscale 50
width 100col%
height 95theight%
\end_inset
\end_layout
\begin_layout Plain Layout
\begin_inset Caption Standard
\begin_layout Plain Layout
\begin_inset Argument 1
status collapsed
\begin_layout Plain Layout
Dependency graph of steps in reproducible workflow.
\end_layout
\end_inset
\begin_inset CommandInset label
LatexCommand label
name "fig:rulegraph"
\end_inset
\series bold
Dependency graph of steps in reproducible workflow.
\series default
The analysis flows from left to right.
Arrows indicate which analysis steps depend on the output of other steps.
\end_layout
\end_inset
\end_layout
\end_inset
\end_layout
\begin_layout Standard
\begin_inset ERT
status open
\begin_layout Plain Layout
\backslash
end{landscape}
\end_layout
\begin_layout Plain Layout
}
\end_layout
\end_inset
\end_layout
\begin_layout Standard
In addition to simply making it easier to organize the steps in the analysis,
structuring the analysis as a workflow allowed for some analysis strategies
that would not have been practical otherwise.
For example, 5 different
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
RNA-seq
\end_layout
\end_inset
quantification methods were tested against two different reference transcriptom
e annotations for a total of 10 different quantifications of the same
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
RNA-seq
\end_layout
\end_inset
data.
These were then compared against each other in the exploratory data analysis
step, to determine that the results were not very sensitive to either the
choice of quantification method or the choice of annotation.
This was possible with a single script for the exploratory data analysis,
because Snakemake was able to automate running this script for every combinatio
n of method and reference.
In a similar manner, two different peak calling methods were tested against
each other, and in this case it was determined that
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
SICER
\end_layout
\end_inset
was unambiguously superior to
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
MACS
\end_layout
\end_inset
for all histone marks studied.
By enabling these types of comparisons, structuring the analysis as an
automated workflow allowed important analysis decisions to be made in a
data-driven way, by running every reasonable option through the downstream
steps, seeing the consequences of choosing each option, and deciding accordingl
y.
\end_layout
\begin_layout Standard
\begin_inset Note Note
status open
\begin_layout Subsection
Data quality issues limit conclusions
\end_layout
\begin_layout Plain Layout
\begin_inset Flex TODO Note (inline)
status open
\begin_layout Plain Layout
Is this needed?
\end_layout
\end_inset
\end_layout
\end_inset
\end_layout
\begin_layout Section
Future Directions
\end_layout
\begin_layout Standard
The analysis of
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
RNA-seq
\end_layout
\end_inset
and
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
ChIP-seq
\end_layout
\end_inset
in CD4
\begin_inset Formula $^{+}$
\end_inset
T-cells in Chapter 2 is in many ways a preliminary study that suggests
a multitude of new avenues of investigation.
Here we consider a selection of such avenues.
\end_layout
\begin_layout Subsection
Previous negative results
\end_layout
\begin_layout Standard
Two additional analyses were conducted beyond those reported in the results.
First, we searched for evidence that the presence or absence of a
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
CpGi
\end_layout
\end_inset
in the promoter was correlated with increases or decreases in gene expression
or any histone mark in any of the tested contrasts.
Second, we searched for evidence that the relative
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
ChIP-seq
\end_layout
\end_inset
coverage profiles prior to activations could predict the change in expression
of a gene after activation.
Neither analysis turned up any clear positive results.
\end_layout
\begin_layout Subsection
Improve on the idea of an effective promoter radius
\end_layout
\begin_layout Standard
This study introduced the concept of an
\begin_inset Quotes eld
\end_inset
effective promoter radius
\begin_inset Quotes erd
\end_inset
specific to each histone mark based on distance from the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
TSS
\end_layout
\end_inset
within which an excess of peaks was called for that mark.
This concept was then used to guide further analyses throughout the study.
However, while the effective promoter radius was useful in those analyses,
it is both limited in theory and shown in practice to be a possible oversimplif
ication.
First, the effective promoter radii used in this study were chosen based
on manual inspection of the TSS-to-peak distance distributions in Figure
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:near-promoter-peak-enrich"
plural "false"
caps "false"
noprefix "false"
\end_inset
, selecting round numbers of analyst convenience (Table
\begin_inset CommandInset ref
LatexCommand ref
reference "tab:effective-promoter-radius"
plural "false"
caps "false"
noprefix "false"
\end_inset
).
It would be better to define an algorithm that selects a more precise radius
based on the features of the graph.
One possible way to do this would be to randomly rearrange the called peaks
throughout the genome many (while preserving the distribution of peak widths)
and re-generate the same plot as in Figure
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:near-promoter-peak-enrich"
plural "false"
caps "false"
noprefix "false"
\end_inset
.
This would yield a better
\begin_inset Quotes eld
\end_inset
background
\begin_inset Quotes erd
\end_inset
distribution that demonstrates the degree of near-TSS enrichment that would
be expected by random chance.
The effective promoter radius could be defined as the point where the true
distribution diverges from the randomized background distribution.
\end_layout
\begin_layout Standard
Furthermore, the above definition of effective promoter radius has the significa
nt limitation of being based on the peak calling method.
It is thus very sensitive to the choice of peak caller and significance
threshold for calling peaks, as well as the degree of saturation in the
sequencing.
Calling peaks from
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
ChIP-seq
\end_layout
\end_inset
samples with insufficient coverage depth, with the wrong peak caller, or
with a different significance threshold could give a drastically different
number of called peaks, and hence a drastically different distribution
of peak-to-TSS distances.
To address this, it is desirable to develop a better method of determining
the effective promoter radius that relies only on the distribution of read
coverage around the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
TSS
\end_layout
\end_inset
, independent of the peak calling.
Furthermore, as demonstrated by the upstream-downstream asymmetries observed
in Figures
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:H3K4me2-neighborhood"
plural "false"
caps "false"
noprefix "false"
\end_inset
,
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:H3K4me3-neighborhood"
plural "false"
caps "false"
noprefix "false"
\end_inset
, and
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:H3K27me3-neighborhood"
plural "false"
caps "false"
noprefix "false"
\end_inset
, this definition should determine a different radius for the upstream and
downstream directions.
At this point, it may be better to rename this concept
\begin_inset Quotes eld
\end_inset
effective promoter extent
\begin_inset Quotes erd
\end_inset
and avoid the word
\begin_inset Quotes eld
\end_inset
radius
\begin_inset Quotes erd
\end_inset
, since a radius implies a symmetry about the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
TSS
\end_layout
\end_inset
that is not supported by the data.
\end_layout
\begin_layout Standard
Beyond improving the definition of effective promoter extent, functional
validation is necessary to show that this measure of near-TSS enrichment
has biological meaning.
Figures
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:H3K4me2-neighborhood"
plural "false"
caps "false"
noprefix "false"
\end_inset
and
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:H3K4me3-neighborhood"
plural "false"
caps "false"
noprefix "false"
\end_inset
already provide a very limited functional validation of the chosen promoter
extents for H3K4me2 and H3K4me3 by showing that spikes in coverage within
this region are most strongly correlated with elevated gene expression.
However, there are other ways to show functional relevance of the promoter
extent.
For example, correlations could be computed between read counts in peaks
nearby gene promoters and the expression level of those genes, and these
correlations could be plotted against the distance of the peak upstream
or downstream of the gene's
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
TSS
\end_layout
\end_inset
.
If the promoter extent truly defines a
\begin_inset Quotes eld
\end_inset
sphere of influence
\begin_inset Quotes erd
\end_inset
within which a histone mark is involved with the regulation of a gene,
then the correlations for peaks within this extent should be significantly
higher than those further upstream or downstream.
Peaks within these extents may also be more likely to show differential
modification than those outside genic regions of the genome.
\end_layout
\begin_layout Subsection
Design experiments to focus on post-activation convergence of naïve & memory
cells
\end_layout
\begin_layout Standard
In this study, a convergence between naïve and memory cells was observed
in both the pattern of gene expression and in epigenetic state of the 3
histone marks studied, consistent with the hypothesis that any naïve cells
remaining 14 days after activation have differentiated into memory cells,
and that both gene expression and these histone marks are involved in this
differentiation.
However, the current study was not designed with this specific hypothesis
in mind, and it therefore has some deficiencies with regard to testing
it.
The memory CD4
\begin_inset Formula $^{+}$
\end_inset
samples at day 14 do not resemble the memory samples at day 0, indicating
that in the specific model of activation used for this experiment, the
cells are not guaranteed to return to their original pre-activation state,
or perhaps this process takes substantially longer than 14 days.
This difference is expected, as the cell cultures in this experiment were
treated with IL2 from day 5 onward
\begin_inset CommandInset citation
LatexCommand cite
key "LaMere2016"
literal "false"
\end_inset
, so the signalling environments in which the cells are cultured are different
at day 0 and day 14.
This is a challenge for testing the convergence hypothesis because the
ideal comparison to prove that naïve cells are converging to a resting
memory state would be to compare the final naïve time point to the Day
0 memory samples, but this comparison is only meaningful if memory cells
generally return to the same
\begin_inset Quotes eld
\end_inset
resting
\begin_inset Quotes erd
\end_inset
state that they started at.
\end_layout
\begin_layout Standard
Because pre-culture and post-culture cells will probably never behave identicall
y even if they both nominally have a
\begin_inset Quotes eld
\end_inset
resting
\begin_inset Quotes erd
\end_inset
phenotype, a different experiment should be designed in which post-activation
naive cells are compared to memory cells that were cultured for the same
amount of time but never activated, in addition to post-activation memory
cells.
If the convergence hypothesis is correct, both post-activation cultures
should converge on the culture of never-activated memory cells.
\end_layout
\begin_layout Standard
In addition, if naïve-to-memory convergence is a general pattern, it should
also be detectable in other epigenetic marks, including other histone marks
and DNA methylation.
An experiment should be designed studying a large number of epigenetic
marks known or suspected to be involved in regulation of gene expression,
assaying all of these at the same pre- and post-activation time points.
Multi-dataset factor analysis methods like
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
MOFA
\end_layout
\end_inset
can then be used to identify coordinated patterns of regulation shared
across many epigenetic marks.
Of course, CD4
\begin_inset Formula $^{+}$
\end_inset
T-cells are not the only adaptive immune cells that exhibit memory formation.
A similar study could be designed for CD8
\begin_inset Formula $^{+}$
\end_inset
T-cells, B-cells, and even specific subsets of CD4
\begin_inset Formula $^{+}$
\end_inset
T-cells, such as Th1, Th2, Treg, and Th17 cells, to determine whether these
also show convergence.
\end_layout
\begin_layout Subsection
Follow up on hints of interesting patterns in promoter relative coverage
profiles
\end_layout
\begin_layout Standard
The analysis of promoter coverage landscapes in resting naive CD4
\begin_inset Formula $^{+}$
\end_inset
T-cells and their correlations with gene expression raises many interesting
questions.
The chosen analysis strategy used a clustering approach, but this approach
was subsequently shown to be a poor fit for the data.
In light of this, a better means of dimension reduction for promoter landscape
data is required.
In the case of H3K4me2 and H3K4me3, one option is to define the first 3
principal componets as orthogonal promoter
\begin_inset Quotes eld
\end_inset
state variables
\begin_inset Quotes erd
\end_inset
: upstream vs downstream coverage, TSS-centered peak vs trough, and proximal
upstream trough vs proximal downstream trough.
Gene expression could then be modeled as a function of these three variables,
or possibly as a function of the first
\begin_inset Formula $N$
\end_inset
principal components for
\begin_inset Formula $N$
\end_inset
larger than 3.
For H3K4me2 and H3K4me3, a better representation might be obtained by transform
ing the first 2 principal coordinates into a polar coordinate system
\begin_inset Formula $(r,\theta)$
\end_inset
with the origin at the center of the
\begin_inset Quotes eld
\end_inset
no peak
\begin_inset Quotes erd
\end_inset
cluster, where the radius
\begin_inset Formula $r$
\end_inset
represents the peak height above the background and the angle
\begin_inset Formula $\theta$
\end_inset
represents the peak's position upstream or downstream of the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
TSS
\end_layout
\end_inset
.
\end_layout
\begin_layout Standard
Another weakness in the current analysis is the normalization of the average
abundance of each promoter to an average of zero.
This allows the abundance value in each window to represent the relative
abundance of that window compared to all the other windows in the interrogated
area.
However, while using the remainder of the windows to set the
\begin_inset Quotes eld
\end_inset
background
\begin_inset Quotes erd
\end_inset
level against which each window is normalized is convenient, it is far
from optimal.
As shown in Table
\begin_inset CommandInset ref
LatexCommand ref
reference "tab:peak-calling-summary"
plural "false"
caps "false"
noprefix "false"
\end_inset
, many enriched regions are larger than the 5
\begin_inset space ~
\end_inset
kbp radius., which means there may not be any
\begin_inset Quotes eld
\end_inset
background
\begin_inset Quotes erd
\end_inset
regions within 5
\begin_inset space ~
\end_inset
kbp of the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
TSS
\end_layout
\end_inset
to normalize against.
For example, this normalization strategy fails to distinguish between a
trough in coverage at the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
TSS
\end_layout
\end_inset
and a pair of wide peaks upstream and downstream of the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
TSS
\end_layout
\end_inset
.
Both cases would present as lower coverage in the windows immediately adjacent
to the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
TSS
\end_layout
\end_inset
and higher coverage in windows further away, but the functional implications
of these two cases might be completely different.
To improve the normalization, the background estimation method used by
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
SICER
\end_layout
\end_inset
, which is specifically designed for finding broad regions of enrichment,
should be adapted to estimate the background sequencing depth in each window
from the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
ChIP-seq
\end_layout
\end_inset
input samples, and each window's read count should be normalized against
the background and reported as a
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
logFC
\end_layout
\end_inset
relative to that background.
\end_layout
\begin_layout Standard
Lastly, the analysis of promoter coverage landscapes presented in this work
only looked at promoter coverage of resting naive CD4
\begin_inset Formula $^{+}$
\end_inset
T-cells, with the goal of determining whether this initial promoter state
was predictive of post-activation changes in gene expression.
Changes in the promoter coverage landscape over time have not yet been
considered.
This represents a significant analysis challenge, by adding yet another
dimension (genomic coordinate) in to the data.
\end_layout
\begin_layout Subsection
Investigate causes of high correlation between mutually exclusive histone
marks
\end_layout
\begin_layout Standard
The high correlation between coverage depth observed between H3K4me2 and
H3K4me3 is both expected and unexpected.
Since both marks are associated with elevated gene transcription, a positive
correlation between them is not surprising.
However, these two marks represent different post-translational modifications
of the
\emph on
same
\emph default
lysine residue on the histone H3 polypeptide, which means that they cannot
both be present on the same H3 subunit.
Thus, the high correlation between them has several potential explanations.
One possible reason is cell population heterogeneity: perhaps some genomic
loci are frequently marked with H3K4me2 in some cells, while in other cells
the same loci are marked with H3K4me3.
Another possibility is allele-specific modifications: the loci are marked
in each diploid cell with H3K4me2 on one allele and H3K4me3 on the other
allele.
Lastly, since each histone octamer contains 2 H3 subunits, it is possible
that having one H3K4me2 mark and one H3K4me3 mark on a given histone octamer
represents a distinct epigenetic state with a different function than either
double H3K4me2 or double H3K4me3.
\end_layout
\begin_layout Standard
The hypothesis of allele-specific histone modification can easily be tested
with existing data by locating all heterozygous loci occurring within both
H3K4me3 and H3K4me2 peaks and checking for opposite allelic imbalance between
H3K4me3 and H3K4me2 read at each locus.
If the allele fractions in the reads from the two histone marks for each
locus are plotted against each other, there should be a negative correlation.
If no such negative correlation is found, then allele-specific histone
modification is unlikely to be the reason for the high correlation between
these histone marks.
\end_layout
\begin_layout Standard
To test the hypothesis that H3K4me2 and H3K4me3 marks are occurring on the
same histones.
A double
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
ChIP
\end_layout
\end_inset
experiment can be performed
\begin_inset CommandInset citation
LatexCommand cite
key "Jin2007"
literal "false"
\end_inset
.
In this assay, the input DNA goes through two sequential immunoprecipitations
with different antibodies: first the anti-H3K4me2 antibody, then the anti-H3K4m
e3 antibody.
Only bearing both histone marks, and the DNA associated with them, should
be isolated.
This can be followed by
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
HTS
\end_layout
\end_inset
to form a
\begin_inset Quotes eld
\end_inset
double
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
ChIP-seq
\end_layout
\end_inset
\begin_inset Quotes erd
\end_inset
assay that can be used to identify DNA regions bound by the isolated histones
\begin_inset CommandInset citation
LatexCommand cite
key "Jin2009"
literal "false"
\end_inset
.
If peaks called from this double
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
ChIP-seq
\end_layout
\end_inset
assay are highly correlated with both H3K4me2 and H3K4me3 peaks, then this
is strong evidence that the correlation between the two marks is actually
caused by physical co-location on the same histone.
\end_layout
\begin_layout Chapter
\begin_inset CommandInset label
LatexCommand label
name "chap:Improving-array-based-diagnostic"
\end_inset
Improving array-based diagnostics for transplant rejection by optimizing
data preprocessing
\end_layout
\begin_layout Standard
\size large
Ryan C.
Thompson, Sunil M.
Kurian, Thomas Whisnant, Padmaja Natarajan, Daniel R.
Salomon
\end_layout
\begin_layout Standard
\begin_inset ERT
status collapsed
\begin_layout Plain Layout
\backslash
glsresetall
\end_layout
\end_inset
\begin_inset Note Note
status collapsed
\begin_layout Plain Layout
Reintroduce all abbreviations
\end_layout
\end_inset
\end_layout
\begin_layout Section
Introduction
\end_layout
\begin_layout Standard
\begin_inset Flex TODO Note (inline)
status open
\begin_layout Plain Layout
Fill this out
\end_layout
\end_inset
\end_layout
\begin_layout Subsection
Arrays for diagnostics
\end_layout
\begin_layout Standard
Arrays are an attractive platform for diagnostics
\end_layout
\begin_layout Subsection
Proper pre-processing is essential for array data
\end_layout
\begin_layout Standard
Microarrays, bead arrays, and similar assays produce raw data in the form
of fluorescence intensity measurements, with each intensity measurement
proportional to the abundance of some fluorescently labelled target DNA
or RNA sequence that base pairs to a specific probe sequence.
However, the fluorescence measurements for each probe are also affected
my many technical confounding factors, such as the concentration of target
material, strength of off-target binding, the sensitivity of the imaging
sensor, and visual artifacts in the image.
Some array designs also use multiple probe sequences for each target.
Hence, extensive pre-processing of array data is necessary to normalize
out the effects of these technical factors and summarize the information
from multiple probes to arrive at a single usable estimate of abundance
or other relevant quantity, such as a ratio of two abundances, for each
target
\begin_inset CommandInset citation
LatexCommand cite
key "Gentleman2005"
literal "false"
\end_inset
.
\end_layout
\begin_layout Standard
The choice of pre-processing algorithms used in the analysis of an array
data set can have a large effect on the results of that analysis.
However, despite their importance, these steps are often neglected or rushed
in order to get to the more scientifically interesting analysis steps involving
the actual biology of the system under study.
Hence, it is often possible to achieve substantial gains in statistical
power, model goodness-of-fit, or other relevant performance measures, by
checking the assumptions made by each preprocessing step and choosing specific
normalization methods tailored to the specific goals of the current analysis.
\end_layout
\begin_layout Section
Approach
\end_layout
\begin_layout Subsection
Clinical diagnostic applications for microarrays require single-channel
normalization
\end_layout
\begin_layout Standard
As the cost of performing microarray assays falls, there is increasing interest
in using genomic assays for diagnostic purposes, such as distinguishing
\begin_inset ERT
status collapsed
\begin_layout Plain Layout
\backslash
glsdisp*{TX}{healthy transplants (TX)}
\end_layout
\end_inset
from transplants undergoing
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
AR
\end_layout
\end_inset
or
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
ADNR
\end_layout
\end_inset
.
However, the the standard normalization algorithm used for microarray data,
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
RMA
\end_layout
\end_inset
\begin_inset CommandInset citation
LatexCommand cite
key "Irizarry2003a"
literal "false"
\end_inset
, is not applicable in a clinical setting.
Two of the steps in
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
RMA
\end_layout
\end_inset
, quantile normalization and probe summarization by median polish, depend
on every array in the data set being normalized.
This means that adding or removing any arrays from a data set changes the
normalized values for all arrays, and data sets that have been normalized
separately cannot be compared to each other.
Hence, when using
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
RMA
\end_layout
\end_inset
, any arrays to be analyzed together must also be normalized together, and
the set of arrays included in the data set must be held constant throughout
an analysis.
\end_layout
\begin_layout Standard
These limitations present serious impediments to the use of arrays as a
diagnostic tool.
When training a classifier, the samples to be classified must not be involved
in any step of the training process, lest their inclusion bias the training
process.
Once a classifier is deployed in a clinical setting, the samples to be
classified will not even
\emph on
exist
\emph default
at the time of training, so including them would be impossible even if
it were statistically justifiable.
Therefore, any machine learning application for microarrays demands that
the normalized expression values computed for an array must depend only
on information contained within that array.
This would ensure that each array's normalization is independent of every
other array, and that arrays normalized separately can still be compared
to each other without bias.
Such a normalization is commonly referred to as
\begin_inset Quotes eld
\end_inset
single-channel normalization
\begin_inset Quotes erd
\end_inset
.
\end_layout
\begin_layout Standard
\begin_inset Flex Glossary Term (Capital)
status open
\begin_layout Plain Layout
fRMA
\end_layout
\end_inset
addresses these concerns by replacing the quantile normalization and median
polish with alternatives that do not introduce inter-array dependence,
allowing each array to be normalized independently of all others
\begin_inset CommandInset citation
LatexCommand cite
key "McCall2010"
literal "false"
\end_inset
.
Quantile normalization is performed against a pre-generated set of quantiles
learned from a collection of 850 publicly available arrays sampled from
a wide variety of tissues in
\begin_inset ERT
status collapsed
\begin_layout Plain Layout
\backslash
glsdisp*{GEO}{the Gene Expression Omnibus (GEO)}
\end_layout
\end_inset
.
Each array's probe intensity distribution is normalized against these pre-gener
ated quantiles.
The median polish step is replaced with a robust weighted average of probe
intensities, using inverse variance weights learned from the same public
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
GEO
\end_layout
\end_inset
data.
The result is a normalization that satisfies the requirements mentioned
above: each array is normalized independently of all others, and any two
normalized arrays can be compared directly to each other.
\end_layout
\begin_layout Standard
One important limitation of
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
fRMA
\end_layout
\end_inset
is that it requires a separate reference data set from which to learn the
parameters (reference quantiles and probe weights) that will be used to
normalize each array.
These parameters are specific to a given array platform, and pre-generated
parameters are only provided for the most common platforms, such as Affymetrix
hgu133plus2.
For a less common platform, such as hthgu133pluspm, is is necessary to
learn custom parameters from in-house data before
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
fRMA
\end_layout
\end_inset
can be used to normalize samples on that platform
\begin_inset CommandInset citation
LatexCommand cite
key "McCall2011"
literal "false"
\end_inset
.
\end_layout
\begin_layout Standard
One other option is the aptly-named
\begin_inset ERT
status collapsed
\begin_layout Plain Layout
\backslash
glsdisp*{SCAN}{Single Channel Array Normalization (SCAN)}
\end_layout
\end_inset
, which adapts a normalization method originally designed for tiling arrays
\begin_inset CommandInset citation
LatexCommand cite
key "Piccolo2012"
literal "false"
\end_inset
.
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
SCAN
\end_layout
\end_inset
is truly single-channel in that it does not require a set of normalization
parameters estimated from an external set of reference samples like
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
fRMA
\end_layout
\end_inset
does.
\end_layout
\begin_layout Subsection
Heteroskedasticity must be accounted for in methylation array data
\end_layout
\begin_layout Standard
DNA methylation arrays are a relatively new kind of assay that uses microarrays
to measure the degree of methylation on cytosines in specific regions arrayed
across the genome.
First, bisulfite treatment converts all unmethylated cytosines to uracil
(which are read as thymine during amplification and sequencing) while leaving
methylated cytosines unaffected.
Then, each target region is interrogated with two probes: one binds to
the original genomic sequence and interrogates the level of methylated
DNA, and the other binds to the same sequence with all cytosines replaced
by thymidines and interrogates the level of unmethylated DNA.
\end_layout
\begin_layout Standard
After normalization, these two probe intensities are summarized in one of
two ways, each with advantages and disadvantages.
β
\series bold
\series default
values, interpreted as fraction of DNA copies methylated, range from 0 to
1.
β
\series bold
\series default
values are conceptually easy to interpret, but the constrained range makes
them unsuitable for linear modeling, and their error distributions are
highly non-normal, which also frustrates linear modeling.
\begin_inset ERT
status collapsed
\begin_layout Plain Layout
\backslash
glsdisp*{M-value}{M-values}
\end_layout
\end_inset
, interpreted as the log ratios of methylated to unmethylated copies for
each probe region, are computed by mapping the beta values from
\begin_inset Formula $[0,1]$
\end_inset
onto
\begin_inset Formula $(-\infty,+\infty)$
\end_inset
using a sigmoid curve (Figure
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:Sigmoid-beta-m-mapping"
plural "false"
caps "false"
noprefix "false"
\end_inset
).
This transformation results in values with better statistical properties:
the unconstrained range is suitable for linear modeling, and the error
distributions are more normal.
Hence, most linear modeling and other statistical testing on methylation
arrays is performed using
\begin_inset Flex Glossary Term (pl)
status open
\begin_layout Plain Layout
M-value
\end_layout
\end_inset
.
\end_layout
\begin_layout Standard
\begin_inset Float figure
wide false
sideways false
status collapsed
\begin_layout Plain Layout
\align center
\begin_inset Graphics
filename graphics/methylvoom/sigmoid.pdf
lyxscale 50
width 60col%
groupId colwidth
\end_inset
\end_layout
\begin_layout Plain Layout
\begin_inset Caption Standard
\begin_layout Plain Layout
\begin_inset Argument 1
status collapsed
\begin_layout Plain Layout
Sigmoid shape of the mapping between β and M values.
\end_layout
\end_inset
\begin_inset CommandInset label
LatexCommand label
name "fig:Sigmoid-beta-m-mapping"
\end_inset
\series bold
Sigmoid shape of the mapping between β and M values.
\series default
This mapping is monotonic and non-linear, but it is approximately linear
in the neighborhood of
\begin_inset Formula $(\beta=0.5,M=0)$
\end_inset
.
\end_layout
\end_inset
\end_layout
\end_inset
\end_layout
\begin_layout Standard
However, the steep slope of the sigmoid transformation near 0 and 1 tends
to over-exaggerate small differences in β values near those extremes, which
in turn amplifies the error in those values, leading to a U-shaped trend
in the mean-variance curve: extreme values have higher variances than values
near the middle.
This mean-variance dependency must be accounted for when fitting the linear
model for differential methylation, or else the variance will be systematically
overestimated for probes with moderate
\begin_inset Flex Glossary Term (pl)
status open
\begin_layout Plain Layout
M-value
\end_layout
\end_inset
and underestimated for probes with extreme
\begin_inset Flex Glossary Term (pl)
status open
\begin_layout Plain Layout
M-value
\end_layout
\end_inset
.
This is particularly undesirable for methylation data because the intermediate
\begin_inset Flex Glossary Term (pl)
status open
\begin_layout Plain Layout
M-value
\end_layout
\end_inset
are the ones of most interest, since they are more likely to represent
areas of varying methylation, whereas extreme
\begin_inset Flex Glossary Term (pl)
status open
\begin_layout Plain Layout
M-value
\end_layout
\end_inset
typically represent complete methylation or complete lack of methylation.
\end_layout
\begin_layout Standard
\begin_inset Flex Glossary Term (Capital)
status open
\begin_layout Plain Layout
RNA-seq
\end_layout
\end_inset
read count data are also known to show heteroskedasticity, and the voom
method was introduced for modeling this heteroskedasticity by estimating
the mean-variance trend in the data and using this trend to assign precision
weights to each observation
\begin_inset CommandInset citation
LatexCommand cite
key "Law2014"
literal "false"
\end_inset
.
While methylation array data are not derived from counts and have a very
different mean-variance relationship from that of typical
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
RNA-seq
\end_layout
\end_inset
data, the voom method makes no specific assumptions on the shape of the
mean-variance relationship – it only assumes that the relationship can
be modeled as a smooth curve.
Hence, the method is sufficiently general to model the mean-variance relationsh
ip in methylation array data.
However, while the method does not require count data as input, the standard
implementation of voom assumes that the input is given in raw read counts,
and it must be adapted to run on methylation
\begin_inset Flex Glossary Term (pl)
status open
\begin_layout Plain Layout
M-value
\end_layout
\end_inset
.
\end_layout
\begin_layout Section
Methods
\end_layout
\begin_layout Subsection
Evaluation of classifier performance with different normalization methods
\end_layout
\begin_layout Standard
For testing different expression microarray normalizations, a data set of
157 hgu133plus2 arrays was used, consisting of blood samples from kidney
transplant patients whose grafts had been graded as
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
TX
\end_layout
\end_inset
,
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
AR
\end_layout
\end_inset
, or
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
ADNR
\end_layout
\end_inset
via biopsy and histology (46 TX, 69 AR, 42 ADNR)
\begin_inset CommandInset citation
LatexCommand cite
key "Kurian2014"
literal "true"
\end_inset
.
Additionally, an external validation set of 75 samples was gathered from
public
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
GEO
\end_layout
\end_inset
data (37 TX, 38 AR, no ADNR).
\end_layout
\begin_layout Standard
\begin_inset Flex TODO Note (inline)
status open
\begin_layout Plain Layout
Find appropriate GEO identifiers if possible.
Kurian 2014 says GSE15296, but this seems to be different data.
I also need to look up the GEO accession for the external validation set.
\end_layout
\end_inset
\end_layout
\begin_layout Standard
To evaluate the effect of each normalization on classifier performance,
the same classifier training and validation procedure was used after each
normalization method.
The
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
PAM
\end_layout
\end_inset
algorithm was used to train a nearest shrunken centroid classifier on the
training set and select the appropriate threshold for centroid shrinking
\begin_inset CommandInset citation
LatexCommand cite
key "Tibshirani2002"
literal "false"
\end_inset
.
Then the trained classifier was used to predict the class probabilities
of each validation sample.
From these class probabilities,
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
ROC
\end_layout
\end_inset
curves and
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
AUC
\end_layout
\end_inset
values were generated
\begin_inset CommandInset citation
LatexCommand cite
key "Turck2011"
literal "false"
\end_inset
.
Each normalization was tested on two different sets of training and validation
samples.
For internal validation, the 115
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
TX
\end_layout
\end_inset
and
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
AR
\end_layout
\end_inset
arrays in the internal set were split at random into two equal sized sets,
one for training and one for validation, each containing the same numbers
of
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
TX
\end_layout
\end_inset
and
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
AR
\end_layout
\end_inset
samples as the other set.
For external validation, the full set of 115
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
TX
\end_layout
\end_inset
and
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
AR
\end_layout
\end_inset
samples were used as a training set, and the 75 external
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
TX
\end_layout
\end_inset
and
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
AR
\end_layout
\end_inset
samples were used as the validation set.
Thus, 2
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
ROC
\end_layout
\end_inset
curves and
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
AUC
\end_layout
\end_inset
values were generated for each normalization method: one internal and one
external.
Because the external validation set contains no
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
ADNR
\end_layout
\end_inset
samples, only classification of
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
TX
\end_layout
\end_inset
and
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
AR
\end_layout
\end_inset
samples was considered.
The
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
ADNR
\end_layout
\end_inset
samples were included during normalization but excluded from all classifier
training and validation.
This ensures that the performance on internal and external validation sets
is directly comparable, since both are performing the same task: distinguishing
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
TX
\end_layout
\end_inset
from
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
AR
\end_layout
\end_inset
.
\end_layout
\begin_layout Standard
\begin_inset Flex TODO Note (inline)
status open
\begin_layout Plain Layout
Summarize the get.best.threshold algorithm for PAM threshold selection, or
just put the code online?
\end_layout
\end_inset
\end_layout
\begin_layout Standard
Six different normalization strategies were evaluated.
First, 2 well-known non-single-channel normalization methods were considered:
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
RMA
\end_layout
\end_inset
and dChip
\begin_inset CommandInset citation
LatexCommand cite
key "Li2001,Irizarry2003a"
literal "false"
\end_inset
.
Since
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
RMA
\end_layout
\end_inset
produces expression values on a
\begin_inset Formula $\log_{2}$
\end_inset
scale and dChip does not, the values from dChip were
\begin_inset Formula $\log_{2}$
\end_inset
transformed after normalization.
Next,
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
RMA
\end_layout
\end_inset
and dChip followed by
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
GRSN
\end_layout
\end_inset
were tested
\begin_inset CommandInset citation
LatexCommand cite
key "Pelz2008"
literal "false"
\end_inset
.
Post-processing with
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
GRSN
\end_layout
\end_inset
does not turn
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
RMA
\end_layout
\end_inset
or dChip into single-channel methods, but it may help mitigate batch effects
and is therefore useful as a benchmark.
Lastly, the two single-channel normalization methods,
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
fRMA
\end_layout
\end_inset
and
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
SCAN
\end_layout
\end_inset
, were tested
\begin_inset CommandInset citation
LatexCommand cite
key "McCall2010,Piccolo2012"
literal "false"
\end_inset
.
When evaluating internal validation performance, only the 157 internal
samples were normalized; when evaluating external validation performance,
all 157 internal samples and 75 external samples were normalized together.
\end_layout
\begin_layout Standard
For demonstrating the problem with separate normalization of training and
validation data, one additional normalization was performed: the internal
and external sets were each normalized separately using
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
RMA
\end_layout
\end_inset
, and the normalized data for each set were combined into a single set with
no further attempts at normalizing between the two sets.
This represents approximately how
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
RMA
\end_layout
\end_inset
would have to be used in a clinical setting, where the samples to be classified
are not available at the time the classifier is trained.
\end_layout
\begin_layout Subsection
Generating custom fRMA vectors for hthgu133pluspm array platform
\end_layout
\begin_layout Standard
In order to enable
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
fRMA
\end_layout
\end_inset
normalization for the hthgu133pluspm array platform, custom
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
fRMA
\end_layout
\end_inset
normalization vectors were trained using the
\begin_inset Flex Code
status open
\begin_layout Plain Layout
frmaTools
\end_layout
\end_inset
package
\begin_inset CommandInset citation
LatexCommand cite
key "McCall2011"
literal "false"
\end_inset
.
Separate vectors were created for two types of samples: kidney graft biopsy
samples and blood samples from graft recipients.
For training, 341 kidney biopsy samples from 2 data sets and 965 blood
samples from 5 data sets were used as the reference set.
Arrays were groups into batches based on unique combinations of sample
type (blood or biopsy), diagnosis (TX, AR, etc.), data set, and scan date.
Thus, each batch represents arrays of the same kind that were run together
on the same day.
For estimating the probe inverse variance weights, frmaTools requires equal-siz
ed batches, which means a batch size must be chosen, and then batches smaller
than that size must be ignored, while batches larger than the chosen size
must be downsampled.
This downsampling is performed randomly, so the sampling process is repeated
5 times and the resulting normalizations are compared to each other.
\end_layout
\begin_layout Standard
To evaluate the consistency of the generated normalization vectors, the
5
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
fRMA
\end_layout
\end_inset
vector sets generated from 5 random batch samplings were each used to normalize
the same 20 randomly selected samples from each tissue.
Then the normalized expression values for each probe on each array were
compared across all normalizations.
Each
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
fRMA
\end_layout
\end_inset
normalization was also compared against the normalized expression values
obtained by normalizing the same 20 samples with ordinary
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
RMA
\end_layout
\end_inset
.
\end_layout
\begin_layout Subsection
Modeling methylation array M-value heteroskedasticity with a modified voom
implementation
\end_layout
\begin_layout Standard
\begin_inset Flex TODO Note (inline)
status open
\begin_layout Plain Layout
Put code on Github and reference it.
\end_layout
\end_inset
\end_layout
\begin_layout Standard
To investigate the whether DNA methylation could be used to distinguish
between healthy and dysfunctional transplants, a data set of 78 Illumina
450k methylation arrays from human kidney graft biopsies was analyzed for
differential methylation between 4 transplant statuses:
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
TX
\end_layout
\end_inset
, transplants undergoing
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
AR
\end_layout
\end_inset
,
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
ADNR
\end_layout
\end_inset
, and
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
CAN
\end_layout
\end_inset
.
The data consisted of 33 TX, 9 AR, 8 ADNR, and 28 CAN samples.
The uneven group sizes are a result of taking the biopsy samples before
the eventual fate of the transplant was known.
Each sample was additionally annotated with a donor
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
ID
\end_layout
\end_inset
(anonymized), sex, age, ethnicity, creatinine level, and diabetes diagnosis
(all samples in this data set came from patients with either
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
T1D
\end_layout
\end_inset
or
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
T2D
\end_layout
\end_inset
).
\end_layout
\begin_layout Standard
The intensity data were first normalized using
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
SWAN
\end_layout
\end_inset
\begin_inset CommandInset citation
LatexCommand cite
key "Maksimovic2012"
literal "false"
\end_inset
, then converted to intensity ratios (beta values)
\begin_inset CommandInset citation
LatexCommand cite
key "Aryee2014"
literal "false"
\end_inset
.
Any probes binding to loci that overlapped annotated SNPs were dropped,
and the annotated sex of each sample was verified against the sex inferred
from the ratio of median probe intensities for the X and Y chromosomes.
Then, the ratios were transformed to
\begin_inset Flex Glossary Term (pl)
status open
\begin_layout Plain Layout
M-value
\end_layout
\end_inset
.
\end_layout
\begin_layout Standard
\begin_inset Float table
wide false
sideways false
status collapsed
\begin_layout Plain Layout
\align center
\begin_inset Tabular
|
\begin_inset Text
\begin_layout Plain Layout
Analysis
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
random effect
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
eBayes
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
SVA
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
weights
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
voom
\end_layout
\end_inset
|
|
\begin_inset Text
\begin_layout Plain Layout
A
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
Yes
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
Yes
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
No
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
No
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
No
\end_layout
\end_inset
|
|
\begin_inset Text
\begin_layout Plain Layout
B
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
Yes
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
Yes
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
Yes
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
Yes
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
No
\end_layout
\end_inset
|
|
\begin_inset Text
\begin_layout Plain Layout
C
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
Yes
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
Yes
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
Yes
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
Yes
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
Yes
\end_layout
\end_inset
|
\end_inset
\end_layout
\begin_layout Plain Layout
\begin_inset Caption Standard
\begin_layout Plain Layout
\begin_inset Argument 1
status collapsed
\begin_layout Plain Layout
Summary of analysis variants for methylation array data.
\end_layout
\end_inset
\begin_inset CommandInset label
LatexCommand label
name "tab:Summary-of-meth-analysis"
\end_inset
\series bold
Summary of analysis variants for methylation array data.
\series default
Each analysis included a different set of steps to adjust or account for
various systematic features of the data.
Random effect: The model included a random effect accounting for correlation
between samples from the same patient
\begin_inset CommandInset citation
LatexCommand cite
key "Smyth2005a"
literal "false"
\end_inset
; eBayes: Empirical bayes squeezing of per-probe variances toward the mean-varia
nce trend
\begin_inset CommandInset citation
LatexCommand cite
key "Ritchie2015"
literal "false"
\end_inset
; SVA: Surrogate variable analysis to account for unobserved confounders
\begin_inset CommandInset citation
LatexCommand cite
key "Leek2007"
literal "false"
\end_inset
; Weights: Estimate sample weights to account for differences in sample
quality
\begin_inset CommandInset citation
LatexCommand cite
key "Liu2015,Ritchie2006"
literal "false"
\end_inset
; voom: Use mean-variance trend to assign individual sample weights
\begin_inset CommandInset citation
LatexCommand cite
key "Law2014"
literal "false"
\end_inset
.
See the text for a more detailed explanation of each step.
\end_layout
\end_inset
\end_layout
\end_inset
\end_layout
\begin_layout Standard
From the
\begin_inset Flex Glossary Term (pl)
status open
\begin_layout Plain Layout
M-value
\end_layout
\end_inset
, a series of parallel analyses was performed, each adding additional steps
into the model fit to accommodate a feature of the data (see Table
\begin_inset CommandInset ref
LatexCommand ref
reference "tab:Summary-of-meth-analysis"
plural "false"
caps "false"
noprefix "false"
\end_inset
).
For analysis A, a
\begin_inset Quotes eld
\end_inset
basic
\begin_inset Quotes erd
\end_inset
linear modeling analysis was performed, compensating for known confounders
by including terms for the factor of interest (transplant status) as well
as the known biological confounders: sex, age, ethnicity, and diabetes.
Since some samples came from the same patients at different times, the
intra-patient correlation was modeled as a random effect, estimating a
shared correlation value across all probes
\begin_inset CommandInset citation
LatexCommand cite
key "Smyth2005a"
literal "false"
\end_inset
.
Then the linear model was fit, and the variance was modeled using empirical
Bayes squeezing toward the mean-variance trend
\begin_inset CommandInset citation
LatexCommand cite
key "Ritchie2015"
literal "false"
\end_inset
.
Finally, t-tests or F-tests were performed as appropriate for each test:
t-tests for single contrasts, and F-tests for multiple contrasts.
P-values were corrected for multiple testing using the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
BH
\end_layout
\end_inset
procedure for
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
FDR
\end_layout
\end_inset
control
\begin_inset CommandInset citation
LatexCommand cite
key "Benjamini1995"
literal "false"
\end_inset
.
\end_layout
\begin_layout Standard
For the analysis B,
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
SVA
\end_layout
\end_inset
was used to infer additional unobserved sources of heterogeneity in the
data
\begin_inset CommandInset citation
LatexCommand cite
key "Leek2007"
literal "false"
\end_inset
.
These surrogate variables were added to the design matrix before fitting
the linear model.
In addition, sample quality weights were estimated from the data and used
during linear modeling to down-weight the contribution of highly variable
arrays while increasing the weight to arrays with lower variability
\begin_inset CommandInset citation
LatexCommand cite
key "Ritchie2006"
literal "false"
\end_inset
.
The remainder of the analysis proceeded as in analysis A.
For analysis C, the voom method was adapted to run on methylation array
data and used to model and correct for the mean-variance trend using individual
observation weights
\begin_inset CommandInset citation
LatexCommand cite
key "Law2014"
literal "false"
\end_inset
, which were combined with the sample weights
\begin_inset CommandInset citation
LatexCommand cite
key "Liu2015,Ritchie2006"
literal "false"
\end_inset
.
Each time weights were used, they were estimated once before estimating
the random effect correlation value, and then the weights were re-estimated
taking the random effect into account.
The remainder of the analysis proceeded as in analysis B.
\end_layout
\begin_layout Section
Results
\end_layout
\begin_layout 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
To demonstrate the problem with non-single-channel normalization methods,
we considered the problem of training a classifier to distinguish
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
TX
\end_layout
\end_inset
from
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
AR
\end_layout
\end_inset
using the samples from the internal set as training data, evaluating performanc
e on the external set.
First, training and evaluation were performed after normalizing all array
samples together as a single set using
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
RMA
\end_layout
\end_inset
, and second, the internal samples were normalized separately from the external
samples and the training and evaluation were repeated.
For each sample in the validation set, the classifier probabilities from
both classifiers were plotted against each other (Fig.
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:Classifier-probabilities-RMA"
plural "false"
caps "false"
noprefix "false"
\end_inset
).
As expected, separate normalization biases the classifier probabilities,
resulting in several misclassifications.
In this case, the bias from separate normalization causes the classifier
to assign a lower probability of
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
AR
\end_layout
\end_inset
to every sample.
\end_layout
\begin_layout Standard
\begin_inset Float figure
wide false
sideways false
status collapsed
\begin_layout Plain Layout
\align center
\begin_inset Graphics
filename graphics/PAM/predplot.pdf
lyxscale 50
width 60col%
groupId colwidth
\end_inset
\end_layout
\begin_layout Plain Layout
\begin_inset Caption Standard
\begin_layout Plain Layout
\begin_inset Argument 1
status collapsed
\begin_layout Plain Layout
Classifier probabilities on validation samples when normalized with RMA
together vs.
separately.
\end_layout
\end_inset
\begin_inset CommandInset label
LatexCommand label
name "fig:Classifier-probabilities-RMA"
\end_inset
\series bold
Classifier probabilities on validation samples when normalized with RMA
together vs.
separately.
\series default
The PAM classifier algorithm was trained on the training set of arrays to
distinguish AR from TX and then used to assign class probabilities to the
validation set.
The process was performed after normalizing all samples together and after
normalizing the training and test sets separately, and the class probabilities
assigned to each sample in the validation set were plotted against each
other.
Each axis indicates the posterior probability of AR assigned to a sample
by the classifier in the specified analysis.
The color of each point indicates the true classification of that sample.
\end_layout
\end_inset
\end_layout
\end_inset
\end_layout
\begin_layout Subsection
fRMA and SCAN maintain classification performance while eliminating dependence
on normalization strategy
\end_layout
\begin_layout Standard
For internal validation, the 6 methods' AUC values ranged from 0.816 to 0.891,
as shown in Table
\begin_inset CommandInset ref
LatexCommand ref
reference "tab:AUC-PAM"
plural "false"
caps "false"
noprefix "false"
\end_inset
.
Among the non-single-channel normalizations, dChip outperformed
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
RMA
\end_layout
\end_inset
, while
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
GRSN
\end_layout
\end_inset
reduced the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
AUC
\end_layout
\end_inset
values for both dChip and
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
RMA
\end_layout
\end_inset
.
Both single-channel methods,
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
fRMA
\end_layout
\end_inset
and
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
SCAN
\end_layout
\end_inset
, slightly outperformed
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
RMA
\end_layout
\end_inset
, with
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
fRMA
\end_layout
\end_inset
ahead of
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
SCAN
\end_layout
\end_inset
.
However, the difference between
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
RMA
\end_layout
\end_inset
and
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
fRMA
\end_layout
\end_inset
is still quite small.
Figure
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:ROC-PAM-int"
plural "false"
caps "false"
noprefix "false"
\end_inset
shows that the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
ROC
\end_layout
\end_inset
curves for
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
RMA
\end_layout
\end_inset
, dChip, and
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
fRMA
\end_layout
\end_inset
look very similar and relatively smooth, while both
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
GRSN
\end_layout
\end_inset
curves and the curve for
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
SCAN
\end_layout
\end_inset
have a more jagged appearance.
\end_layout
\begin_layout Standard
\begin_inset Float figure
wide false
sideways false
status collapsed
\begin_layout Plain Layout
\align center
\begin_inset Float figure
placement tb
wide false
sideways false
status open
\begin_layout Plain Layout
\align center
\begin_inset Graphics
filename graphics/PAM/ROC-TXvsAR-internal.pdf
lyxscale 50
height 40theight%
groupId roc-pam
\end_inset
\end_layout
\begin_layout Plain Layout
\begin_inset Caption Standard
\begin_layout Plain Layout
\begin_inset CommandInset label
LatexCommand label
name "fig:ROC-PAM-int"
\end_inset
ROC curves for PAM on internal validation data
\end_layout
\end_inset
\end_layout
\end_inset
\end_layout
\begin_layout Plain Layout
\align center
\begin_inset Float figure
placement tb
wide false
sideways false
status open
\begin_layout Plain Layout
\align center
\begin_inset Graphics
filename graphics/PAM/ROC-TXvsAR-external.pdf
lyxscale 50
height 40theight%
groupId roc-pam
\end_inset
\end_layout
\begin_layout Plain Layout
\begin_inset Caption Standard
\begin_layout Plain Layout
\begin_inset CommandInset label
LatexCommand label
name "fig:ROC-PAM-ext"
\end_inset
ROC curves for PAM on external validation data
\end_layout
\end_inset
\end_layout
\end_inset
\end_layout
\begin_layout Plain Layout
\begin_inset Caption Standard
\begin_layout Plain Layout
\begin_inset Argument 1
status collapsed
\begin_layout Plain Layout
ROC curves for PAM using different normalization strategies.
\end_layout
\end_inset
\begin_inset CommandInset label
LatexCommand label
name "fig:ROC-PAM-main"
\end_inset
\series bold
ROC curves for PAM using different normalization strategies.
\series default
ROC curves were generated for PAM classification of AR vs TX after 6 different
normalization strategies applied to the same data sets.
Only fRMA and SCAN are single-channel normalizations.
The other normalizations are for comparison.
\end_layout
\end_inset
\end_layout
\end_inset
\end_layout
\begin_layout Standard
\begin_inset Float table
wide false
sideways false
status collapsed
\begin_layout Plain Layout
\align center
\begin_inset Tabular
|
\begin_inset Text
\begin_layout Plain Layout
\family roman
\series medium
\shape up
\size normal
\emph off
\bar no
\strikeout off
\xout off
\uuline off
\uwave off
\noun off
\color none
Normalization
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
Single-channel?
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
\family roman
\series medium
\shape up
\size normal
\emph off
\bar no
\strikeout off
\xout off
\uuline off
\uwave off
\noun off
\color none
Internal Val.
AUC
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
External Val.
AUC
\end_layout
\end_inset
|
|
\begin_inset Text
\begin_layout Plain Layout
\family roman
\series medium
\shape up
\size normal
\emph off
\bar no
\strikeout off
\xout off
\uuline off
\uwave off
\noun off
\color none
RMA
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
No
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
\family roman
\series medium
\shape up
\size normal
\emph off
\bar no
\strikeout off
\xout off
\uuline off
\uwave off
\noun off
\color none
0.852
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
\family roman
\series medium
\shape up
\size normal
\emph off
\bar no
\strikeout off
\xout off
\uuline off
\uwave off
\noun off
\color none
0.713
\end_layout
\end_inset
|
|
\begin_inset Text
\begin_layout Plain Layout
\family roman
\series medium
\shape up
\size normal
\emph off
\bar no
\strikeout off
\xout off
\uuline off
\uwave off
\noun off
\color none
dChip
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
No
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
\family roman
\series medium
\shape up
\size normal
\emph off
\bar no
\strikeout off
\xout off
\uuline off
\uwave off
\noun off
\color none
0.891
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
\family roman
\series medium
\shape up
\size normal
\emph off
\bar no
\strikeout off
\xout off
\uuline off
\uwave off
\noun off
\color none
0.657
\end_layout
\end_inset
|
|
\begin_inset Text
\begin_layout Plain Layout
\family roman
\series medium
\shape up
\size normal
\emph off
\bar no
\strikeout off
\xout off
\uuline off
\uwave off
\noun off
\color none
RMA + GRSN
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
No
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
\family roman
\series medium
\shape up
\size normal
\emph off
\bar no
\strikeout off
\xout off
\uuline off
\uwave off
\noun off
\color none
0.816
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
\family roman
\series medium
\shape up
\size normal
\emph off
\bar no
\strikeout off
\xout off
\uuline off
\uwave off
\noun off
\color none
0.750
\end_layout
\end_inset
|
|
\begin_inset Text
\begin_layout Plain Layout
\family roman
\series medium
\shape up
\size normal
\emph off
\bar no
\strikeout off
\xout off
\uuline off
\uwave off
\noun off
\color none
dChip + GRSN
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
No
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
\family roman
\series medium
\shape up
\size normal
\emph off
\bar no
\strikeout off
\xout off
\uuline off
\uwave off
\noun off
\color none
0.875
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
\family roman
\series medium
\shape up
\size normal
\emph off
\bar no
\strikeout off
\xout off
\uuline off
\uwave off
\noun off
\color none
0.642
\end_layout
\end_inset
|
|
\begin_inset Text
\begin_layout Plain Layout
\family roman
\series medium
\shape up
\size normal
\emph off
\bar no
\strikeout off
\xout off
\uuline off
\uwave off
\noun off
\color none
fRMA
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
Yes
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
\family roman
\series medium
\shape up
\size normal
\emph off
\bar no
\strikeout off
\xout off
\uuline off
\uwave off
\noun off
\color none
0.863
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
\family roman
\series medium
\shape up
\size normal
\emph off
\bar no
\strikeout off
\xout off
\uuline off
\uwave off
\noun off
\color none
0.718
\end_layout
\end_inset
|
|
\begin_inset Text
\begin_layout Plain Layout
\family roman
\series medium
\shape up
\size normal
\emph off
\bar no
\strikeout off
\xout off
\uuline off
\uwave off
\noun off
\color none
SCAN
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
Yes
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
\family roman
\series medium
\shape up
\size normal
\emph off
\bar no
\strikeout off
\xout off
\uuline off
\uwave off
\noun off
\color none
0.853
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
\family roman
\series medium
\shape up
\size normal
\emph off
\bar no
\strikeout off
\xout off
\uuline off
\uwave off
\noun off
\color none
0.689
\end_layout
\end_inset
|
\end_inset
\end_layout
\begin_layout Plain Layout
\begin_inset Caption Standard
\begin_layout Plain Layout
\begin_inset Argument 1
status collapsed
\begin_layout Plain Layout
ROC curve AUC values for internal and external validation with 6 different
normalization strategies.
\end_layout
\end_inset
\begin_inset CommandInset label
LatexCommand label
name "tab:AUC-PAM"
\end_inset
\series bold
ROC curve AUC values for internal and external validation with 6 different
normalization strategies.
\series default
These AUC values correspond to the ROC curves in Figure
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:ROC-PAM-main"
plural "false"
caps "false"
noprefix "false"
\end_inset
.
\end_layout
\end_inset
\end_layout
\end_inset
\end_layout
\begin_layout Standard
For external validation, as expected, all the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
AUC
\end_layout
\end_inset
values are lower than the internal validations, ranging from 0.642 to 0.750
(Table
\begin_inset CommandInset ref
LatexCommand ref
reference "tab:AUC-PAM"
plural "false"
caps "false"
noprefix "false"
\end_inset
).
With or without
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
GRSN
\end_layout
\end_inset
,
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
RMA
\end_layout
\end_inset
shows its dominance over dChip in this more challenging test.
Unlike in the internal validation,
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
GRSN
\end_layout
\end_inset
actually improves the classifier performance for
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
RMA
\end_layout
\end_inset
, although it does not for dChip.
Once again, both single-channel methods perform about on par with
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
RMA
\end_layout
\end_inset
, with
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
fRMA
\end_layout
\end_inset
performing slightly better and
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
SCAN
\end_layout
\end_inset
performing a bit worse.
Figure
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:ROC-PAM-ext"
plural "false"
caps "false"
noprefix "false"
\end_inset
shows the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
ROC
\end_layout
\end_inset
curves for the external validation test.
As expected, none of them are as clean-looking as the internal validation
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
ROC
\end_layout
\end_inset
curves.
The curves for
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
RMA
\end_layout
\end_inset
, RMA+GRSN, and
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
fRMA
\end_layout
\end_inset
all look similar, while the other curves look more divergent.
\end_layout
\begin_layout Subsection
fRMA with custom-generated vectors enables single-channel normalization
on hthgu133pluspm platform
\end_layout
\begin_layout Standard
In order to enable use of
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
fRMA
\end_layout
\end_inset
to normalize hthgu133pluspm, a custom set of
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
fRMA
\end_layout
\end_inset
vectors was created.
First, an appropriate batch size was chosen by looking at the number of
batches and number of samples included as a function of batch size (Figure
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:frmatools-batch-size"
plural "false"
caps "false"
noprefix "false"
\end_inset
).
For a given batch size, all batches with fewer samples that the chosen
size must be ignored during training, while larger batches must be randomly
downsampled to the chosen size.
Hence, the number of samples included for a given batch size equals the
batch size times the number of batches with at least that many samples.
From Figure
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:batch-size-samples"
plural "false"
caps "false"
noprefix "false"
\end_inset
, it is apparent that a batch size of 8 maximizes the number of samples
included in training.
Increasing the batch size beyond this causes too many smaller batches to
be excluded, reducing the total number of samples for both tissue types.
However, a batch size of 8 is not necessarily optimal.
The article introducing frmaTools concluded that it was highly advantageous
to use a smaller batch size in order to include more batches, even at the
cost of including fewer total samples in training
\begin_inset CommandInset citation
LatexCommand cite
key "McCall2011"
literal "false"
\end_inset
.
To strike an appropriate balance between more batches and more samples,
a batch size of 5 was chosen.
For both blood and biopsy samples, this increased the number of batches
included by 10, with only a modest reduction in the number of samples compared
to a batch size of 8.
With a batch size of 5, 26 batches of biopsy samples and 46 batches of
blood samples were available.
\end_layout
\begin_layout Standard
\begin_inset Float figure
wide false
sideways false
status collapsed
\begin_layout Plain Layout
\align center
\begin_inset Float figure
placement tb
wide false
sideways false
status collapsed
\begin_layout Plain Layout
\align center
\begin_inset Graphics
filename graphics/frma-pax-bx/batchsize_batches.pdf
lyxscale 50
height 35theight%
groupId frmatools-subfig
\end_inset
\end_layout
\begin_layout Plain Layout
\begin_inset Caption Standard
\begin_layout Plain Layout
\begin_inset CommandInset label
LatexCommand label
name "fig:batch-size-batches"
\end_inset
\series bold
Number of batches usable in fRMA probe weight learning as a function of
batch size.
\end_layout
\end_inset
\end_layout
\end_inset
\end_layout
\begin_layout Plain Layout
\align center
\begin_inset Float figure
placement tb
wide false
sideways false
status collapsed
\begin_layout Plain Layout
\align center
\begin_inset Graphics
filename graphics/frma-pax-bx/batchsize_samples.pdf
lyxscale 50
height 35theight%
groupId frmatools-subfig
\end_inset
\end_layout
\begin_layout Plain Layout
\begin_inset Caption Standard
\begin_layout Plain Layout
\begin_inset CommandInset label
LatexCommand label
name "fig:batch-size-samples"
\end_inset
\series bold
Number of samples usable in fRMA probe weight learning as a function of
batch size.
\end_layout
\end_inset
\end_layout
\end_inset
\end_layout
\begin_layout Plain Layout
\begin_inset Caption Standard
\begin_layout Plain Layout
\begin_inset Argument 1
status collapsed
\begin_layout Plain Layout
Effect of batch size selection on number of batches and number of samples
included in fRMA probe weight learning.
\end_layout
\end_inset
\begin_inset CommandInset label
LatexCommand label
name "fig:frmatools-batch-size"
\end_inset
\series bold
Effect of batch size selection on number of batches and number of samples
included in fRMA probe weight learning.
\series default
For batch sizes ranging from 3 to 15, the number of batches (a) and samples
(b) included in probe weight training were plotted for biopsy (BX) and
blood (PAX) samples.
The selected batch size, 5, is marked with a dotted vertical line.
\end_layout
\end_inset
\end_layout
\end_inset
\end_layout
\begin_layout Standard
Since
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
fRMA
\end_layout
\end_inset
training requires equal-size batches, larger batches are downsampled randomly.
This introduces a nondeterministic step in the generation of normalization
vectors.
To show that this randomness does not substantially change the outcome,
the random downsampling and subsequent vector learning was repeated 5 times,
with a different random seed each time.
20 samples were selected at random as a test set and normalized with each
of the 5 sets of
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
fRMA
\end_layout
\end_inset
normalization vectors as well as ordinary RMA, and the normalized expression
values were compared across normalizations.
Figure
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:m-bx-violin"
plural "false"
caps "false"
noprefix "false"
\end_inset
shows a summary of these comparisons for biopsy samples.
Comparing RMA to each of the 5
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
fRMA
\end_layout
\end_inset
normalizations, the distribution of log ratios is somewhat wide, indicating
that the normalizations disagree on the expression values of a fair number
of probe sets.
In contrast, comparisons of
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
fRMA
\end_layout
\end_inset
against
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
fRMA
\end_layout
\end_inset
, the vast majority of probe sets have very small log ratios, indicating
a very high agreement between the normalized values generated by the two
normalizations.
This shows that the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
fRMA
\end_layout
\end_inset
normalization's behavior is not very sensitive to the random downsampling
of larger batches during training.
\end_layout
\begin_layout Standard
\begin_inset Float figure
wide false
sideways false
status collapsed
\begin_layout Plain Layout
\align center
\begin_inset Graphics
filename graphics/frma-pax-bx/M-BX-violin.pdf
lyxscale 40
height 90theight%
groupId m-violin
\end_inset
\end_layout
\begin_layout Plain Layout
\begin_inset Caption Standard
\begin_layout Plain Layout
\begin_inset Argument 1
status collapsed
\begin_layout Plain Layout
Violin plot of log ratios between normalizations for 20 biopsy samples.
\end_layout
\end_inset
\begin_inset CommandInset label
LatexCommand label
name "fig:m-bx-violin"
\end_inset
\series bold
Violin plot of log ratios between normalizations for 20 biopsy samples.
\series default
Each of 20 randomly selected samples was normalized with RMA and with 5
different sets of fRMA vectors.
The distribution of log ratios between normalized expression values, aggregated
across all 20 arrays, was plotted for each pair of normalizations.
\end_layout
\end_inset
\end_layout
\end_inset
\end_layout
\begin_layout Standard
\begin_inset Float figure
wide false
sideways false
status collapsed
\begin_layout Plain Layout
\align center
\begin_inset Graphics
filename graphics/frma-pax-bx/M-PAX-violin.pdf
lyxscale 40
height 90theight%
groupId m-violin
\end_inset
\end_layout
\begin_layout Plain Layout
\begin_inset Caption Standard
\begin_layout Plain Layout
\begin_inset CommandInset label
LatexCommand label
name "fig:m-pax-violin"
\end_inset
\begin_inset Argument 1
status open
\begin_layout Plain Layout
Violin plot of log ratios between normalizations for 20 blood samples.
\end_layout
\end_inset
\series bold
Violin plot of log ratios between normalizations for 20 blood samples.
\series default
Each of 20 randomly selected samples was normalized with RMA and with 5
different sets of fRMA vectors.
The distribution of log ratios between normalized expression values, aggregated
across all 20 arrays, was plotted for each pair of normalizations.
\end_layout
\end_inset
\end_layout
\end_inset
\end_layout
\begin_layout Standard
Figure
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:ma-bx-rma-frma"
plural "false"
caps "false"
noprefix "false"
\end_inset
shows an MA plot of the RMA-normalized values against the fRMA-normalized
values for the same probe sets and arrays, corresponding to the first row
of Figure
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:m-bx-violin"
plural "false"
caps "false"
noprefix "false"
\end_inset
.
This MA plot shows that not only is there a wide distribution of
\begin_inset Flex Glossary Term (pl)
status open
\begin_layout Plain Layout
M-value
\end_layout
\end_inset
, but the trend of
\begin_inset Flex Glossary Term (pl)
status open
\begin_layout Plain Layout
M-value
\end_layout
\end_inset
is dependent on the average normalized intensity.
This is expected, since the overall trend represents the differences in
the quantile normalization step.
When running
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
RMA
\end_layout
\end_inset
, only the quantiles for these specific 20 arrays are used, while for
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
fRMA
\end_layout
\end_inset
the quantile distribution is taking from all arrays used in training.
Figure
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:ma-bx-frma-frma"
plural "false"
caps "false"
noprefix "false"
\end_inset
shows a similar MA plot comparing 2 different
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
fRMA
\end_layout
\end_inset
normalizations, corresponding to the 6th row of Figure
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:m-bx-violin"
plural "false"
caps "false"
noprefix "false"
\end_inset
.
The MA plot is very tightly centered around zero with no visible trend.
Figures
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:m-pax-violin"
plural "false"
caps "false"
noprefix "false"
\end_inset
,
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:MA-PAX-rma-frma"
plural "false"
caps "false"
noprefix "false"
\end_inset
, and
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:ma-bx-frma-frma"
plural "false"
caps "false"
noprefix "false"
\end_inset
show exactly the same information for the blood samples, once again comparing
the normalized expression values between normalizations for all probe sets
across 20 randomly selected test arrays.
Once again, there is a wider distribution of log ratios between RMA-normalized
values and fRMA-normalized, and a much tighter distribution when comparing
different
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
fRMA
\end_layout
\end_inset
normalizations to each other, indicating that the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
fRMA
\end_layout
\end_inset
training process is robust to random batch sub-sampling for the blood samples
as well.
\end_layout
\begin_layout Standard
\begin_inset Float figure
wide false
sideways false
status collapsed
\begin_layout Plain Layout
\align center
\begin_inset Float figure
wide false
sideways false
status open
\begin_layout Plain Layout
\align center
\begin_inset Graphics
filename graphics/frma-pax-bx/MA-BX-RMA.fRMA-RASTER.png
lyxscale 10
width 45col%
groupId ma-frma
\end_inset
\end_layout
\begin_layout Plain Layout
\begin_inset Caption Standard
\begin_layout Plain Layout
\begin_inset CommandInset label
LatexCommand label
name "fig:ma-bx-rma-frma"
\end_inset
RMA vs.
fRMA for biopsy samples.
\end_layout
\end_inset
\end_layout
\end_inset
\begin_inset space \hfill{}
\end_inset
\begin_inset Float figure
wide false
sideways false
status collapsed
\begin_layout Plain Layout
\align center
\begin_inset Graphics
filename graphics/frma-pax-bx/MA-BX-fRMA.fRMA-RASTER.png
lyxscale 10
width 45col%
groupId ma-frma
\end_inset
\end_layout
\begin_layout Plain Layout
\begin_inset Caption Standard
\begin_layout Plain Layout
\begin_inset CommandInset label
LatexCommand label
name "fig:ma-bx-frma-frma"
\end_inset
fRMA vs fRMA for biopsy samples.
\end_layout
\end_inset
\end_layout
\end_inset
\end_layout
\begin_layout Plain Layout
\align center
\begin_inset Float figure
wide false
sideways false
status collapsed
\begin_layout Plain Layout
\align center
\begin_inset Graphics
filename graphics/frma-pax-bx/MA-PAX-RMA.fRMA-RASTER.png
lyxscale 10
width 45col%
groupId ma-frma
\end_inset
\end_layout
\begin_layout Plain Layout
\begin_inset Caption Standard
\begin_layout Plain Layout
\begin_inset CommandInset label
LatexCommand label
name "fig:MA-PAX-rma-frma"
\end_inset
RMA vs.
fRMA for blood samples.
\end_layout
\end_inset
\end_layout
\end_inset
\begin_inset space \hfill{}
\end_inset
\begin_inset Float figure
wide false
sideways false
status collapsed
\begin_layout Plain Layout
\align center
\begin_inset Graphics
filename graphics/frma-pax-bx/MA-PAX-fRMA.fRMA-RASTER.png
lyxscale 10
width 45col%
groupId ma-frma
\end_inset
\end_layout
\begin_layout Plain Layout
\begin_inset Caption Standard
\begin_layout Plain Layout
\begin_inset CommandInset label
LatexCommand label
name "fig:MA-PAX-frma-frma"
\end_inset
fRMA vs fRMA for blood samples.
\end_layout
\end_inset
\end_layout
\end_inset
\end_layout
\begin_layout Plain Layout
\begin_inset Caption Standard
\begin_layout Plain Layout
\begin_inset Argument 1
status collapsed
\begin_layout Plain Layout
Representative MA plots comparing RMA and custom fRMA normalizations.
\end_layout
\end_inset
\begin_inset CommandInset label
LatexCommand label
name "fig:Representative-MA-plots"
\end_inset
\series bold
Representative MA plots comparing RMA and custom fRMA normalizations.
\series default
For each plot, 20 samples were normalized using 2 different normalizations,
and then averages (A) and log ratios (M) were plotted between the two different
normalizations for every probe.
For the
\begin_inset Quotes eld
\end_inset
fRMA vs fRMA
\begin_inset Quotes erd
\end_inset
plots (b & d), two different fRMA normalizations using vectors from two
independent batch samplings were compared.
Density of points is represented by blue shading, and individual outlier
points are plotted.
\end_layout
\end_inset
\end_layout
\end_inset
\end_layout
\begin_layout Subsection
SVA, voom, and array weights improve model fit for methylation array data
\end_layout
\begin_layout Standard
Figure
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:meanvar-basic"
plural "false"
caps "false"
noprefix "false"
\end_inset
shows the relationship between the mean
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
M-value
\end_layout
\end_inset
and the standard deviation calculated for each probe in the methylation
array data set.
A few features of the data are apparent.
First, the data are very strongly bimodal, with peaks in the density around
\begin_inset Flex Glossary Term (pl)
status open
\begin_layout Plain Layout
M-value
\end_layout
\end_inset
of +4 and -4.
These modes correspond to methylation sites that are nearly 100% methylated
and nearly 100% unmethylated, respectively.
The strong bimodality indicates that a majority of probes interrogate sites
that fall into one of these two categories.
The points in between these modes represent sites that are either partially
methylated in many samples, or are fully methylated in some samples and
fully unmethylated in other samples, or some combination.
The next visible feature of the data is the W-shaped variance trend.
The upticks in the variance trend on either side are expected, based on
the sigmoid transformation exaggerating small differences at extreme
\begin_inset Flex Glossary Term (pl)
status open
\begin_layout Plain Layout
M-value
\end_layout
\end_inset
(Figure
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:Sigmoid-beta-m-mapping"
plural "false"
caps "false"
noprefix "false"
\end_inset
).
However, the uptick in the center is interesting: it indicates that sites
that are not constitutively methylated or unmethylated have a higher variance.
This could be a genuine biological effect, or it could be spurious noise
that is only observable at sites with varying methylation.
\end_layout
\begin_layout Standard
\begin_inset ERT
status open
\begin_layout Plain Layout
\backslash
afterpage{
\end_layout
\begin_layout Plain Layout
\backslash
begin{landscape}
\end_layout
\end_inset
\end_layout
\begin_layout Standard
\begin_inset Float figure
wide false
sideways false
status open
\begin_layout Plain Layout
\begin_inset Flex TODO Note (inline)
status open
\begin_layout Plain Layout
Fix axis labels:
\begin_inset Quotes eld
\end_inset
log2 M-value
\begin_inset Quotes erd
\end_inset
is redundant because M-values are already log scale
\end_layout
\end_inset
\end_layout
\begin_layout Plain Layout
\begin_inset Float figure
wide false
sideways false
status collapsed
\begin_layout Plain Layout
\align center
\begin_inset Graphics
filename graphics/methylvoom/unadj.dupcor/meanvar-trends-PAGE1-CROP-RASTER.png
lyxscale 15
width 30col%
groupId voomaw-subfig
\end_inset
\end_layout
\begin_layout Plain Layout
\begin_inset Caption Standard
\begin_layout Plain Layout
\begin_inset CommandInset label
LatexCommand label
name "fig:meanvar-basic"
\end_inset
Mean-variance trend for analysis A.
\end_layout
\end_inset
\end_layout
\end_inset
\begin_inset space \hfill{}
\end_inset
\begin_inset Float figure
wide false
sideways false
status collapsed
\begin_layout Plain Layout
\align center
\begin_inset Graphics
filename graphics/methylvoom/unadj.dupcor.sva.aw/meanvar-trends-PAGE1-CROP-RASTER.png
lyxscale 15
width 30col%
groupId voomaw-subfig
\end_inset
\end_layout
\begin_layout Plain Layout
\begin_inset Caption Standard
\begin_layout Plain Layout
\begin_inset CommandInset label
LatexCommand label
name "fig:meanvar-sva-aw"
\end_inset
Mean-variance trend for analysis B.
\end_layout
\end_inset
\end_layout
\end_inset
\begin_inset space \hfill{}
\end_inset
\begin_inset Float figure
wide false
sideways false
status collapsed
\begin_layout Plain Layout
\align center
\begin_inset Graphics
filename graphics/methylvoom/unadj.dupcor.sva.voomaw/meanvar-trends-PAGE2-CROP-RASTER.png
lyxscale 15
width 30col%
groupId voomaw-subfig
\end_inset
\end_layout
\begin_layout Plain Layout
\begin_inset Caption Standard
\begin_layout Plain Layout
\begin_inset CommandInset label
LatexCommand label
name "fig:meanvar-sva-voomaw"
\end_inset
Mean-variance trend after voom modeling in analysis C.
\end_layout
\end_inset
\end_layout
\end_inset
\end_layout
\begin_layout Plain Layout
\begin_inset Caption Standard
\begin_layout Plain Layout
\begin_inset Argument 1
status collapsed
\begin_layout Plain Layout
Mean-variance trend modeling in methylation array data.
\end_layout
\end_inset
\begin_inset CommandInset label
LatexCommand label
name "fig:-Meanvar-trend-methyl"
\end_inset
\series bold
Mean-variance trend modeling in methylation array data.
\series default
The estimated
\begin_inset Formula $\log_{2}$
\end_inset
(standard deviation) for each probe is plotted against the probe's average
M-value across all samples as a black point, with some transparency to
make over-plotting more visible, since there are about 450,000 points.
Density of points is also indicated by the dark blue contour lines.
The prior variance trend estimated by eBayes is shown in light blue, while
the lowess trend of the points is shown in red.
\end_layout
\end_inset
\end_layout
\end_inset
\end_layout
\begin_layout Standard
\begin_inset ERT
status open
\begin_layout Plain Layout
\backslash
end{landscape}
\end_layout
\begin_layout Plain Layout
}
\end_layout
\end_inset
\end_layout
\begin_layout Standard
In Figure
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:meanvar-sva-aw"
plural "false"
caps "false"
noprefix "false"
\end_inset
, we see the mean-variance trend for the same methylation array data, this
time with surrogate variables and sample quality weights estimated from
the data and included in the model.
As expected, the overall average variance is smaller, since the surrogate
variables account for some of the variance.
In addition, the uptick in variance in the middle of the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
M-value
\end_layout
\end_inset
range has disappeared, turning the W shape into a wide U shape.
This indicates that the excess variance in the probes with intermediate
\begin_inset Flex Glossary Term (pl)
status open
\begin_layout Plain Layout
M-value
\end_layout
\end_inset
was explained by systematic variations not correlated with known covariates,
and these variations were modeled by the surrogate variables.
The result is a nearly flat variance trend for the entire intermediate
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
M-value
\end_layout
\end_inset
range from about -3 to +3.
Note that this corresponds closely to the range within which the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
M-value
\end_layout
\end_inset
transformation shown in Figure
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:Sigmoid-beta-m-mapping"
plural "false"
caps "false"
noprefix "false"
\end_inset
is nearly linear.
In contrast, the excess variance at the extremes (greater than +3 and less
than -3) was not
\begin_inset Quotes eld
\end_inset
absorbed
\begin_inset Quotes erd
\end_inset
by the surrogate variables and remains in the plot, indicating that this
variation has no systematic component: probes with extreme
\begin_inset Flex Glossary Term (pl)
status open
\begin_layout Plain Layout
M-value
\end_layout
\end_inset
are uniformly more variable across all samples, as expected.
\end_layout
\begin_layout Standard
Figure
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:meanvar-sva-voomaw"
plural "false"
caps "false"
noprefix "false"
\end_inset
shows the mean-variance trend after fitting the model with the observation
weights assigned by voom based on the mean-variance trend shown in Figure
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:meanvar-sva-aw"
plural "false"
caps "false"
noprefix "false"
\end_inset
.
As expected, the weights exactly counteract the trend in the data, resulting
in a nearly flat trend centered vertically at 1 (i.e.
0 on the log scale).
This shows that the observations with extreme
\begin_inset Flex Glossary Term (pl)
status open
\begin_layout Plain Layout
M-value
\end_layout
\end_inset
have been appropriately down-weighted to account for the fact that the
noise in those observations has been amplified by the non-linear
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
M-value
\end_layout
\end_inset
transformation.
In turn, this gives relatively more weight to observations in the middle
region, which are more likely to correspond to probes measuring interesting
biology (not constitutively methylated or unmethylated).
\end_layout
\begin_layout Standard
To determine whether any of the known experimental factors had an impact
on data quality, the sample quality weights estimated from the data were
tested for association with each of the experimental factors (Table
\begin_inset CommandInset ref
LatexCommand ref
reference "tab:weight-covariate-tests"
plural "false"
caps "false"
noprefix "false"
\end_inset
).
Diabetes diagnosis was found to have a potentially significant association
with the sample weights, with a t-test p-value of
\begin_inset Formula $1.06\times10^{-3}$
\end_inset
.
Figure
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:diabetes-sample-weights"
plural "false"
caps "false"
noprefix "false"
\end_inset
shows the distribution of sample weights grouped by diabetes diagnosis.
The samples from patients with
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
T2D
\end_layout
\end_inset
were assigned significantly lower weights than those from patients with
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
T1D
\end_layout
\end_inset
.
This indicates that the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
T2D
\end_layout
\end_inset
samples had an overall higher variance on average across all probes.
\end_layout
\begin_layout Standard
\begin_inset Float table
wide false
sideways false
status collapsed
\begin_layout Plain Layout
\align center
\begin_inset Tabular
|
\begin_inset Text
\begin_layout Plain Layout
Covariate
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
Test used
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
p-value
\end_layout
\end_inset
|
|
\begin_inset Text
\begin_layout Plain Layout
Transplant Status
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
F-test
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
0.404
\end_layout
\end_inset
|
|
\begin_inset Text
\begin_layout Plain Layout
Diabetes Diagnosis
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
\emph on
t
\emph default
-test
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
0.00106
\end_layout
\end_inset
|
|
\begin_inset Text
\begin_layout Plain Layout
Sex
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
\emph on
t
\emph default
-test
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
0.148
\end_layout
\end_inset
|
|
\begin_inset Text
\begin_layout Plain Layout
Age
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
linear regression
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
0.212
\end_layout
\end_inset
|
\end_inset
\end_layout
\begin_layout Plain Layout
\begin_inset Caption Standard
\begin_layout Plain Layout
\begin_inset Argument 1
status collapsed
\begin_layout Plain Layout
Association of sample weights with clinical covariates in methylation array
data.
\end_layout
\end_inset
\begin_inset CommandInset label
LatexCommand label
name "tab:weight-covariate-tests"
\end_inset
\series bold
Association of sample weights with clinical covariates in methylation array
data.
\series default
Computed sample quality log weights were tested for significant association
with each of the variables in the model (1st column).
An appropriate test was selected for each variable based on whether the
variable had 2 categories (
\emph on
t
\emph default
-test), had more than 2 categories (F-test), or was numeric (linear regression).
The test selected is shown in the 2nd column.
P-values for association with the log weights are shown in the 3rd column.
No multiple testing adjustment was performed for these p-values.
\end_layout
\end_inset
\end_layout
\end_inset
\end_layout
\begin_layout Standard
\begin_inset Float figure
wide false
sideways false
status collapsed
\begin_layout Plain Layout
\begin_inset Flex TODO Note (inline)
status open
\begin_layout Plain Layout
Redo the sample weight boxplot with notches, and remove fill colors
\end_layout
\end_inset
\end_layout
\begin_layout Plain Layout
\align center
\begin_inset Graphics
filename graphics/methylvoom/unadj.dupcor.sva.voomaw/sample-weights-PAGE3-CROP.pdf
lyxscale 50
width 60col%
groupId colwidth
\end_inset
\end_layout
\begin_layout Plain Layout
\begin_inset Caption Standard
\begin_layout Plain Layout
\begin_inset Argument 1
status collapsed
\begin_layout Plain Layout
Box-and-whiskers plot of sample quality weights grouped by diabetes diagnosis.
\end_layout
\end_inset
\begin_inset CommandInset label
LatexCommand label
name "fig:diabetes-sample-weights"
\end_inset
\series bold
Box-and-whiskers plot of sample quality weights grouped by diabetes diagnosis.
\series default
Samples were grouped based on diabetes diagnosis, and the distribution of
sample quality weights for each diagnosis was plotted as a box-and-whiskers
plot
\begin_inset CommandInset citation
LatexCommand cite
key "McGill1978"
literal "false"
\end_inset
.
\end_layout
\end_inset
\end_layout
\end_inset
\end_layout
\begin_layout Standard
Table
\begin_inset CommandInset ref
LatexCommand ref
reference "tab:methyl-num-signif"
plural "false"
caps "false"
noprefix "false"
\end_inset
shows the number of significantly differentially methylated probes reported
by each analysis for each comparison of interest at an
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
FDR
\end_layout
\end_inset
of 10%.
As expected, the more elaborate analyses, B and C, report more significant
probes than the more basic analysis A, consistent with the conclusions
above that the data contain hidden systematic variations that must be modeled.
Table
\begin_inset CommandInset ref
LatexCommand ref
reference "tab:methyl-est-nonnull"
plural "false"
caps "false"
noprefix "false"
\end_inset
shows the estimated number differentially methylated probes for each test
from each analysis.
This was computed by estimating the proportion of null hypotheses that
were true using the method of
\begin_inset CommandInset citation
LatexCommand cite
key "Phipson2013Thesis"
literal "false"
\end_inset
and subtracting that fraction from the total number of probes, yielding
an estimate of the number of null hypotheses that are false based on the
distribution of p-values across the entire dataset.
Note that this does not identify which null hypotheses should be rejected
(i.e.
which probes are significant); it only estimates the true number of such
probes.
Once again, analyses B and C result it much larger estimates for the number
of differentially methylated probes.
In this case, analysis C, the only analysis that includes voom, estimates
the largest number of differentially methylated probes for all 3 contrasts.
If the assumptions of all the methods employed hold, then this represents
a gain in statistical power over the simpler analysis A.
Figure
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:meth-p-value-histograms"
plural "false"
caps "false"
noprefix "false"
\end_inset
shows the p-value distributions for each test, from which the numbers in
Table
\begin_inset CommandInset ref
LatexCommand ref
reference "tab:methyl-est-nonnull"
plural "false"
caps "false"
noprefix "false"
\end_inset
were generated.
The distributions for analysis A all have a dip in density near zero, which
is a strong sign of a poor model fit.
The histograms for analyses B and C are more well-behaved, with a uniform
component stretching all the way from 0 to 1 representing the probes for
which the null hypotheses is true (no differential methylation), and a
zero-biased component representing the probes for which the null hypothesis
is false (differentially methylated).
These histograms do not indicate any major issues with the model fit.
\end_layout
\begin_layout Standard
\begin_inset Float table
wide false
sideways false
status collapsed
\begin_layout Plain Layout
\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
|
\begin_inset Text
\begin_layout Plain Layout
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
Analysis
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
\end_layout
\end_inset
|
|
\begin_inset Text
\begin_layout Plain Layout
Contrast
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
A
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
B
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
C
\end_layout
\end_inset
|
|
\begin_inset Text
\begin_layout Plain Layout
TX vs AR
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
0
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
25
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
22
\end_layout
\end_inset
|
|
\begin_inset Text
\begin_layout Plain Layout
TX vs ADNR
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
7
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
338
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
369
\end_layout
\end_inset
|
|
\begin_inset Text
\begin_layout Plain Layout
TX vs CAN
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
0
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
231
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
278
\end_layout
\end_inset
|
\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
|
\begin_inset Text
\begin_layout Plain Layout
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
Analysis
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
\end_layout
\end_inset
|
|
\begin_inset Text
\begin_layout Plain Layout
Contrast
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
A
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
B
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
C
\end_layout
\end_inset
|
|
\begin_inset Text
\begin_layout Plain Layout
TX vs AR
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
0
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
10,063
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
11,225
\end_layout
\end_inset
|
|
\begin_inset Text
\begin_layout Plain Layout
TX vs ADNR
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
27
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
12,674
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
13,086
\end_layout
\end_inset
|
|
\begin_inset Text
\begin_layout Plain Layout
TX vs CAN
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
966
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
20,039
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
20,955
\end_layout
\end_inset
|
\end_inset
\end_layout
\begin_layout Plain Layout
\begin_inset Caption Standard
\begin_layout Plain Layout
\begin_inset CommandInset label
LatexCommand label
name "tab:methyl-est-nonnull"
\end_inset
Estimated number of non-null tests, using the method of averaging local
FDR values
\begin_inset CommandInset citation
LatexCommand cite
key "Phipson2013Thesis"
literal "false"
\end_inset
.
\end_layout
\end_inset
\end_layout
\end_inset
\end_layout
\begin_layout Plain Layout
\begin_inset Caption Standard
\begin_layout Plain Layout
\begin_inset Argument 1
status collapsed
\begin_layout Plain Layout
Estimates of degree of differential methylation in for each contrast in
each analysis.
\end_layout
\end_inset
\series bold
Estimates of degree of differential methylation in for each contrast in
each analysis.
\series default
For each of the analyses in Table
\begin_inset CommandInset ref
LatexCommand ref
reference "tab:Summary-of-meth-analysis"
plural "false"
caps "false"
noprefix "false"
\end_inset
, these tables show the number of probes called significantly differentially
methylated at a threshold of 10% FDR for each comparison between TX and
the other 3 transplant statuses (a) and the estimated total number of probes
that are differentially methylated (b).
\end_layout
\end_inset
\end_layout
\end_inset
\end_layout
\begin_layout Standard
\begin_inset Float figure
wide false
sideways false
status collapsed
\begin_layout Plain Layout
\align center
\series bold
\begin_inset Float figure
wide false
sideways false
status collapsed
\begin_layout Plain Layout
\align center
\begin_inset Graphics
filename graphics/methylvoom/unadj.dupcor/pval-histograms-PAGE1.pdf
lyxscale 33
width 30col%
groupId meth-pval-hist
\end_inset
\end_layout
\begin_layout Plain Layout
\series bold
\begin_inset Caption Standard
\begin_layout Plain Layout
AR vs.
TX, Analysis A
\end_layout
\end_inset
\end_layout
\end_inset
\begin_inset space \hfill{}
\end_inset
\begin_inset Float figure
wide false
sideways false
status collapsed
\begin_layout Plain Layout
\align center
\begin_inset Graphics
filename graphics/methylvoom/unadj.dupcor/pval-histograms-PAGE2.pdf
lyxscale 33
width 30col%
groupId meth-pval-hist
\end_inset
\end_layout
\begin_layout Plain Layout
\series bold
\begin_inset Caption Standard
\begin_layout Plain Layout
ADNR vs.
TX, Analysis A
\end_layout
\end_inset
\end_layout
\end_inset
\begin_inset space \hfill{}
\end_inset
\begin_inset Float figure
wide false
sideways false
status collapsed
\begin_layout Plain Layout
\align center
\begin_inset Graphics
filename graphics/methylvoom/unadj.dupcor/pval-histograms-PAGE3.pdf
lyxscale 33
width 30col%
groupId meth-pval-hist
\end_inset
\end_layout
\begin_layout Plain Layout
\series bold
\begin_inset Caption Standard
\begin_layout Plain Layout
CAN vs.
TX, Analysis A
\end_layout
\end_inset
\end_layout
\end_inset
\end_layout
\begin_layout Plain Layout
\align center
\series bold
\begin_inset Float figure
wide false
sideways false
status collapsed
\begin_layout Plain Layout
\align center
\begin_inset Graphics
filename graphics/methylvoom/unadj.dupcor.sva.aw/pval-histograms-PAGE1.pdf
lyxscale 33
width 30col%
groupId meth-pval-hist
\end_inset
\end_layout
\begin_layout Plain Layout
\series bold
\begin_inset Caption Standard
\begin_layout Plain Layout
AR vs.
TX, Analysis B
\end_layout
\end_inset
\end_layout
\end_inset
\begin_inset space \hfill{}
\end_inset
\begin_inset Float figure
wide false
sideways false
status collapsed
\begin_layout Plain Layout
\align center
\begin_inset Graphics
filename graphics/methylvoom/unadj.dupcor.sva.aw/pval-histograms-PAGE2.pdf
lyxscale 33
width 30col%
groupId meth-pval-hist
\end_inset
\end_layout
\begin_layout Plain Layout
\series bold
\begin_inset Caption Standard
\begin_layout Plain Layout
ADNR vs.
TX, Analysis B
\end_layout
\end_inset
\end_layout
\end_inset
\begin_inset space \hfill{}
\end_inset
\begin_inset Float figure
wide false
sideways false
status collapsed
\begin_layout Plain Layout
\align center
\begin_inset Graphics
filename graphics/methylvoom/unadj.dupcor.sva.aw/pval-histograms-PAGE3.pdf
lyxscale 33
width 30col%
groupId meth-pval-hist
\end_inset
\end_layout
\begin_layout Plain Layout
\series bold
\begin_inset Caption Standard
\begin_layout Plain Layout
CAN vs.
TX, Analysis B
\end_layout
\end_inset
\end_layout
\end_inset
\end_layout
\begin_layout Plain Layout
\align center
\series bold
\begin_inset Float figure
wide false
sideways false
status collapsed
\begin_layout Plain Layout
\align center
\begin_inset Graphics
filename graphics/methylvoom/unadj.dupcor.sva.voomaw/pval-histograms-PAGE1.pdf
lyxscale 33
width 30col%
groupId meth-pval-hist
\end_inset
\end_layout
\begin_layout Plain Layout
\series bold
\begin_inset Caption Standard
\begin_layout Plain Layout
AR vs.
TX, Analysis C
\end_layout
\end_inset
\end_layout
\end_inset
\begin_inset space \hfill{}
\end_inset
\begin_inset Float figure
wide false
sideways false
status collapsed
\begin_layout Plain Layout
\align center
\begin_inset Graphics
filename graphics/methylvoom/unadj.dupcor.sva.voomaw/pval-histograms-PAGE2.pdf
lyxscale 33
width 30col%
groupId meth-pval-hist
\end_inset
\end_layout
\begin_layout Plain Layout
\series bold
\begin_inset Caption Standard
\begin_layout Plain Layout
ADNR vs.
TX, Analysis C
\end_layout
\end_inset
\end_layout
\end_inset
\begin_inset space \hfill{}
\end_inset
\begin_inset Float figure
wide false
sideways false
status collapsed
\begin_layout Plain Layout
\align center
\begin_inset Graphics
filename graphics/methylvoom/unadj.dupcor.sva.voomaw/pval-histograms-PAGE3.pdf
lyxscale 33
width 30col%
groupId meth-pval-hist
\end_inset
\end_layout
\begin_layout Plain Layout
\series bold
\begin_inset Caption Standard
\begin_layout Plain Layout
CAN vs.
TX, Analysis C
\end_layout
\end_inset
\end_layout
\end_inset
\end_layout
\begin_layout Plain Layout
\begin_inset Caption Standard
\begin_layout Plain Layout
\begin_inset Argument 1
status collapsed
\begin_layout Plain Layout
Probe p-value histograms for each contrast in each analysis.
\end_layout
\end_inset
\begin_inset CommandInset label
LatexCommand label
name "fig:meth-p-value-histograms"
\end_inset
\series bold
Probe p-value histograms for each contrast in each analysis.
\series default
For each differential methylation test of interest, the distribution of
p-values across all probes is plotted as a histogram.
The red solid line indicates the density that would be expected under the
null hypothesis for all probes (a
\begin_inset Formula $\mathrm{Uniform}(0,1)$
\end_inset
distribution), while the blue dotted line indicates the fraction of p-values
that actually follow the null hypothesis (
\begin_inset Formula $\hat{\pi}_{0}$
\end_inset
) estimated using the method of averaging local FDR values
\begin_inset CommandInset citation
LatexCommand cite
key "Phipson2013Thesis"
literal "false"
\end_inset
.
A blue line is only shown in each plot if the estimate of
\begin_inset Formula $\hat{\pi}_{0}$
\end_inset
for that p-value distribution is smaller than 1.
\end_layout
\end_inset
\end_layout
\end_inset
\end_layout
\begin_layout Standard
\begin_inset Flex TODO Note (inline)
status open
\begin_layout Plain Layout
If time allows, maybe generate the PCA plots before/after SVA effect subtraction
?
\end_layout
\end_inset
\end_layout
\begin_layout Section
Discussion
\end_layout
\begin_layout Subsection
fRMA achieves clinically applicable normalization without sacrificing classifica
tion performance
\end_layout
\begin_layout Standard
As shown in Figure
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:Classifier-probabilities-RMA"
plural "false"
caps "false"
noprefix "false"
\end_inset
, improper normalization, particularly separate normalization of training
and test samples, leads to unwanted biases in classification.
In a controlled experimental context, it is always possible to correct
this issue by normalizing all experimental samples together.
However, because it is not feasible to normalize all samples together in
a clinical context, a single-channel normalization is required.
\end_layout
\begin_layout Standard
The major concern in using a single-channel normalization is that non-single-cha
nnel methods can share information between arrays to improve the normalization,
and single-channel methods risk sacrificing the gains in normalization
accuracy that come from this information sharing.
In the case of
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
RMA
\end_layout
\end_inset
, this information sharing is accomplished through quantile normalization
and median polish steps.
The need for information sharing in quantile normalization can easily be
removed by learning a fixed set of quantiles from external data and normalizing
each array to these fixed quantiles, instead of the quantiles of the data
itself.
As long as the fixed quantiles are reasonable, the result will be similar
to standard
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
RMA
\end_layout
\end_inset
.
However, there is no analogous way to eliminate cross-array information
sharing in the median polish step, so
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
fRMA
\end_layout
\end_inset
replaces this with a weighted average of probes on each array, with the
weights learned from external data.
This step of
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
fRMA
\end_layout
\end_inset
has the greatest potential to diverge from RMA in undesirable ways.
\end_layout
\begin_layout Standard
However, when run on real data,
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
fRMA
\end_layout
\end_inset
performed at least as well as
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
RMA
\end_layout
\end_inset
in both the internal validation and external validation tests.
This shows that
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
fRMA
\end_layout
\end_inset
can be used to normalize individual clinical samples in a class prediction
context without sacrificing the classifier performance that would be obtained
by using the more well-established
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
RMA
\end_layout
\end_inset
for normalization.
The other single-channel normalization method considered,
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
SCAN
\end_layout
\end_inset
, showed some loss of
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
AUC
\end_layout
\end_inset
in the external validation test.
Based on these results,
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
fRMA
\end_layout
\end_inset
is the preferred normalization for clinical samples in a class prediction
context.
\end_layout
\begin_layout Subsection
Robust fRMA vectors can be generated for new array platforms
\end_layout
\begin_layout Standard
The published
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
fRMA
\end_layout
\end_inset
normalization vectors for the hgu133plus2 platform were generated from
a set of 850 samples chosen from a wide range of tissues, which the authors
determined was sufficient to generate a robust set of normalization vectors
that could be applied across all tissues
\begin_inset CommandInset citation
LatexCommand cite
key "McCall2010"
literal "false"
\end_inset
.
Since we only had hthgu133pluspm for 2 tissues of interest, our needs were
more modest.
Even using only 130 samples in 26 batches of 5 samples each for kidney
biopsies, we were able to train a robust set of
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
fRMA
\end_layout
\end_inset
normalization vectors that were not meaningfully affected by the random
selection of 5 samples from each batch.
As expected, the training process was just as robust for the blood samples
with 230 samples in 46 batches of 5 samples each.
Because these vectors were each generated using training samples from a
single tissue, they are not suitable for general use, unlike the vectors
provided with
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
fRMA
\end_layout
\end_inset
itself.
They are purpose-built for normalizing a specific type of sample on a specific
platform.
This is a mostly acceptable limitation in the context of developing a machine
learning classifier for diagnosing a disease from samples of a specific
tissue.
\end_layout
\begin_layout Subsection
Methylation array data can be successfully analyzed using existing techniques,
but machine learning poses additional challenges
\end_layout
\begin_layout Standard
Both analysis strategies B and C both yield a reasonable analysis, with
a mean-variance trend that matches the expected behavior for the non-linear
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
M-value
\end_layout
\end_inset
transformation (Figure
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:meanvar-sva-aw"
plural "false"
caps "false"
noprefix "false"
\end_inset
) and well-behaved p-value distributions (Figure
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:meth-p-value-histograms"
plural "false"
caps "false"
noprefix "false"
\end_inset
).
These two analyses also yield similar numbers of significant probes (Table
\begin_inset CommandInset ref
LatexCommand ref
reference "tab:methyl-num-signif"
plural "false"
caps "false"
noprefix "false"
\end_inset
) and similar estimates of the number of differentially methylated probes
(Table
\begin_inset CommandInset ref
LatexCommand ref
reference "tab:methyl-est-nonnull"
plural "false"
caps "false"
noprefix "false"
\end_inset
).
The main difference between these two analyses is the method used to account
for the mean-variance trend.
In analysis B, the trend is estimated and applied at the probe level: each
probe's estimated variance is squeezed toward the trend using an empirical
Bayes procedure (Figure
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:meanvar-sva-aw"
plural "false"
caps "false"
noprefix "false"
\end_inset
).
In analysis C, the trend is still estimated at the probe level, but instead
of estimating a single variance value shared across all observations for
a given probe, the voom method computes an initial estimate of the variance
for each observation individually based on where its model-fitted
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
M-value
\end_layout
\end_inset
falls on the trend line and then assigns inverse-variance weights to model
the difference in variance between observations.
An overall variance is still estimated for each probe using the same empirical
Bayes method, but now the residual trend is flat (Figure
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:meanvar-sva-voomaw"
plural "false"
caps "false"
noprefix "false"
\end_inset
), indicating that the mean-variance trend is adequately modeled by scaling
the estimated variance for each observation using the weights computed
by voom.
\end_layout
\begin_layout Standard
The difference between the standard empirical Bayes trended variance modeling
(analysis B) and voom (analysis C) is analogous to the difference between
a t-test with equal variance and a t-test with unequal variance, except
that the unequal group variances used in the latter test are estimated
based on the mean-variance trend from all the probes rather than the data
for the specific probe being tested, thus stabilizing the group variance
estimates by sharing information between probes.
Allowing voom to model the variance using observation weights in this manner
allows the linear model fit to concentrate statistical power where it will
do the most good.
For example, if a particular probe's
\begin_inset Flex Glossary Term (pl)
status open
\begin_layout Plain Layout
M-value
\end_layout
\end_inset
are always at the extreme of the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
M-value
\end_layout
\end_inset
range (e.g.
less than -4) for
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
ADNR
\end_layout
\end_inset
samples, but the
\begin_inset Flex Glossary Term (pl)
status open
\begin_layout Plain Layout
M-value
\end_layout
\end_inset
for that probe in
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
TX
\end_layout
\end_inset
and
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
CAN
\end_layout
\end_inset
samples are within the flat region of the mean-variance trend (between
\begin_inset Formula $-3$
\end_inset
and
\begin_inset Formula $+3$
\end_inset
), voom is able to down-weight the contribution of the high-variance
\begin_inset Flex Glossary Term (pl)
status open
\begin_layout Plain Layout
M-value
\end_layout
\end_inset
from the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
ADNR
\end_layout
\end_inset
samples in order to gain more statistical power while testing for differential
methylation between
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
TX
\end_layout
\end_inset
and
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
CAN
\end_layout
\end_inset
.
In contrast, modeling the mean-variance trend only at the probe level would
combine the high-variance
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
ADNR
\end_layout
\end_inset
samples and lower-variance samples from other conditions and estimate an
intermediate variance for this probe.
In practice, analysis B shows that this approach is adequate, but the voom
approach in analysis C performs at least as well on all model fit criteria
and yields a larger estimate for the number of differentially methylated
genes,
\emph on
and
\emph default
it matches up slightly better with the theoretical properties of the data.
\end_layout
\begin_layout Standard
The significant association of diabetes diagnosis with sample quality is
interesting.
The samples with
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
T2D
\end_layout
\end_inset
tended to have more variation, averaged across all probes, than those with
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
T1D
\end_layout
\end_inset
.
This is consistent with the consensus that
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
T2D
\end_layout
\end_inset
and the associated metabolic syndrome represent a broad dysregulation of
the body's endocrine signaling related to metabolism
\begin_inset CommandInset citation
LatexCommand cite
key "Volkmar2012,Hall2018,Yokoi2018"
literal "false"
\end_inset
.
This dysregulation could easily manifest as a greater degree of variation
in the DNA methylation patterns of affected tissues.
In contrast,
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
T1D
\end_layout
\end_inset
has a more specific cause and effect, so a less variable methylation signature
is expected.
\end_layout
\begin_layout Standard
This preliminary analysis suggests that some degree of differential methylation
exists between
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
TX
\end_layout
\end_inset
and each of the three types of transplant disfunction studied.
Hence, it may be feasible to train a classifier to diagnose transplant
disfunction from DNA methylation array data.
However, the major importance of both
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
SVA
\end_layout
\end_inset
and sample quality weighting for proper modeling of this data poses significant
challenges for any attempt at a machine learning on data of similar quality.
While these are easily used in a modeling context with full sample information,
neither of these methods is directly applicable in a machine learning context,
where the diagnosis is not known ahead of time.
If a machine learning approach for methylation-based diagnosis is to be
pursued, it will either require machine-learning-friendly methods to address
the same systematic trends in the data that
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
SVA
\end_layout
\end_inset
and sample quality weighting address, or it will require higher quality
data with substantially less systematic perturbation of the data.
\end_layout
\begin_layout Section
Future Directions
\end_layout
\begin_layout 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
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
fRMA
\end_layout
\end_inset
normalization vectors require equal-size batches, many samples must be
discarded from the training data.
This is undesirable for a few reasons.
First, more data is simply better, all other things being equal.
In this case,
\begin_inset Quotes eld
\end_inset
better
\begin_inset Quotes erd
\end_inset
means a more precise estimate of normalization parameters.
In addition, the samples to be discarded must be chosen arbitrarily, which
introduces an unnecessary element of randomness into the estimation process.
While the randomness can be made deterministic by setting a consistent
random seed, the need for equal size batches also introduces a need for
the analyst to decide on the appropriate trade-off between batch size and
the number of batches.
This introduces an unnecessary and undesirable
\begin_inset Quotes eld
\end_inset
researcher degree of freedom
\begin_inset Quotes erd
\end_inset
into the analysis, since the generated normalization vectors now depend
on the choice of batch size based on vague selection criteria and instinct,
which can unintentionally introduce bias if the researcher chooses a batch
size based on what seems to yield the most favorable downstream results
\begin_inset CommandInset citation
LatexCommand cite
key "Simmons2011"
literal "false"
\end_inset
.
\end_layout
\begin_layout Standard
Fortunately, the requirement for equal-size batches is not inherent to the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
fRMA
\end_layout
\end_inset
algorithm but rather a limitation of the implementation in the
\begin_inset Flex Code
status open
\begin_layout Plain Layout
frmaTools
\end_layout
\end_inset
package.
In personal communication, the package's author, Matthew McCall, has indicated
that with some work, it should be possible to improve the implementation
to work with batches of unequal sizes.
The current implementation ignores the batch size when calculating with-batch
and between-batch residual variances, since the batch size constant cancels
out later in the calculations as long as all batches are of equal size.
Hence, the calculations of these parameters would need to be modified to
remove this optimization and properly calculate the variances using the
full formula.
Once this modification is made, a new strategy would need to be developed
for assessing the stability of parameter estimates, since the random sub-sampli
ng step is eliminated, meaning that different sub-samplings can no longer
be compared as in Figures
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:frma-violin"
plural "false"
caps "false"
noprefix "false"
\end_inset
and
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:Representative-MA-plots"
plural "false"
caps "false"
noprefix "false"
\end_inset
.
Bootstrap resampling is likely a good candidate here: sample many training
sets of equal size from the existing training set with replacement, estimate
parameters from each resampled training set, and compare the estimated
parameters between bootstraps in order to quantify the variability in each
parameter's estimation.
\end_layout
\begin_layout Subsection
Developing methylation arrays as a diagnostic tool for kidney transplant
rejection
\end_layout
\begin_layout Standard
The current study has showed that DNA methylation, as assayed by Illumina
450k methylation arrays, has some potential for diagnosing transplant dysfuncti
ons, including rejection.
However, very few probes could be confidently identified as differentially
methylated between healthy and dysfunctional transplants.
One likely explanation for this is the predominant influence of unobserved
confounding factors.
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
SVA
\end_layout
\end_inset
can model and correct for such factors, but the correction can never be
perfect, so some degree of unwanted systematic variation will always remain
after
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
SVA
\end_layout
\end_inset
correction.
If the effect size of the confounding factors was similar to that of the
factor of interest (in this case, transplant status), this would be an
acceptable limitation, since removing most of the confounding factors'
effects would allow the main effect to stand out.
However, in this data set, the confounding factors have a much larger effect
size than transplant status, which means that the small degree of remaining
variation not removed by
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
SVA
\end_layout
\end_inset
can still swamp the effect of interest, making it difficult to detect.
This is, of course, a major issue when the end goal is to develop a classifier
to diagnose transplant rejection from methylation data, since batch-correction
methods like
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
SVA
\end_layout
\end_inset
that work in a linear modeling context cannot be applied in a machine learning
context.
\end_layout
\begin_layout Standard
Currently, the source of these unwanted systematic variations in the data
is unknown.
The best solution would be to determine the cause of the variation and
eliminate it, thereby eliminating the need to model and remove that variation.
However, if this proves impractical, another option is to use
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
SVA
\end_layout
\end_inset
to identify probes that are highly associated with the surrogate variables
that describe the unwanted variation in the data.
These probes could be discarded prior to classifier training, in order
to maximize the chance that the training algorithm will be able to identify
highly predictive probes from those remaining.
Lastly, it is possible that some of this unwanted variation is a result
of the array-based assay being used and would be eliminated by switching
to assaying DNA methylation using bisulphite sequencing.
However, this carries the risk that the sequencing assay will have its
own set of biases that must be corrected for in a different way.
\end_layout
\begin_layout Chapter
\begin_inset CommandInset label
LatexCommand label
name "chap:Globin-blocking-cyno"
\end_inset
Globin-blocking for more effective blood RNA-seq analysis in primate animal
model
\end_layout
\begin_layout Standard
\size large
Ryan C.
Thompson, Terri Gelbart, Steven R.
Head, Phillip Ordoukhanian, Courtney Mullen, Dongmei Han, Dora Berman,
Amelia Bartholomew, Norma Kenyon, Daniel R.
Salomon
\end_layout
\begin_layout Standard
\begin_inset ERT
status collapsed
\begin_layout Plain Layout
\backslash
glsresetall
\end_layout
\end_inset
\begin_inset Note Note
status collapsed
\begin_layout Plain Layout
Reintroduce all abbreviations
\end_layout
\end_inset
\end_layout
\begin_layout Standard
\begin_inset Flex TODO Note (inline)
status open
\begin_layout Plain Layout
Choose between above and the paper title: Optimizing yield of deep RNA sequencin
g for gene expression profiling by globin reduction of peripheral blood
samples from cynomolgus monkeys (
\emph on
Macaca fascicularis
\emph default
).
\end_layout
\end_inset
\end_layout
\begin_layout Section*
Abstract
\end_layout
\begin_layout Paragraph
Background
\end_layout
\begin_layout Standard
Primate blood contains high concentrations of globin
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
mRNA
\end_layout
\end_inset
.
Globin reduction is a standard technique used to improve the expression
results obtained by DNA microarrays on RNA from blood samples.
However, with
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
RNA-seq
\end_layout
\end_inset
quickly replacing microarrays for many applications, the impact of globin
reduction for
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
RNA-seq
\end_layout
\end_inset
is less well-studied.
Moreover, no off-the-shelf kits are available for globin reduction in nonhuman
primates.
\end_layout
\begin_layout Paragraph
Results
\end_layout
\begin_layout Standard
Here we report a protocol for
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
RNA-seq
\end_layout
\end_inset
in primate blood samples that uses complimentary
\begin_inset Flex Glossary Term (pl)
status open
\begin_layout Plain Layout
oligo
\end_layout
\end_inset
to block reverse transcription of the alpha and beta globin genes.
In test samples from cynomolgus monkeys (
\emph on
Macaca fascicularis
\emph default
), this
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
GB
\end_layout
\end_inset
protocol approximately doubles the yield of informative (non-globin) reads
by greatly reducing the fraction of globin reads, while also improving
the consistency in sequencing depth between samples.
The increased yield enables detection of about 2000 more genes, significantly
increases the correlation in measured gene expression levels between samples,
and increases the sensitivity of differential gene expression tests.
\end_layout
\begin_layout Paragraph
Conclusions
\end_layout
\begin_layout Standard
These results show that
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
GB
\end_layout
\end_inset
significantly improves the cost-effectiveness of
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
RNA-seq
\end_layout
\end_inset
in primate blood samples by doubling the yield of useful reads, allowing
detection of more genes, and improving the precision of gene expression
measurements.
Based on these results, a globin reducing or blocking protocol is recommended
for all
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
RNA-seq
\end_layout
\end_inset
studies of primate blood samples.
\end_layout
\begin_layout Standard
\begin_inset ERT
status collapsed
\begin_layout Plain Layout
\backslash
glsresetall
\end_layout
\end_inset
\end_layout
\begin_layout Section
Introduction
\end_layout
\begin_layout Standard
As part of a multi-lab PO1 grant to study
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
MSC
\end_layout
\end_inset
infusion as a treatment for graft rejection in cynomolgus monkeys (
\emph on
Macaca fascicularis
\emph default
), a large number of serial blood draws from cynomolgus monkeys were planned
in order to monitor the progress of graft healing and eventual rejection
after transplantation.
In order to streamline the process of performing
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
RNA-seq
\end_layout
\end_inset
on these blood samples, we developed a custom sequencing protocol.
In the developement of this protocol, we required a solution for the problem
of excess globin reads.
High fractions of globin
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
mRNA
\end_layout
\end_inset
are naturally present in mammalian peripheral blood samples (up to 70%
of total
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
mRNA
\end_layout
\end_inset
) and these are known to interfere with the results of array-based expression
profiling
\begin_inset CommandInset citation
LatexCommand cite
key "Winn2010"
literal "false"
\end_inset
.
Globin reduction is also necessary for
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
RNA-seq
\end_layout
\end_inset
of blood samples, though for unrelated reasons: without globin reduction,
many
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
RNA-seq
\end_layout
\end_inset
reads will be derived from the globin genes, leaving fewer for the remainder
of the genes in the transcriptome.
However, existing strategies for globin reduction require an additional
step during sample preparation to deplete the population of globin transcripts
from the sample prior to reverse transcription
\begin_inset CommandInset citation
LatexCommand cite
key "Mastrokolias2012,Choi2014,Shin2014"
literal "false"
\end_inset
.
Furthermore, off-the-shelf globin reduction kits are generally targeted
at human or mouse globin, not cynomolgus monkey, and sequence identity
between human and cyno globin genes cannot be automatically assumed.
Hence, we sought to incorporate a custom globin reduction method into our
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
RNA-seq
\end_layout
\end_inset
protocol purely by adding additional reagents to an existing step in the
sample preparation.
\end_layout
\begin_layout Section
Approach
\end_layout
\begin_layout Standard
\begin_inset Note Note
status collapsed
\begin_layout Plain Layout
Consider putting some of this in the Intro chapter
\end_layout
\begin_layout Itemize
Cynomolgus monkeys as a model organism
\end_layout
\begin_deeper
\begin_layout Itemize
Highly related to humans
\end_layout
\begin_layout Itemize
Small size and short life cycle - good research animal
\end_layout
\begin_layout Itemize
Genomics resources still in development
\end_layout
\end_deeper
\begin_layout Itemize
Inadequacy of existing blood RNA-seq protocols
\end_layout
\begin_deeper
\begin_layout Itemize
Existing protocols use a separate globin pulldown step, slowing down processing
\end_layout
\end_deeper
\end_inset
\end_layout
\begin_layout Standard
We evaluated globin reduction for
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
RNA-seq
\end_layout
\end_inset
by blocking reverse transcription of globin transcripts using custom blocking
\begin_inset Flex Glossary Term (pl)
status open
\begin_layout Plain Layout
oligo
\end_layout
\end_inset
.
We demonstrate that
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
GB
\end_layout
\end_inset
significantly improves the cost-effectiveness of
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
RNA-seq
\end_layout
\end_inset
in blood samples.
Thus, our protocol offers a significant advantage to any investigator planning
to use
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
RNA-seq
\end_layout
\end_inset
for gene expression profiling of nonhuman primate blood samples.
Our method can be generally applied to any species by designing complementary
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
oligo
\end_layout
\end_inset
blocking probes to the globin gene sequences of that species.
Indeed, any highly expressed but biologically uninformative transcripts
can also be blocked to further increase sequencing efficiency and value
\begin_inset CommandInset citation
LatexCommand cite
key "Arnaud2016"
literal "false"
\end_inset
.
\end_layout
\begin_layout Section
Methods
\end_layout
\begin_layout Subsection
Sample collection
\end_layout
\begin_layout Standard
All research reported here was done under IACUC-approved protocols at the
University of Miami and complied with all applicable federal and state
regulations and ethical principles for nonhuman primate research.
Blood draws occurred between 16
\begin_inset space ~
\end_inset
April
\begin_inset space ~
\end_inset
2012 and 18
\begin_inset space ~
\end_inset
June
\begin_inset space ~
\end_inset
2015.
The experimental system involved intrahepatic pancreatic islet transplantation
into Cynomolgus monkeys with induced diabetes mellitus with or without
concomitant infusion of mesenchymal stem cells.
Blood was collected at serial time points before and after transplantation
into PAXgene Blood RNA tubes (PreAnalytiX/Qiagen, Valencia, CA) at the
precise volume:volume ratio of 2.5
\begin_inset space ~
\end_inset
ml whole blood into 6.9
\begin_inset space ~
\end_inset
ml of PAX gene additive.
\end_layout
\begin_layout Subsection
Globin blocking oligonucleotide design
\end_layout
\begin_layout Standard
Four
\begin_inset Flex Glossary Term (pl)
status open
\begin_layout Plain Layout
oligo
\end_layout
\end_inset
were designed to hybridize to the
\begin_inset Formula $3^{\prime}$
\end_inset
end of the transcripts for the Cynomolgus alpha and beta globin, with two
hybridization sites for each gene.
All
\begin_inset Flex Glossary Term (pl)
status open
\begin_layout Plain Layout
oligo
\end_layout
\end_inset
were purchased from Sigma and were entirely composed of 2
\begin_inset Formula $^{\prime}$
\end_inset
O-Me bases with a C3 spacer positioned at the
\begin_inset Formula $3^{\prime}$
\end_inset
ends to prevent any polymerase mediated primer extension.
\end_layout
\begin_layout Description
HBA1/2
\begin_inset space ~
\end_inset
site
\begin_inset space ~
\end_inset
1:
\family typewriter
GCCCACUCAGACUUUAUUCAAAG-C3spacer
\end_layout
\begin_layout Description
HBA1/2
\begin_inset space ~
\end_inset
site
\begin_inset space ~
\end_inset
2:
\family typewriter
GGUGCAAGGAGGGGAGGAG-C3spacer
\end_layout
\begin_layout Description
HBB
\begin_inset space ~
\end_inset
site
\begin_inset space ~
\end_inset
1:
\family typewriter
AAUGAAAAUAAAUGUUUUUUAUUAG-C3spacer
\end_layout
\begin_layout Description
HBB
\begin_inset space ~
\end_inset
site
\begin_inset space ~
\end_inset
2:
\family typewriter
CUCAAGGCCCUUCAUAAUAUCCC-C3spacer
\end_layout
\begin_layout Subsection
RNA-seq library preparation
\end_layout
\begin_layout Standard
Sequencing libraries were prepared with 200
\begin_inset space ~
\end_inset
ng total RNA from each sample.
Polyadenylated
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
mRNA
\end_layout
\end_inset
was selected from 200
\begin_inset space ~
\end_inset
ng aliquots of cynomolgus blood-derived total RNA using Ambion Dynabeads
Oligo(dT)25 beads (Invitrogen) following the manufacturer’s recommended
protocol.
PolyA selected RNA was then combined with 8
\begin_inset space ~
\end_inset
pmol of HBA1/2
\begin_inset space ~
\end_inset
(site
\begin_inset space ~
\end_inset
1), 8
\begin_inset space ~
\end_inset
pmol of HBA1/2
\begin_inset space ~
\end_inset
(site
\begin_inset space ~
\end_inset
2), 12
\begin_inset space ~
\end_inset
pmol of HBB
\begin_inset space ~
\end_inset
(site
\begin_inset space ~
\end_inset
1) and 12
\begin_inset space ~
\end_inset
pmol of HBB
\begin_inset space ~
\end_inset
(site
\begin_inset space ~
\end_inset
2)
\begin_inset Flex Glossary Term (pl)
status open
\begin_layout Plain Layout
oligo
\end_layout
\end_inset
.
In addition, 20
\begin_inset space ~
\end_inset
pmol of RT primer containing a portion of the Illumina adapter sequence
(B-oligo-dTV: GAGTTCCTTGGCACCCGAGAATTCCATTTTTTTTTTTTTTTTTTTV) and 4
\begin_inset space ~
\end_inset
\emph on
μ
\emph default
L of 5X First Strand buffer (250
\begin_inset space ~
\end_inset
mM Tris-HCl pH
\begin_inset space ~
\end_inset
8.3, 375
\begin_inset space ~
\end_inset
mM KCl, 15
\begin_inset space ~
\end_inset
mM
\begin_inset Formula $\textrm{MgCl}_{2}$
\end_inset
) were added in a total volume of 15
\begin_inset space ~
\end_inset
µL.
The RNA was fragmented by heating this cocktail for 3 minutes at 95°C and
then placed on ice.
This was followed by the addition of 2
\begin_inset space ~
\end_inset
µL 0.1
\begin_inset space ~
\end_inset
M DTT, 1
\begin_inset space ~
\end_inset
µL RNaseOUT, 1
\begin_inset space ~
\end_inset
µL 10
\begin_inset space ~
\end_inset
mM dNTPs 10% biotin-16 aminoallyl-
\begin_inset Formula $2^{\prime}$
\end_inset
- dUTP and 10% biotin-16 aminoallyl-
\begin_inset Formula $2^{\prime}$
\end_inset
-dCTP (TriLink Biotech, San Diego, CA), 1
\begin_inset space ~
\end_inset
µL Superscript II (200
\begin_inset space ~
\end_inset
U/µL, Thermo-Fisher).
A second “unblocked” library was prepared in the same way for each sample
but replacing the blocking
\begin_inset Flex Glossary Term (pl)
status open
\begin_layout Plain Layout
oligo
\end_layout
\end_inset
with an equivalent volume of water.
The reaction was carried out at 25°C for 15 minutes and 42°C for 40 minutes,
followed by incubation at 75°C for 10 minutes to inactivate the reverse
transcriptase.
\end_layout
\begin_layout Standard
The cDNA/RNA hybrid molecules were purified using 1.8X Ampure XP beads (Agencourt
) following supplier’s recommended protocol.
The cDNA/RNA hybrid was eluted in 25
\begin_inset space ~
\end_inset
µL of 10
\begin_inset space ~
\end_inset
mM Tris-HCl pH
\begin_inset space ~
\end_inset
8.0, and then bound to 25
\begin_inset space ~
\end_inset
µL of M280 Magnetic Streptavidin beads washed per recommended protocol (Thermo-F
isher).
After 30 minutes of binding, beads were washed one time in 100
\begin_inset space ~
\end_inset
µL 0.1
\begin_inset space ~
\end_inset
N NaOH to denature and remove the bound RNA, followed by two 100
\begin_inset space ~
\end_inset
µL washes with 1X TE buffer.
\end_layout
\begin_layout Standard
Subsequent attachment of the
\begin_inset Formula $5^{\prime}$
\end_inset
Illumina A adapter was performed by on-bead random primer extension of
the following sequence (A-N8 primer:
\family typewriter
TTCAGAGTTCTACAGTCCGACGATCNNNNNNNN
\family default
).
Briefly, beads were resuspended in a 20
\begin_inset space ~
\end_inset
µL reaction containing 5
\begin_inset space ~
\end_inset
µM A-N8 primer, 40
\begin_inset space ~
\end_inset
mM Tris-HCl pH
\begin_inset space ~
\end_inset
7.5, 20
\begin_inset space ~
\end_inset
mM
\begin_inset Formula $\textrm{MgCl}_{2}$
\end_inset
, 50
\begin_inset space ~
\end_inset
mM NaCl, 0.325
\begin_inset space ~
\end_inset
U/µL Sequenase
\begin_inset space ~
\end_inset
2.0 (Affymetrix, Santa Clara, CA), 0.0025
\begin_inset space ~
\end_inset
U/µL inorganic pyrophosphatase (Affymetrix) and 300
\begin_inset space ~
\end_inset
µM each dNTP.
Reaction was incubated at 22°C for 30 minutes, then beads were washed 2
times with 1X TE buffer (200
\begin_inset space ~
\end_inset
µL).
\end_layout
\begin_layout Standard
The magnetic streptavidin beads were resuspended in 34
\begin_inset space ~
\end_inset
µL nuclease-free water and added directly to a
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
PCR
\end_layout
\end_inset
tube.
The two Illumina protocol-specified
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
PCR
\end_layout
\end_inset
primers were added at 0.53
\begin_inset space ~
\end_inset
µM (Illumina TruSeq Universal Primer 1 and Illumina TruSeq barcoded
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
PCR
\end_layout
\end_inset
primer 2), along with 40
\begin_inset space ~
\end_inset
µL 2X KAPA HiFi Hotstart ReadyMix (KAPA, Willmington MA) and thermocycled
as follows: starting with 98°C (2 min-hold); 15 cycles of 98°C, 20sec;
60°C, 30sec; 72°C, 30sec; and finished with a 72°C (2 min-hold).
\end_layout
\begin_layout Standard
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
PCR
\end_layout
\end_inset
products were purified with 1X Ampure Beads following manufacturer’s recommende
d protocol.
Libraries were then analyzed using the Agilent TapeStation and quantitation
of desired size range was performed by “smear analysis”.
Samples were pooled in equimolar batches of 16 samples.
Pooled libraries were size selected on 2% agarose gels (E-Gel EX Agarose
Gels; Thermo-Fisher).
Products were cut between 250 and 350
\begin_inset space ~
\end_inset
bp (corresponding to insert sizes of 130 to 230
\begin_inset space ~
\end_inset
bp).
Finished library pools were then sequenced on the Illumina NextSeq500 instrumen
t with 75
\begin_inset space ~
\end_inset
bp read lengths.
\end_layout
\begin_layout Subsection
Read alignment and counting
\end_layout
\begin_layout Standard
\begin_inset ERT
status collapsed
\begin_layout Plain Layout
\backslash
emergencystretch 3em
\end_layout
\end_inset
\begin_inset Note Note
status collapsed
\begin_layout Plain Layout
Need to relax the justification parameters just for this paragraph, or else
featureCounts can break out of the margin.
\end_layout
\end_inset
\end_layout
\begin_layout Standard
Reads were aligned to the cynomolgus genome using STAR
\begin_inset CommandInset citation
LatexCommand cite
key "Wilson2013,Dobin2012"
literal "false"
\end_inset
.
Counts of uniquely mapped reads were obtained for every gene in each sample
with the
\begin_inset Flex Code
status open
\begin_layout Plain Layout
featureCounts
\end_layout
\end_inset
function from the
\begin_inset Flex Code
status open
\begin_layout Plain Layout
Rsubread
\end_layout
\end_inset
package, using each of the three possibilities for the
\begin_inset Flex Code
status open
\begin_layout Plain Layout
strandSpecific
\end_layout
\end_inset
option: sense, antisense, and unstranded
\begin_inset CommandInset citation
LatexCommand cite
key "Liao2014"
literal "false"
\end_inset
.
A few artifacts in the cynomolgus genome annotation complicated read counting.
First, no ortholog is annotated for alpha globin in the cynomolgus genome,
presumably because the human genome has two alpha globin genes with nearly
identical sequences, making the orthology relationship ambiguous.
However, two loci in the cynomolgus genome are annotated as “hemoglobin
subunit alpha-like” (LOC102136192 and LOC102136846).
LOC102136192 is annotated as a pseudogene while LOC102136846 is annotated
as protein-coding.
Our globin reduction protocol was designed to include blocking of these
two genes.
Indeed, these two genes together have almost the same read counts in each
library as the properly-annotated HBB gene and much larger counts than
any other gene in the unblocked libraries, giving confidence that reads
derived from the real alpha globin are mapping to both genes.
Thus, reads from both of these loci were counted as alpha globin reads
in all further analyses.
The second artifact is a small, uncharacterized non-coding RNA gene (LOC1021365
91), which overlaps the HBA-like gene (LOC102136192) on the opposite strand.
If counting is not performed in stranded mode (or if a non-strand-specific
sequencing protocol is used), many reads mapping to the globin gene will
be discarded as ambiguous due to their overlap with this
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
ncRNA
\end_layout
\end_inset
gene, resulting in significant undercounting of globin reads.
Therefore, stranded sense counts were used for all further analysis in
the present study to insure that we accurately accounted for globin transcript
reduction.
However, we note that stranded reads are not necessary for
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
RNA-seq
\end_layout
\end_inset
using our protocol in standard practice.
\end_layout
\begin_layout Standard
\begin_inset ERT
status collapsed
\begin_layout Plain Layout
\backslash
emergencystretch 0em
\end_layout
\end_inset
\end_layout
\begin_layout Subsection
Normalization and exploratory data analysis
\end_layout
\begin_layout Standard
Libraries were normalized by computing scaling factors using the
\begin_inset Flex Code
status open
\begin_layout Plain Layout
edgeR
\end_layout
\end_inset
package's
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
TMM
\end_layout
\end_inset
method
\begin_inset CommandInset citation
LatexCommand cite
key "Robinson2010"
literal "false"
\end_inset
.
\begin_inset Flex Glossary Term (Capital)
status open
\begin_layout Plain Layout
logCPM
\end_layout
\end_inset
values were calculated using the
\begin_inset Flex Code
status open
\begin_layout Plain Layout
cpm
\end_layout
\end_inset
function in
\begin_inset Flex Code
status open
\begin_layout Plain Layout
edgeR
\end_layout
\end_inset
for individual samples and
\begin_inset Flex Code
status open
\begin_layout Plain Layout
aveLogCPM
\end_layout
\end_inset
function for averages across groups of samples, using those functions’
default prior count values to avoid taking the logarithm of 0.
Genes were considered “present” if their average normalized
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
logCPM
\end_layout
\end_inset
values across all libraries were at least
\begin_inset Formula $-1$
\end_inset
.
Normalizing for gene length was unnecessary because the sequencing protocol
is
\begin_inset Formula $3^{\prime}$
\end_inset
-biased and hence the expected read count for each gene is related to the
transcript’s copy number but not its length.
\end_layout
\begin_layout Standard
In order to assess the effect of
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
GB
\end_layout
\end_inset
on reproducibility, Pearson and Spearman correlation coefficients were
computed between the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
logCPM
\end_layout
\end_inset
values for every pair of libraries within the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
GB
\end_layout
\end_inset
non-GB groups, and
\begin_inset Flex Code
status open
\begin_layout Plain Layout
edgeR
\end_layout
\end_inset
's
\begin_inset Flex Code
status open
\begin_layout Plain Layout
estimateDisp
\end_layout
\end_inset
function was used to compute
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
NB
\end_layout
\end_inset
dispersions separately for the two groups
\begin_inset CommandInset citation
LatexCommand cite
key "Chen2014"
literal "false"
\end_inset
.
\end_layout
\begin_layout Subsection
Differential expression analysis
\end_layout
\begin_layout Standard
All tests for differential gene expression were performed using
\begin_inset Flex Code
status open
\begin_layout Plain Layout
edgeR
\end_layout
\end_inset
, by first fitting a
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
NB
\end_layout
\end_inset
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
GLM
\end_layout
\end_inset
to the counts and normalization factors and then performing a quasi-likelihood
F-test with robust estimation of outlier gene dispersions
\begin_inset CommandInset citation
LatexCommand cite
key "Lund2012,Phipson2016"
literal "false"
\end_inset
.
To investigate the effects of
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
GB
\end_layout
\end_inset
on each gene, an additive model was fit to the full data with coefficients
for
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
GB
\end_layout
\end_inset
and Sample
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
ID
\end_layout
\end_inset
.
To test the effect of
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
GB
\end_layout
\end_inset
on detection of differentially expressed genes, the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
GB
\end_layout
\end_inset
samples and non-GB samples were each analyzed independently as follows:
for each animal with both a pre-transplant and a post-transplant time point
in the data set, the pre-transplant sample and the earliest post-transplant
sample were selected, and all others were excluded, yielding a pre-/post-transp
lant pair of samples for each animal (
\begin_inset Formula $N=7$
\end_inset
animals with paired samples).
These samples were analyzed for pre-transplant vs.
post-transplant differential gene expression while controlling for inter-animal
variation using an additive model with coefficients for transplant and
animal
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
ID
\end_layout
\end_inset
.
In all analyses, p-values were adjusted using the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
BH
\end_layout
\end_inset
procedure for
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
FDR
\end_layout
\end_inset
control
\begin_inset CommandInset citation
LatexCommand cite
key "Benjamini1995"
literal "false"
\end_inset
.
\end_layout
\begin_layout Standard
\begin_inset Note Note
status open
\begin_layout Itemize
New blood RNA-seq protocol to block reverse transcription of globin genes
\end_layout
\begin_layout Itemize
Blood RNA-seq time course after transplants with/without MSC infusion
\end_layout
\end_inset
\end_layout
\begin_layout Section
Results
\end_layout
\begin_layout Subsection
Globin blocking yields a larger and more consistent fraction of useful reads
\end_layout
\begin_layout Standard
The objective of the present study was to validate a new protocol for deep
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
RNA-seq
\end_layout
\end_inset
of whole blood drawn into PaxGene tubes from cynomolgus monkeys undergoing
islet transplantation, with particular focus on minimizing the loss of
useful sequencing space to uninformative globin reads.
The details of the analysis with respect to transplant outcomes and the
impact of mesenchymal stem cell treatment will be reported in a separate
manuscript (in preparation).
To focus on the efficacy of our
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
GB
\end_layout
\end_inset
protocol, 37 blood samples, 16 from pre-transplant and 21 from post-transplant
time points, were each prepped once with and once without
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
GB
\end_layout
\end_inset
\begin_inset Flex Glossary Term (pl)
status open
\begin_layout Plain Layout
oligo
\end_layout
\end_inset
, and were then sequenced on an Illumina NextSeq500 instrument.
The number of reads aligning to each gene in the cynomolgus genome was
counted.
Table
\begin_inset CommandInset ref
LatexCommand ref
reference "tab:Fractions-of-reads"
plural "false"
caps "false"
noprefix "false"
\end_inset
summarizes the distribution of read fractions among the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
GB
\end_layout
\end_inset
and non-GB libraries.
In the libraries with no
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
GB
\end_layout
\end_inset
, globin reads made up an average of 44.6% of total input reads, while reads
assigned to all other genes made up an average of 26.3%.
The remaining reads either aligned to intergenic regions (that include
long non-coding RNAs) or did not align with any annotated transcripts in
the current build of the cynomolgus genome.
In the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
GB
\end_layout
\end_inset
libraries, globin reads made up only 3.48% and reads assigned to all other
genes increased to 50.4%.
Thus,
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
GB
\end_layout
\end_inset
resulted in a 92.2% reduction in globin reads and a 91.6% increase in yield
of useful non-globin reads.
\end_layout
\begin_layout Standard
\begin_inset ERT
status open
\begin_layout Plain Layout
\backslash
afterpage{
\end_layout
\begin_layout Plain Layout
\backslash
begin{landscape}
\end_layout
\end_inset
\end_layout
\begin_layout Standard
\begin_inset Float table
placement p
wide false
sideways false
status collapsed
\begin_layout Plain Layout
\align center
\begin_inset Tabular
|
\begin_inset Text
\begin_layout Plain Layout
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
\family roman
\series medium
\shape up
\size normal
\emph off
\bar no
\strikeout off
\xout off
\uuline off
\uwave off
\noun off
\color none
Percent of Total Reads
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
\family roman
\series medium
\shape up
\size normal
\emph off
\bar no
\strikeout off
\xout off
\uuline off
\uwave off
\noun off
\color none
Percent of Genic Reads
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
\end_layout
\end_inset
|
|
\begin_inset Text
\begin_layout Plain Layout
GB
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
\family roman
\series medium
\shape up
\size normal
\emph off
\bar no
\strikeout off
\xout off
\uuline off
\uwave off
\noun off
\color none
Non-globin Reads
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
\family roman
\series medium
\shape up
\size normal
\emph off
\bar no
\strikeout off
\xout off
\uuline off
\uwave off
\noun off
\color none
Globin Reads
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
\family roman
\series medium
\shape up
\size normal
\emph off
\bar no
\strikeout off
\xout off
\uuline off
\uwave off
\noun off
\color none
All Genic Reads
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
\family roman
\series medium
\shape up
\size normal
\emph off
\bar no
\strikeout off
\xout off
\uuline off
\uwave off
\noun off
\color none
All Aligned Reads
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
\family roman
\series medium
\shape up
\size normal
\emph off
\bar no
\strikeout off
\xout off
\uuline off
\uwave off
\noun off
\color none
Non-globin Reads
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
\family roman
\series medium
\shape up
\size normal
\emph off
\bar no
\strikeout off
\xout off
\uuline off
\uwave off
\noun off
\color none
Globin Reads
\end_layout
\end_inset
|
|
\begin_inset Text
\begin_layout Plain Layout
\family roman
\series medium
\shape up
\size normal
\emph off
\bar no
\strikeout off
\xout off
\uuline off
\uwave off
\noun off
\color none
Yes
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
\family roman
\series medium
\shape up
\size normal
\emph off
\bar no
\strikeout off
\xout off
\uuline off
\uwave off
\noun off
\color none
50.4% ± 6.82
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
\family roman
\series medium
\shape up
\size normal
\emph off
\bar no
\strikeout off
\xout off
\uuline off
\uwave off
\noun off
\color none
3.48% ± 2.94
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
\family roman
\series medium
\shape up
\size normal
\emph off
\bar no
\strikeout off
\xout off
\uuline off
\uwave off
\noun off
\color none
53.9% ± 6.81
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
\family roman
\series medium
\shape up
\size normal
\emph off
\bar no
\strikeout off
\xout off
\uuline off
\uwave off
\noun off
\color none
89.7% ± 2.40
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
\family roman
\series medium
\shape up
\size normal
\emph off
\bar no
\strikeout off
\xout off
\uuline off
\uwave off
\noun off
\color none
93.5% ± 5.25
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
\family roman
\series medium
\shape up
\size normal
\emph off
\bar no
\strikeout off
\xout off
\uuline off
\uwave off
\noun off
\color none
6.49% ± 5.25
\end_layout
\end_inset
|
|
\begin_inset Text
\begin_layout Plain Layout
\family roman
\series medium
\shape up
\size normal
\emph off
\bar no
\strikeout off
\xout off
\uuline off
\uwave off
\noun off
\color none
No
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
\family roman
\series medium
\shape up
\size normal
\emph off
\bar no
\strikeout off
\xout off
\uuline off
\uwave off
\noun off
\color none
26.3% ± 8.95
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
\family roman
\series medium
\shape up
\size normal
\emph off
\bar no
\strikeout off
\xout off
\uuline off
\uwave off
\noun off
\color none
44.6% ± 16.6
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
\family roman
\series medium
\shape up
\size normal
\emph off
\bar no
\strikeout off
\xout off
\uuline off
\uwave off
\noun off
\color none
70.1% ± 9.38
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
\family roman
\series medium
\shape up
\size normal
\emph off
\bar no
\strikeout off
\xout off
\uuline off
\uwave off
\noun off
\color none
90.7% ± 5.16
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
\family roman
\series medium
\shape up
\size normal
\emph off
\bar no
\strikeout off
\xout off
\uuline off
\uwave off
\noun off
\color none
38.8% ± 17.1
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
\family roman
\series medium
\shape up
\size normal
\emph off
\bar no
\strikeout off
\xout off
\uuline off
\uwave off
\noun off
\color none
61.2% ± 17.1
\end_layout
\end_inset
|
\end_inset
\end_layout
\begin_layout Plain Layout
\begin_inset Caption Standard
\begin_layout Plain Layout
\begin_inset Argument 1
status collapsed
\begin_layout Plain Layout
Fractions of reads mapping to genomic features in GB and non-GB samples.
\end_layout
\end_inset
\begin_inset CommandInset label
LatexCommand label
name "tab:Fractions-of-reads"
\end_inset
\series bold
Fractions of reads mapping to genomic features in GB and non-GB samples.
\series default
All values are given as mean ± standard deviation.
\end_layout
\end_inset
\end_layout
\end_inset
\end_layout
\begin_layout Standard
\begin_inset ERT
status open
\begin_layout Plain Layout
\backslash
end{landscape}
\end_layout
\begin_layout Plain Layout
}
\end_layout
\end_inset
\end_layout
\begin_layout Standard
This reduction is not quite as efficient as the previous analysis showed
for human samples by DeepSAGE (<0.4% globin reads after globin reduction)
\begin_inset CommandInset citation
LatexCommand cite
key "Mastrokolias2012"
literal "false"
\end_inset
.
Nonetheless, this degree of globin reduction is sufficient to nearly double
the yield of useful reads.
Thus,
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
GB
\end_layout
\end_inset
cuts the required sequencing effort (and costs) to achieve a target coverage
depth by almost 50%.
Consistent with this near doubling of yield, the average difference in
un-normalized
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
logCPM
\end_layout
\end_inset
across all genes between the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
GB
\end_layout
\end_inset
libraries and non-GB libraries is approximately 1 (mean = 1.01, median =
1.08), an overall 2-fold increase.
Un-normalized values are used here because the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
TMM
\end_layout
\end_inset
normalization correctly identifies this 2-fold difference as biologically
irrelevant and removes it.
\end_layout
\begin_layout Standard
Another important aspect is that the standard deviations in Table
\begin_inset CommandInset ref
LatexCommand ref
reference "tab:Fractions-of-reads"
plural "false"
caps "false"
noprefix "false"
\end_inset
are uniformly smaller in the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
GB
\end_layout
\end_inset
samples than the non-GB ones, indicating much greater consistency of yield.
This is best seen in the percentage of non-globin reads as a fraction of
total reads aligned to annotated genes (genic reads).
For the non-GB samples, this measure ranges from 10.9% to 80.9%, while for
the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
GB
\end_layout
\end_inset
samples it ranges from 81.9% to 99.9% (Figure
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:Fraction-of-genic-reads"
plural "false"
caps "false"
noprefix "false"
\end_inset
\begin_inset Float figure
wide false
sideways false
status collapsed
\begin_layout Plain Layout
\align center
\begin_inset Graphics
filename graphics/globin-paper/figure1-globin-fractions.pdf
lyxscale 50
width 100col%
groupId colfullwidth
\end_inset
\end_layout
\begin_layout Plain Layout
\begin_inset Caption Standard
\begin_layout Plain Layout
\begin_inset Argument 1
status collapsed
\begin_layout Plain Layout
Fraction of genic reads in each sample aligned to non-globin genes, with
and without GB.
\end_layout
\end_inset
\begin_inset CommandInset label
LatexCommand label
name "fig:Fraction-of-genic-reads"
\end_inset
\series bold
Fraction of genic reads in each sample aligned to non-globin genes, with
and without GB.
\series default
All reads in each sequencing library were aligned to the cyno genome, and
the number of reads uniquely aligning to each gene was counted.
For each sample, counts were summed separately for all globin genes and
for the remainder of the genes (non-globin genes), and the fraction of
genic reads aligned to non-globin genes was computed.
Each point represents an individual sample.
Gray + signs indicate the means for globin-blocked libraries and unblocked
libraries.
The overall distribution for each group is represented as a notched box
plot.
Points are randomly spread vertically to avoid excessive overlapping.
\end_layout
\end_inset
\end_layout
\end_inset
\begin_inset Note Note
status open
\begin_layout Plain Layout
Float lost issues
\end_layout
\end_inset
).
This means that for applications where it is critical that each sample
achieve a specified minimum coverage in order to provide useful information,
it would be necessary to budget up to 10 times the sequencing depth per
sample without
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
GB
\end_layout
\end_inset
, even though the average yield improvement for
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
GB
\end_layout
\end_inset
is only 2-fold, because every sample has a chance of being 90% globin and
10% useful reads.
Hence, the more consistent behavior of
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
GB
\end_layout
\end_inset
samples makes planning an experiment easier and more efficient because
it eliminates the need to over-sequence every sample in order to guard
against the worst case of a high-globin fraction.
\end_layout
\begin_layout Subsection
Globin blocking lowers the noise floor and allows detection of about 2000
more low-expression genes
\end_layout
\begin_layout Standard
\begin_inset Flex TODO Note (inline)
status open
\begin_layout Plain Layout
Remove redundant titles from figures
\end_layout
\end_inset
\end_layout
\begin_layout Standard
Since
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
GB
\end_layout
\end_inset
yields more usable sequencing depth, it should also allow detection of
more genes at any given threshold.
When we looked at the distribution of average normalized
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
logCPM
\end_layout
\end_inset
values across all libraries for genes with at least one read assigned to
them, we observed the expected bimodal distribution, with a high-abundance
"signal" peak representing detected genes and a low-abundance "noise" peak
representing genes whose read count did not rise above the noise floor
(Figure
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:logcpm-dists"
plural "false"
caps "false"
noprefix "false"
\end_inset
).
Consistent with the 2-fold increase in raw counts assigned to non-globin
genes, the signal peak for
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
GB
\end_layout
\end_inset
samples is shifted to the right relative to the non-GB signal peak.
When all the samples are normalized together, this difference is normalized
out, lining up the signal peaks, and this reveals that, as expected, the
noise floor for the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
GB
\end_layout
\end_inset
samples is about 2-fold lower.
This greater separation between signal and noise peaks in the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
GB
\end_layout
\end_inset
samples means that low-expression genes should be more easily detected
and more precisely quantified than in the non-GB samples.
\end_layout
\begin_layout Standard
\begin_inset Float figure
wide false
sideways false
status open
\begin_layout Plain Layout
\align center
\begin_inset Graphics
filename graphics/globin-paper/figure2-aveLogCPM-colored.pdf
lyxscale 50
height 60theight%
\end_inset
\end_layout
\begin_layout Plain Layout
\begin_inset Caption Standard
\begin_layout Plain Layout
\begin_inset Argument 1
status collapsed
\begin_layout Plain Layout
Distributions of average group gene abundances when normalized separately
or together.
\end_layout
\end_inset
\begin_inset CommandInset label
LatexCommand label
name "fig:logcpm-dists"
\end_inset
\series bold
Distributions of average group gene abundances when normalized separately
or together.
\series default
All reads in each sequencing library were aligned to the cyno genome, and
the number of reads uniquely aligning to each gene was counted.
Genes with zero counts in all libraries were discarded.
Libraries were normalized using the TMM method.
Libraries were split into GB and non-GB groups and the average logCPM was
computed.
The distribution of average gene logCPM values was plotted for both groups
using a kernel density plot to approximate a continuous distribution.
The GB logCPM distributions are marked in red, non-GB in blue.
The black vertical line denotes the chosen detection threshold of
\begin_inset Formula $-1$
\end_inset
.
Top panel: Libraries were split into GB and non-GB groups first and normalized
separately.
Bottom panel: Libraries were all normalized together first and then split
into groups.
\end_layout
\end_inset
\end_layout
\end_inset
\end_layout
\begin_layout Standard
Based on these distributions, we selected a detection threshold of
\begin_inset Formula $-1$
\end_inset
, which is approximately the leftmost edge of the trough between the signal
and noise peaks.
This represents the most liberal possible detection threshold that doesn't
call substantial numbers of noise genes as detected.
Among the full dataset, 13429 genes were detected at this threshold, and
22276 were not.
When considering the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
GB
\end_layout
\end_inset
libraries and non-GB libraries separately and re-computing normalization
factors independently within each group, 14535 genes were detected in the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
GB
\end_layout
\end_inset
libraries while only 12460 were detected in the non-GB libraries.
Thus,
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
GB
\end_layout
\end_inset
allowed the detection of 2000 extra genes that were buried under the noise
floor without
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
GB
\end_layout
\end_inset
.
This pattern of at least 2000 additional genes detected with
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
GB
\end_layout
\end_inset
was also consistent across a wide range of possible detection thresholds,
from -2 to 3 (see Figure
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:Gene-detections"
plural "false"
caps "false"
noprefix "false"
\end_inset
).
\end_layout
\begin_layout Standard
\begin_inset Float figure
wide false
sideways false
status open
\begin_layout Plain Layout
\align center
\begin_inset Graphics
filename graphics/globin-paper/figure3-detection.pdf
lyxscale 50
width 70col%
\end_inset
\end_layout
\begin_layout Plain Layout
\begin_inset Caption Standard
\begin_layout Plain Layout
\begin_inset Argument 1
status collapsed
\begin_layout Plain Layout
Gene detections as a function of abundance thresholds in GB and non-GB samples.
\end_layout
\end_inset
\begin_inset CommandInset label
LatexCommand label
name "fig:Gene-detections"
\end_inset
\series bold
Gene detections as a function of abundance thresholds in GB and non-GB samples.
\series default
Average logCPM was computed by separate group normalization as described
in Figure
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:logcpm-dists"
plural "false"
caps "false"
noprefix "false"
\end_inset
for both the GB and non-GB groups, as well as for all samples considered
as one large group.
For each every integer threshold from
\begin_inset Formula $-2$
\end_inset
to 3, the number of genes detected at or above that logCPM threshold was
plotted for each group.
\end_layout
\end_inset
\end_layout
\end_inset
\end_layout
\begin_layout Subsection
Globin blocking does not add significant additional noise or decrease sample
quality
\end_layout
\begin_layout Standard
One potential worry is that the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
GB
\end_layout
\end_inset
protocol could perturb the levels of non-globin genes.
There are two kinds of possible perturbations: systematic and random.
The former is not a major concern for detection of differential expression,
since a 2-fold change in every sample has no effect on the relative fold
change between samples.
In contrast, random perturbations would increase the noise and obscure
the signal in the dataset, reducing the capacity to detect differential
expression.
\end_layout
\begin_layout Standard
\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
LatexCommand ref
reference "fig:MA-plot"
plural "false"
caps "false"
noprefix "false"
\end_inset
).
Other than the 3 designated alpha and beta globin genes, two other genes
stand out as having especially large negative
\begin_inset Flex Glossary Term (pl)
status open
\begin_layout Plain Layout
logFC
\end_layout
\end_inset
: HBD and LOC1021365.
HBD, delta globin, is most likely targeted by the blocking
\begin_inset Flex Glossary Term (pl)
status open
\begin_layout Plain Layout
oligo
\end_layout
\end_inset
due to high sequence homology with the other globin genes.
LOC1021365 is the aforementioned
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
ncRNA
\end_layout
\end_inset
that is reverse-complementary to one of the alpha-like genes and that would
be expected to be removed during the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
GB
\end_layout
\end_inset
step.
All other genes appear in a cluster centered vertically at 0, and the vast
majority of genes in this cluster show an absolute
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
logFC
\end_layout
\end_inset
of 0.5 or less.
Nevertheless, many of these small perturbations are still statistically
significant, indicating that the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
GB
\end_layout
\end_inset
\begin_inset Flex Glossary Term (pl)
status open
\begin_layout Plain Layout
oligo
\end_layout
\end_inset
likely cause very small but non-zero systematic perturbations in measured
gene expression levels.
\end_layout
\begin_layout Standard
\begin_inset Float figure
wide false
sideways false
status open
\begin_layout Plain Layout
\align center
\begin_inset Graphics
filename graphics/globin-paper/figure4-maplot-colored.pdf
lyxscale 50
width 100col%
groupId colfullwidth
\end_inset
\end_layout
\begin_layout Plain Layout
\begin_inset Caption Standard
\begin_layout Plain Layout
\begin_inset Argument 1
status collapsed
\begin_layout Plain Layout
MA plot showing effects of GB on each gene's abundance.
\end_layout
\end_inset
\begin_inset CommandInset label
LatexCommand label
name "fig:MA-plot"
\end_inset
\series bold
MA plot showing effects of GB on each gene's abundance.
\series default
All libraries were normalized together as described in Figure
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:logcpm-dists"
plural "false"
caps "false"
noprefix "false"
\end_inset
, and genes with an average logCPM below
\begin_inset Formula $-1$
\end_inset
were filtered out.
Each remaining gene was tested for differential abundance with respect
to
\begin_inset Flex Glossary Term (glstext)
status open
\begin_layout Plain Layout
GB
\end_layout
\end_inset
using
\begin_inset Flex Code
status open
\begin_layout Plain Layout
edgeR
\end_layout
\end_inset
’s quasi-likelihood F-test, fitting a NB GLM to table of read counts in
each library.
For each gene,
\begin_inset Flex Code
status open
\begin_layout Plain Layout
edgeR
\end_layout
\end_inset
reported average logCPM, logFC, p-value, and BH-adjusted FDR.
Each gene's logFC was plotted against its logCPM, colored by FDR.
Red points are significant at
\begin_inset Formula $≤10\%$
\end_inset
FDR, and blue are not significant at that threshold.
The alpha and beta globin genes targeted for blocking are marked with large
triangles, while all other genes are represented as small points.
\end_layout
\end_inset
\end_layout
\end_inset
\end_layout
\begin_layout Standard
\begin_inset Flex TODO Note (inline)
status open
\begin_layout Plain Layout
Give these numbers the LaTeX math treatment
\end_layout
\end_inset
\end_layout
\begin_layout Standard
To evaluate the possibility of
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
GB
\end_layout
\end_inset
causing random perturbations and reducing sample quality, we computed the
Pearson correlation between
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
logCPM
\end_layout
\end_inset
values for every pair of samples with and without
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
GB
\end_layout
\end_inset
and plotted them against each other (Figure
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:gene-abundance-correlations"
plural "false"
caps "false"
noprefix "false"
\end_inset
).
The plot indicated that the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
GB
\end_layout
\end_inset
libraries have higher sample-to-sample correlations than the non-GB libraries.
Parametric and nonparametric tests for differences between the correlations
with and without
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
GB
\end_layout
\end_inset
both confirmed that this difference was highly significant (2-sided paired
t-test:
\begin_inset Formula $t=37.2$
\end_inset
,
\begin_inset Formula $d.f.=665$
\end_inset
,
\begin_inset Formula $P\ll2.2\times10^{-16}$
\end_inset
; 2-sided Wilcoxon sign-rank test:
\begin_inset Formula $V=2195$
\end_inset
,
\begin_inset Formula $P\ll2.2\times10^{-16}$
\end_inset
).
Performing the same tests on the Spearman correlations gave the same conclusion
(t-test:
\begin_inset Formula $t=26.8$
\end_inset
,
\begin_inset Formula $d.f.=665$
\end_inset
,
\begin_inset Formula $P\ll2.2\times10^{-16}$
\end_inset
; sign-rank test:
\begin_inset Formula $V=8781$
\end_inset
,
\begin_inset Formula $P\ll2.2\times10^{-16}$
\end_inset
).
The
\begin_inset Flex Code
status open
\begin_layout Plain Layout
edgeR
\end_layout
\end_inset
package was used to compute the overall
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
BCV
\end_layout
\end_inset
for
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
GB
\end_layout
\end_inset
and non-GB libraries, and found that
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
GB
\end_layout
\end_inset
resulted in a negligible increase in the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
BCV
\end_layout
\end_inset
(0.417 with
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
GB
\end_layout
\end_inset
vs.
0.400 without).
The near equality of the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
BCV
\end_layout
\end_inset
for both sets indicates that the higher correlations in the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
GB
\end_layout
\end_inset
libraries are most likely a result of the increased yield of useful reads,
which reduces the contribution of Poisson counting uncertainty to the overall
variance of the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
logCPM
\end_layout
\end_inset
values
\begin_inset CommandInset citation
LatexCommand cite
key "McCarthy2012"
literal "false"
\end_inset
.
This improves the precision of expression measurements and more than offsets
the negligible increase in
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
BCV
\end_layout
\end_inset
.
\end_layout
\begin_layout Standard
\begin_inset Float figure
wide false
sideways false
status open
\begin_layout Plain Layout
\align center
\begin_inset Graphics
filename graphics/globin-paper/figure5-corrplot.pdf
lyxscale 50
width 100col%
groupId colfullwidth
\end_inset
\end_layout
\begin_layout Plain Layout
\begin_inset Caption Standard
\begin_layout Plain Layout
\begin_inset Argument 1
status collapsed
\begin_layout Plain Layout
Comparison of inter-sample gene abundance correlations with and without
GB.
\end_layout
\end_inset
\begin_inset CommandInset label
LatexCommand label
name "fig:gene-abundance-correlations"
\end_inset
\series bold
Comparison of inter-sample gene abundance correlations with and without
GB.
\series default
All libraries were normalized together as described in Figure
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:logcpm-dists"
plural "false"
caps "false"
noprefix "false"
\end_inset
, and genes with an average logCPM less than
\begin_inset Formula $-1$
\end_inset
were filtered out.
Each gene’s logCPM was computed in each library using
\begin_inset Flex Code
status open
\begin_layout Plain Layout
edgeR
\end_layout
\end_inset
's
\begin_inset Flex Code
status open
\begin_layout Plain Layout
cpm
\end_layout
\end_inset
function.
For each pair of biological samples, the Pearson correlation between those
samples' GB libraries was plotted against the correlation between the same
samples’ non-GB libraries.
Each point represents an unique pair of samples.
The solid gray line shows a quantile-quantile plot of distribution of GB
correlations vs.
that of non-GB correlations.
The thin dashed line is the identity line, provided for reference.
\end_layout
\end_inset
\end_layout
\end_inset
\end_layout
\begin_layout Subsection
More differentially expressed genes are detected with globin blocking
\end_layout
\begin_layout Standard
To compare performance on differential gene expression tests, we took subsets
of both the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
GB
\end_layout
\end_inset
and non-GB libraries with exactly one pre-transplant and one post-transplant
sample for each animal that had paired samples available for analysis (
\begin_inset Formula $N=7$
\end_inset
animals,
\begin_inset Formula $N=14$
\end_inset
samples in each subset).
The same test for pre- vs.
post-transplant differential gene expression was performed on the same
7 pairs of samples from
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
GB
\end_layout
\end_inset
libraries and non-GB libraries, in each case using an
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
FDR
\end_layout
\end_inset
of 10% as the threshold of significance.
Out of 12,954 genes that passed the detection threshold in both subsets,
358 were called significantly differentially expressed in the same direction
in both sets; 1063 were differentially expressed in the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
GB
\end_layout
\end_inset
set only; 296 were differentially expressed in the non-GB set only; 2 genes
were called significantly up in the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
GB
\end_layout
\end_inset
set but significantly down in the non-GB set; and the remaining 11,235
were not called differentially expressed in either set.
These data are summarized in Table
\begin_inset CommandInset ref
LatexCommand ref
reference "tab:Comparison-of-significant"
plural "false"
caps "false"
noprefix "false"
\end_inset
.
The differences in
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
BCV
\end_layout
\end_inset
calculated by
\begin_inset Flex Code
status open
\begin_layout Plain Layout
edgeR
\end_layout
\end_inset
for these subsets of samples were negligible (
\begin_inset Formula $\textrm{BCV}=0.302$
\end_inset
for
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
GB
\end_layout
\end_inset
and 0.297 for non-GB).
\end_layout
\begin_layout Standard
\begin_inset Float table
wide false
sideways false
status collapsed
\begin_layout Plain Layout
\align center
\begin_inset Tabular
|
\begin_inset Text
\begin_layout Plain Layout
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
\series bold
No Globin Blocking
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
\end_layout
\end_inset
|
|
\begin_inset Text
\begin_layout Plain Layout
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
\series bold
Up
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
\series bold
NS
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
\series bold
Down
\end_layout
\end_inset
|
|
\begin_inset Text
\begin_layout Plain Layout
\series bold
Globin-Blocking
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
\series bold
Up
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
\family roman
\series medium
\shape up
\size normal
\emph off
\bar no
\strikeout off
\xout off
\uuline off
\uwave off
\noun off
\color none
231
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
\family roman
\series medium
\shape up
\size normal
\emph off
\bar no
\strikeout off
\xout off
\uuline off
\uwave off
\noun off
\color none
515
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
\family roman
\series medium
\shape up
\size normal
\emph off
\bar no
\strikeout off
\xout off
\uuline off
\uwave off
\noun off
\color none
2
\end_layout
\end_inset
|
|
\begin_inset Text
\begin_layout Plain Layout
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
\series bold
NS
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
\family roman
\series medium
\shape up
\size normal
\emph off
\bar no
\strikeout off
\xout off
\uuline off
\uwave off
\noun off
\color none
160
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
\family roman
\series medium
\shape up
\size normal
\emph off
\bar no
\strikeout off
\xout off
\uuline off
\uwave off
\noun off
\color none
11235
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
\family roman
\series medium
\shape up
\size normal
\emph off
\bar no
\strikeout off
\xout off
\uuline off
\uwave off
\noun off
\color none
136
\end_layout
\end_inset
|
|
\begin_inset Text
\begin_layout Plain Layout
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
\series bold
Down
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
\family roman
\series medium
\shape up
\size normal
\emph off
\bar no
\strikeout off
\xout off
\uuline off
\uwave off
\noun off
\color none
0
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
\family roman
\series medium
\shape up
\size normal
\emph off
\bar no
\strikeout off
\xout off
\uuline off
\uwave off
\noun off
\color none
548
\end_layout
\end_inset
|
\begin_inset Text
\begin_layout Plain Layout
\family roman
\series medium
\shape up
\size normal
\emph off
\bar no
\strikeout off
\xout off
\uuline off
\uwave off
\noun off
\color none
127
\end_layout
\end_inset
|
\end_inset
\end_layout
\begin_layout Plain Layout
\begin_inset Caption Standard
\begin_layout Plain Layout
\begin_inset Argument 1
status collapsed
\begin_layout Plain Layout
Comparison of significantly differentially expressed genes with and without
globin blocking.
\end_layout
\end_inset
\begin_inset CommandInset label
LatexCommand label
name "tab:Comparison-of-significant"
\end_inset
\series bold
Comparison of significantly differentially expressed genes with and without
globin blocking.
\series default
Up, Down: Genes significantly up/down-regulated in post-transplant samples
relative to pre-transplant samples, with a false discovery rate of 10%
or less.
NS: Non-significant genes (false discovery rate greater than 10%).
\end_layout
\end_inset
\end_layout
\end_inset
\end_layout
\begin_layout Standard
The key point is that the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
GB
\end_layout
\end_inset
data results in substantially more differentially expressed calls than
the non-GB data.
Since there is no gold standard for this dataset, it is impossible to be
certain whether this is due to under-calling of differential expression
in the non-GB samples or over-calling in the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
GB
\end_layout
\end_inset
samples.
However, given that both datasets are derived from the same biological
samples and have nearly equal
\begin_inset Flex Glossary Term (pl)
status open
\begin_layout Plain Layout
BCV
\end_layout
\end_inset
, it is more likely that the larger number of differential expression calls
in the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
GB
\end_layout
\end_inset
samples are genuine detections that were enabled by the higher sequencing
depth and measurement precision of the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
GB
\end_layout
\end_inset
samples.
Note that the same set of genes was considered in both subsets, so the
larger number of differentially expressed gene calls in the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
GB
\end_layout
\end_inset
data set reflects a greater sensitivity to detect significant differential
gene expression and not simply the larger total number of detected genes
in
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
GB
\end_layout
\end_inset
samples described earlier.
\end_layout
\begin_layout Section
Discussion
\end_layout
\begin_layout Standard
The original experience with whole blood gene expression profiling on DNA
microarrays demonstrated that the high concentration of globin transcripts
reduced the sensitivity to detect genes with relatively low expression
levels, in effect, significantly reducing the sensitivity.
To address this limitation, commercial protocols for globin reduction were
developed based on strategies to block globin transcript amplification
during labeling or physically removing globin transcripts by affinity bead
methods
\begin_inset CommandInset citation
LatexCommand cite
key "Winn2010"
literal "false"
\end_inset
.
More recently, using the latest generation of labeling protocols and arrays,
it was determined that globin reduction was no longer necessary to obtain
sufficient sensitivity to detect differential transcript expression
\begin_inset CommandInset citation
LatexCommand cite
key "NuGEN2010"
literal "false"
\end_inset
.
However, we are not aware of any publications using these currently available
protocols with the latest generation of microarrays that actually compare
the detection sensitivity with and without globin reduction.
However, in practice this has now been adopted generally primarily driven
by concerns for cost control.
The main objective of our work was to directly test the impact of globin
gene transcripts and a new
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
GB
\end_layout
\end_inset
protocol for application to the newest generation of differential gene
expression profiling determined using next generation sequencing.
\end_layout
\begin_layout Standard
The challenge of doing global gene expression profiling in cynomolgus monkeys
is that the current available arrays were never designed to comprehensively
cover this genome and have not been updated since the first assemblies
of the cynomolgus genome were published.
Therefore, we determined that the best strategy for peripheral blood profiling
was to perform deep
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
RNA-seq
\end_layout
\end_inset
and inform the workflow using the latest available genome assembly and
annotation
\begin_inset CommandInset citation
LatexCommand cite
key "Wilson2013"
literal "false"
\end_inset
.
However, it was not immediately clear whether globin reduction was necessary
for
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
RNA-seq
\end_layout
\end_inset
or how much improvement in efficiency or sensitivity to detect differential
gene expression would be achieved for the added cost and effort.
\end_layout
\begin_layout Standard
Existing strategies for globin reduction involve degradation or physical
removal of globin transcripts in a separate step prior to reverse transcription
\begin_inset CommandInset citation
LatexCommand cite
key "Mastrokolias2012,Choi2014,Shin2014"
literal "false"
\end_inset
.
This additional step adds significant time, complexity, and cost to sample
preparation.
Faced with the need to perform
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
RNA-seq
\end_layout
\end_inset
on large numbers of blood samples we sought a solution to globin reduction
that could be achieved purely by adding additional reagents during the
reverse transcription reaction.
Furthermore, we needed a globin reduction method specific to cynomolgus
globin sequences that would work an organism for which no kit is available
off the shelf.
\end_layout
\begin_layout Standard
As mentioned above, the addition of
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
GB
\end_layout
\end_inset
\begin_inset Flex Glossary Term (pl)
status open
\begin_layout Plain Layout
oligo
\end_layout
\end_inset
has a very small impact on measured expression levels of gene expression.
However, this is a non-issue for the purposes of differential expression
testing, since a systematic change in a gene in all samples does not affect
relative expression levels between samples.
However, we must acknowledge that simple comparisons of gene expression
data obtained by
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
GB
\end_layout
\end_inset
and non-GB protocols are not possible without additional normalization.
\end_layout
\begin_layout Standard
More importantly,
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
GB
\end_layout
\end_inset
not only nearly doubles the yield of usable reads, it also increases inter-samp
le correlation and sensitivity to detect differential gene expression relative
to the same set of samples profiled without
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
GB
\end_layout
\end_inset
.
In addition,
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
GB
\end_layout
\end_inset
does not add a significant amount of random noise to the data.
\begin_inset Flex Glossary Term (Capital)
status open
\begin_layout Plain Layout
GB
\end_layout
\end_inset
thus represents a cost-effective and low-effort way to squeeze more data
and statistical power out of the same blood samples and the same amount
of sequencing.
In conclusion,
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
GB
\end_layout
\end_inset
greatly increases the yield of useful
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
RNA-seq
\end_layout
\end_inset
reads mapping to the rest of the genome, with minimal perturbations in
the relative levels of non-globin genes.
Based on these results, globin transcript reduction using sequence-specific,
complementary blocking
\begin_inset Flex Glossary Term (pl)
status open
\begin_layout Plain Layout
oligo
\end_layout
\end_inset
is recommended for all deep
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
RNA-seq
\end_layout
\end_inset
of cynomolgus and other nonhuman primate blood samples.
\end_layout
\begin_layout Section
Future Directions
\end_layout
\begin_layout Standard
One drawback of the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
GB
\end_layout
\end_inset
method presented in this analysis is a poor yield of genic reads, only
around 50%.
In a separate experiment, the reagent mixture was modified so as to address
this drawback, resulting in a method that produces an even better reduction
in globin reads without reducing the overall fraction of genic reads.
However, the data showing this improvement consists of only a few test
samples, so the larger data set analyzed above was chosen in order to demonstra
te the effectiveness of the method in reducing globin reads while preserving
the biological signal.
\end_layout
\begin_layout Standard
The motivation for developing a fast practical way to enrich for non-globin
reads in cyno blood samples was to enable a large-scale
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
RNA-seq
\end_layout
\end_inset
experiment investigating the effects of mesenchymal stem cell infusion
on blood gene expression in cynomologus transplant recipients in a time
course after transplantation.
With the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
GB
\end_layout
\end_inset
method in place, the way is now clear for this experiment to proceed.
\end_layout
\begin_layout Chapter
\begin_inset CommandInset label
LatexCommand label
name "chap:Conclusions"
\end_inset
Conclusions
\end_layout
\begin_layout Standard
\begin_inset ERT
status collapsed
\begin_layout Plain Layout
\backslash
glsresetall
\end_layout
\end_inset
\begin_inset Note Note
status collapsed
\begin_layout Plain Layout
Reintroduce all abbreviations
\end_layout
\end_inset
\end_layout
\begin_layout Standard
In this work, I have presented a wide range of applications for high-thoughput
genomic and epigenomic assays based on sequencing and arrays in the context
of immunology and transplant rejection.
Chapter
\begin_inset CommandInset ref
LatexCommand ref
reference "chap:CD4-ChIP-seq"
plural "false"
caps "false"
noprefix "false"
\end_inset
described the use of
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
RNA-seq
\end_layout
\end_inset
and
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
ChIP-seq
\end_layout
\end_inset
to investigate the interplay between promoter histone marks and gene expression
during activation of naïve and memory CD4
\begin_inset Formula $^{+}$
\end_inset
T-cells.
Chapter
\begin_inset CommandInset ref
LatexCommand ref
reference "chap:Improving-array-based-diagnostic"
plural "false"
caps "false"
noprefix "false"
\end_inset
explored the use of expression microarrays and methylation arrays for diagnosin
g transplant rejection.
Chapter
\begin_inset CommandInset ref
LatexCommand ref
reference "chap:Globin-blocking-cyno"
plural "false"
caps "false"
noprefix "false"
\end_inset
introduced a new
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
RNA-seq
\end_layout
\end_inset
protocol for sequencing blood samples from cynomolgus monkeys designed
to expedite gene expression profiling in serial blood samples from monkeys
who received an experimental treatment for transplant rejection based on
\begin_inset Flex Glossary Term (pl)
status open
\begin_layout Plain Layout
MSC
\end_layout
\end_inset
.
These applications range from basic science to translational medicine,
but in all cases, high-thoughput genomic assays were central to the results.
\end_layout
\begin_layout Section
Every high-throughput analysis presents unique analysis challenges
\end_layout
\begin_layout Standard
In addition, each of these applications of high-throughput genomic assays
presented unique analysis challenges that could not be solved simply by
stringing together standard off-the-shelf methods into a straightforward
analysis pipeline.
In every case, a bespoke analysis workflow tailored to the data was required,
and in no case was it possible to determine every step in the workflow
fully prior to seeing the data.
For example, exploratory data analysis of the CD4
\begin_inset Formula $^{+}$
\end_inset
T-cell
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
RNA-seq
\end_layout
\end_inset
data uncovered the batch effect, and the analysis was adjusted to compensate
for it.
Similarly, analysis of the
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
ChIP-seq
\end_layout
\end_inset
data required choosing an
\begin_inset Quotes eld
\end_inset
effective promoter radius
\begin_inset Quotes erd
\end_inset
based on the data itself, and several different peak callers were tested
before the correct choice became clear.
In the development of custom
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
fRMA
\end_layout
\end_inset
vectors, an appropriate batch size had to be chosen based on the properties
of the training data.
In the analysis of methylation array data, the appropriate analysis strategy
was not obvious and was determined by trying several plausible strategies
and inspecting the model paramters afterward to determine which strategy
appeared to best capture the observed properties of the data and which
strategies appeared to have systematic errors as a result of failing to
capture those properties.
The
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
GB
\end_layout
\end_inset
protocol went through several rounds of testing before satisfactory performance
was achieved, and as mentioned, optimization of the protocol has continued
past the version described here.
These are only a few examples out of many instances of analysis decisions
motivated by the properties of the data.
\end_layout
\begin_layout Section
Successful data analysis requires a toolbox, not a pipeline
\end_layout
\begin_layout Standard
Multiple times throughout this work, I have attempted to construct standard,
reusable, pipelines for analysis of specific kinds of data, such as
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
RNA-seq
\end_layout
\end_inset
or
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
ChIP-seq
\end_layout
\end_inset
.
Each time, the very next data set containing this data broke one or more
of the assumptions I had built into the pipeline, such as an RNA-seq dataset
where some samples aligned to the sense strand while others aligned to
the antisense strand, or the discovery that the effective promoter radius
varies by histone mark.
Each violation of an assumption required a significant rewrite of the pipeline'
s code in order to accommodate the new aspect of the data.
The prospect of reusability turned out to be a pipe(line) dream.
After several attempts to extend my pipelines to be general enough to handle
an ever-increasing variety of data idiosyncrasies, I realized that it was
actually
\emph on
less
\emph default
work to reimplement an analysis workflow from scratch each time rather
than try to adapt an existing workflow that was originally designed for
a different data set.
\end_layout
\begin_layout Standard
Once I embraced the idea of writing a bespoke analysis workflow for every
data set instead of a one-size-fits-all pipeline, I stopped thinking of
the pipeline as the atomic unit of analysis.
Instead, I focused on developing an understanding of the component parts
of each pipeline, which problems each part solves, and what assumptions
it makes, so that when I was presented with a new data set, I could quickly
select the appropriate analysis methods for that data set and compose them
into a new workflow to answer the demands of a new data set.
In cases where no off-the-shelf method existed to address a specific aspect
of the data, knowing about a wide range of analysis methods allowed me
to select the one that was closest to what I needed and adapt it accordingly,
even if it was not originally designed to handle the kind of data I was
analyzing.
For example, when analyzing heteroskedastic methylation array data, I adapted
the
\begin_inset Flex Code
status open
\begin_layout Plain Layout
voom
\end_layout
\end_inset
method from
\begin_inset Flex Code
status open
\begin_layout Plain Layout
limma
\end_layout
\end_inset
, which was originally designed to model heteroskedasticity in
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
RNA-seq
\end_layout
\end_inset
data
\begin_inset CommandInset citation
LatexCommand cite
key "Law2014"
literal "false"
\end_inset
.
While
\begin_inset Flex Code
status open
\begin_layout Plain Layout
voom
\end_layout
\end_inset
was designed to accept read counts, I determined that this was not a fundamenta
l assumption of the method but rather a limitation of the specific implementatio
n, and I was able to craft a modified implementation that accepted
\begin_inset Flex Glossary Term (pl)
status open
\begin_layout Plain Layout
M-value
\end_layout
\end_inset
from methylation arrays.
In contrast, adapting another method such as
\begin_inset Flex Code
status open
\begin_layout Plain Layout
edgeR
\end_layout
\end_inset
for methylation arrays would not be possible, since many steps of the
\begin_inset Flex Code
status open
\begin_layout Plain Layout
edgeR
\end_layout
\end_inset
workflow, from normalization to dispersion estimation to model fitting,
assume that the input is given on the scale of raw counts and take full
advantage of this assumption
\begin_inset CommandInset citation
LatexCommand cite
key "Robinson2010,Robinson2010a,McCarthy2012,Chen2014"
literal "false"
\end_inset
.
In short, I collected a
\begin_inset Quotes eld
\end_inset
toolbox
\begin_inset Quotes erd
\end_inset
full of useful modular analysis methods and developed the knowledge of
when and where each could be applied, as well as how to compose them on
demand into pipelines for specific data sets.
This prepared me to handle the idiosyncrasies of any new data set, even
when the new data has problems that I have not previously encountered in
any other data set.
\end_layout
\begin_layout Standard
Reusable pipelines have their place, but that place is in automating established
processes, not researching new science.
For example, the custom
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
fRMA
\end_layout
\end_inset
vectors developed in Chapter
\begin_inset CommandInset ref
LatexCommand ref
reference "chap:Improving-array-based-diagnostic"
plural "false"
caps "false"
noprefix "false"
\end_inset
, are being incorporated into an automated pipeline for diagnosing transplant
rejection using biopsy and blood samples from transplant recipients.
Once ready, this diagnostic method will consist of normalization using
the pre-trained
\begin_inset Flex Glossary Term
status open
\begin_layout Plain Layout
fRMA
\end_layout
\end_inset
vectors, followed by classification of the sample by a pre-trained classifier,
which outputs a posterior probability of acute rejection.
This is a perfect use case for a proper pipeline: repeating the exact same
sequence of analysis steps many times.
The input to the pipeline is sufficiently well-controlled that we can guarantee
it will satisfy the assumptions of the pipeline.
But research data is not so well-controlled, so when analyzing data in
a research context, the analysis must conform to the data, rather than
trying to force the data to conform to a preferred analysis strategy.
That means having a toolbox full of composable methods ready to respond
to the observed properties of the data.
\end_layout
\begin_layout Standard
\align center
\begin_inset ERT
status collapsed
\begin_layout Plain Layout
% Use "References" as the title of the Bibliography
\end_layout
\begin_layout Plain Layout
\backslash
renewcommand{
\backslash
bibname}{References}
\end_layout
\end_inset
\end_layout
\begin_layout Standard
\begin_inset CommandInset bibtex
LatexCommand bibtex
btprint "btPrintCited"
bibfiles "code-refs,refs-PROCESSED"
options "bibtotoc"
\end_inset
\end_layout
\end_body
\end_document