Thesis/thesis.lyx

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\begin_body

\begin_layout Title
Bioinformatic analysis of complex, high-throughput genomic and epigenomic
 data in the context of immunology and transplant rejection
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\begin_layout Author
A thesis presented 
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by
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 Ryan C.
 Thompson
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 to
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 The Scripps Research Institute Graduate Program 
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in partial fulfillment of the requirements for the degree of
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 Doctor of Philosophy in the subject of Biology
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 for
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 The Scripps Research Institute
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 La Jolla, California
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\begin_layout Date
October 2019
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\begin_layout Standard
[Copyright notice]
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\begin_layout Standard
[Thesis acceptance form]
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\begin_layout Standard
[Dedication]
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\begin_layout Standard
[Acknowledgements]
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LatexCommand tableofcontents

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\begin_layout Standard
[List of Abbreviations]
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Look into auto-generated nomenclature list: https://wiki.lyx.org/Tips/Nomenclature
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\begin_layout List of TODOs

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On final pass: Check all figures to make sure they fit on the page with
 their legends.
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\begin_layout Chapter*
Abstract
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It is included as an integral part of the thesis and should immediately
 precede the introduction.
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\begin_layout Plain Layout
Preparing your Abstract.
 Your abstract (a succinct description of your work) is limited to 350 words.
 UMI will shorten it if they must; please do not exceed the limit.
\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.
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\begin_layout Itemize
Do not include graphs, charts, tables, or illustrations in your abstract.
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\begin_layout Chapter
Introduction
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\begin_layout Section
Background & Significance
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\begin_layout Subsection
Biological motivation
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\begin_layout Itemize
Rejection is the major long-term threat to organ and tissue grafts
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\begin_deeper
\begin_layout Itemize
Common mechanisms of rejection 
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\begin_layout Itemize
Effective immune suppression requires monitoring for rejection and tuning
 
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\begin_layout Itemize
Current tests for rejection (tissue biopsy) are invasive and biased 
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\begin_layout Itemize
A blood test based on microarrays would be less biased and invasive
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\end_deeper
\begin_layout Itemize
Memory cells are resistant to immune suppression
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\begin_deeper
\begin_layout Itemize
Mechanisms of resistance in memory cells are poorly understood 
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\begin_layout Itemize
A better understanding of immune memory formation is needed
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\end_deeper
\begin_layout Itemize
Mesenchymal stem cell infusion is a promising new treatment to prevent/delay
 rejection
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\begin_deeper
\begin_layout Itemize
Demonstrated in mice, but not yet in primates
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\begin_layout Itemize
Mechanism currently unknown, but MSC are known to be immune modulatory
\end_layout

\end_deeper
\begin_layout Subsection
Overview of bioinformatic analysis methods
\end_layout

\begin_layout Standard
An overview of all the methods used, including what problem they solve,
 what assumptions they make, and a basic description of how they work.
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\begin_layout Itemize
ChIP-seq Peak calling
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\begin_deeper
\begin_layout Itemize
Cross-correlation analysis to determine fragment size
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\begin_layout Itemize
Broad vs narrow peaks
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\begin_layout Itemize
SICER for broad peaks
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\begin_layout Itemize
IDR for biologically reproducible peaks
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\begin_layout Itemize
csaw peak filtering guidelines for unbiased downstream analysis
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\end_deeper
\begin_layout Itemize
Normalization is non-trivial and application-dependant
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\begin_deeper
\begin_layout Itemize
Expression arrays: RMA & fRMA; why fRMA is needed
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\begin_layout Itemize
Methylation arrays: M-value transformation approximates normal data but
 induces heteroskedasticity
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\begin_layout Itemize
RNA-seq: normalize based on assumption that the average gene is not changing
\end_layout

\begin_layout Itemize
ChIP-seq: complex with many considerations, dependent on experimental methods,
 biological system, and analysis goals
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\end_deeper
\begin_layout Itemize
Limma: The standard linear modeling framework for genomics
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\begin_deeper
\begin_layout Itemize
empirical Bayes variance modeling: limma's core feature
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\begin_layout Itemize
edgeR & DESeq2: Extend with negative bonomial GLM for RNA-seq and other
 count data
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\begin_layout Itemize
voom: Extend with precision weights to model mean-variance trend
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\begin_layout Itemize
arrayWeights and duplicateCorrelation to handle complex variance structures
\end_layout

\end_deeper
\begin_layout Itemize
sva and ComBat for batch correction
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\begin_layout Itemize
Factor analysis: PCA, MDS, MOFA
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\begin_deeper
\begin_layout Itemize
Batch-corrected PCA is informative, but careful application is required
 to avoid bias
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\end_deeper
\begin_layout Itemize
Gene set analysis: camera and SPIA
\end_layout

\begin_layout Section
Innovation
\end_layout

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

\begin_deeper
\begin_layout Itemize
Characterize MSC response to interferon gamma
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\begin_layout Itemize
IFN-g is thought to stimulate their function
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\begin_layout Itemize
Test IFN-g treated MSC infusion as a therapy to delay graft rejection in
 cynomolgus monkeys
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\begin_layout Itemize
Monitor animals post-transplant using blood RNA-seq at serial time points
\end_layout

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

\begin_deeper
\begin_layout Itemize
Previous studies have looked at single snapshots of histone marks
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\begin_layout Itemize
Instead, look at changes in histone marks across activation and memory
\end_layout

\end_deeper
\begin_layout Itemize
High-throughput sequencing and microarray technologies
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\begin_deeper
\begin_layout Itemize
Powerful methods for assaying gene expression and epigenetics across entire
 genomes
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\begin_layout Itemize
Proper analysis requires finding and exploiting systematic genome-wide trends
\end_layout

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

\begin_layout Standard
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Chapter author list: Me, Sarah, Dan
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Need better section titles throughout the chapter
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\begin_layout Section
Approach
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\begin_layout Itemize
CD4 T-cells are central to all adaptive immune responses and memory
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\begin_layout Itemize
H3K4 and H3K27 methylation are major epigenetic regulators of gene expression
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\begin_layout Itemize
Canonically, H3K4 is activating and H3K27 is inhibitory, but the reality
 is complex
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\begin_layout Itemize
Looking at these marks during CD4 activation and memory should reveal new
 mechanistic details
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\begin_layout Itemize
Test 
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poised promoter
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 hypothesis in which H3K4 and H3K27 are both methylated
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\begin_layout Itemize
Expand scope of analysis beyond simple promoter counts
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\begin_deeper
\begin_layout Itemize
Analyze peaks genome-wide, including in intergenic regions
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\begin_layout Itemize
Analysis of coverage distribution shape within promoters, e.g.
 upstream vs downstream coverage
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\end_deeper
\begin_layout Section
Methods
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\begin_layout Standard
A reproducible workflow 
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key "gh-cd4-csaw"
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 was written to analyze the raw ChIP-seq and RNA-seq data from previous
 studies 
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key "LaMere2016,LaMere2017"
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.
 Briefly, this data consists of RNA-seq and ChIP-seq from CD4 T-cells cultured
 from 4 donors.
 From each donor, naive and memory CD4 T-cells were isolated separately.
 Then cultures of both cells were activated [how?], and samples were taken
 at 4 time points: Day 0 (pre-activation), Day 1 (early activation), Day
 5 (peak activation), and Day 14 (post-activation).
 For each combination of cell type and time point, RNA was isolated, and
 ChIP-seq was performed for each of 3 histone marks: H3K4me2, H3K4me3, and
 H3K27me3.
 The ChIP-seq input was also sequenced for each sample.
 The result was 32 samples for each assay.
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Sequence reads were retrieved from the Sequence Read Archive (SRA) 
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.
 ChIP-seq (and input) reads were aligned to CRCh38 genome assembly using
 Bowtie 2 
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literal "false"

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.
 Artifact regions were annotated using a custom implementation of the GreyListCh
IP algorithm, and these 
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greylists
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 were merged with the ENCODE blacklist 
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key "greylistchip,Amemiya2019,Dunham2012"
literal "false"

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.
 Any read or peak overlapping one of these regions was regarded as artifactual
 and excluded from downstream analyses.
 
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Peaks are called using epic, an implementation of the SICER algorithm 
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.
 Peaks are also called separately using MACS, but MACS was determined to
 be a poor fit for the data, and these peak calls are not used further 
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literal "false"

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.
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\begin_layout Itemize
Re-analyze previously published CD4 ChIP-seq & RNA-seq data 
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\begin_deeper
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Completely reimplement analysis from scratch as a reproducible workflow
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\begin_layout Itemize
Use newly published methods & algorithms not available during the original
 analysis: SICER, csaw, MOFA 
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, ComBat, sva, GREAT, and more
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SICER, IDR, csaw, & GREAT to call ChIP-seq peaks genome-wide, perform differenti
al abundance analysis, and relate those peaks to gene expression
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\begin_layout Itemize
Promoter counts in sliding windows around each gene's highest-expressed
 TSS to investigate coverage distribution within promoters
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\begin_layout Subsection
RNA-seq align+quant method comparison
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STAR quantification, Entrez vs Ensembl gene annotation
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Salmon+Shoal quantification, Entrez vs Ensembl gene annotation
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STAR vs HISAT2 quantification, Ensembl gene annotation
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Salomn vs STAR quantification, Ensembl gene annotation
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Salmon vs Kallisto quantification, Ensembl gene annotation
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Salmon+Shoal vs Salmon alone, Ensembl gene annotation
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RNA-seq comparisons
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\begin_layout Itemize
Ultimately selected shoal as quantification, Ensembl as annotation.
 Why? Running downstream analyses with all quant methods and both annotations
 showed very little practical difference, so choice was not terribly important.
 Prefer shoal due to theoretical advantages.
 To note in discussion: reproducible workflow made it easy to do this, enabling
 an informed decision.
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RNA-seq has a large confounding batch effect
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RNA-seq sample weights, grouped by experimental and technical covariates.
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Batch 1 is garbage quality.
 Analyses involving batch 1 samples are expected to yield poor statistical
 power.
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Before batch correction
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After batch correction with ComBat
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PCoA plots of RNA-seq data showing effect of batch correction.
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RNA-seq batch effect can be partially corrected, but still induces uncorrectable
 biases in downstream analysis
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ChIP-seq blacklisting is important
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Cross-correlation plots with blacklisted reads removed
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Cross-correlation plots without removing blacklisted reads
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Strand cross-correlation plots for ChIP-seq data.
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ChIP-seq peak calling
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\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout
Peak ranks from SICER peak caller
\end_layout

\end_inset


\end_layout

\begin_layout Plain Layout

\end_layout

\end_inset


\begin_inset space \hfill{}
\end_inset


\begin_inset Float figure
wide false
sideways false
status collapsed

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename graphics/CD4-csaw/IDR/D4659vsD5053_macs-PAGE1-CROP.pdf
	lyxscale 50
	width 45col%
	groupId idr-rc-subfig

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout
Peak ranks from MACS peak caller
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout

\series bold
\begin_inset CommandInset label
LatexCommand label
name "fig:IDR-rank-consist"

\end_inset

Irreproducible Discovery Rate rank consistency plots for H3K27me3.

\series default
 Peaks are ranked by the scores assigned by the peak caller in each donor,
 and then the ranks for two donors are plotted against each other.
 Higher ranks are more significant (top right).
 Peaks meeting various thresholds of reproducibility, measured by the irreproduc
ible discovery rate (IDR), are shaded accordingly.
 [This could be explained better, or refer to the text.]
\end_layout

\end_inset


\end_layout

\begin_layout Plain Layout

\end_layout

\end_inset


\end_layout

\begin_layout Standard
Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:IDR-rank-consist"
plural "false"
caps "false"
noprefix "false"

\end_inset

 shows the IDR rank-consistency plots for peaks called in an arbitrarily-chosen
 pair of donors.
 when the peaks for each donor are ranked according to their scores, SICER
 produces much more reproducible results between donors.
 This is consistent with SICER's stated goal of identifying broad peaks,
 in contrast to MACS, which is designed for identifying sharp peaks.
 Based on this observation, the SICER peak calls were used for all downstream
 analyses that involved ChIP-seq peaks.
 
\end_layout

\begin_layout Subsection
ChIP-seq normalization
\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/ChIP-seq/H3K4me2-sample-MAplot-bins-CROP.png
	lyxscale 25
	width 100col%
	groupId colwidth-raster

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout

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

\end_inset

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

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Subsection
ChIP-seq must be corrected for hidden confounding factors
\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/ChIP-seq/H3K4me2-PCA-raw-CROP.png
	lyxscale 25
	width 45col%
	groupId pcoa-subfig

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout

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

\end_inset

H3K4me2, no correction
\end_layout

\end_inset


\end_layout

\end_inset


\begin_inset space \hfill{}
\end_inset


\begin_inset Float figure
wide false
sideways false
status collapsed

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

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout

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

\end_inset

H3K4me2, SVs subtracted
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

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

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

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout

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

\end_inset

H3K4me3, no correction
\end_layout

\end_inset


\end_layout

\end_inset


\begin_inset space \hfill{}
\end_inset


\begin_inset Float figure
wide false
sideways false
status collapsed

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

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout

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

\end_inset

H3K4me3, SVs subtracted
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

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

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

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout

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

\end_inset

H3K27me3, no correction
\end_layout

\end_inset


\end_layout

\end_inset


\begin_inset space \hfill{}
\end_inset


\begin_inset Float figure
wide false
sideways false
status collapsed

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

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout

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

\end_inset

H3K27me3, SVs subtracted
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout

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

\end_inset

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

\end_inset


\end_layout

\begin_layout Plain Layout

\end_layout

\end_inset


\end_layout

\begin_layout Itemize
Figures showing BCV plots with and without SVA for each histone mark?
\end_layout

\begin_layout Subsection
MOFA recovers biologically relevant variation from blind analysis by correlating
 across datasets
\end_layout

\begin_layout Standard
\begin_inset ERT
status open

\begin_layout Plain Layout


\backslash
afterpage{
\end_layout

\begin_layout Plain Layout


\backslash
begin{landscape}
\end_layout

\end_inset


\end_layout

\begin_layout Standard
\begin_inset Float figure
wide false
sideways false
status 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-varExplaiend-matrix-CROP.png
	lyxscale 25
	width 45col%
	groupId mofa-subfig

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout

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

\end_inset

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

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

view
\begin_inset Quotes erd
\end_inset

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

\end_inset


\end_layout

\end_inset


\begin_inset space \hfill{}
\end_inset


\begin_inset Float figure
wide false
sideways false
status open

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename graphics/CD4-csaw/MOFA-LF-scatter-CROP.png
	lyxscale 25
	width 45col%
	groupId mofa-subfig

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout

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

\end_inset

Scatter plots of specific pairs of MOFA latent factors.

\series default
 LFs 1, 4, and 5 explain substantial variation in all data sets, so they
 are plotted against each other in order to reveal patterns of variation
 that are shared across all data sets.
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout

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

\end_inset

MOFA latent factors separate technical confounders from 
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Standard
\begin_inset ERT
status open

\begin_layout Plain Layout


\backslash
end{landscape}
\end_layout

\begin_layout Plain Layout

}
\end_layout

\end_inset


\end_layout

\begin_layout Itemize
Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:mofa-varexplained"
plural "false"
caps "false"
noprefix "false"

\end_inset

 shows that LF1, 4, and 5 explain substantial var in all data sets
\end_layout

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

\end_inset

 shows that those same 3 LFs, (1, 4, & 5) also correlate best with the experimen
tal factors (cell type & time point)
\end_layout

\begin_layout Itemize
LF2 is clearly the RNA-seq 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 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

\begin_layout Itemize
Attempting to remove the effect of LF2 (Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:mofa-batchsub"
plural "false"
caps "false"
noprefix "false"

\end_inset

) results in batch correction comparable to ComBat (Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:RNA-PCA-ComBat-batchsub"
plural "false"
caps "false"
noprefix "false"

\end_inset

)
\end_layout

\begin_layout Itemize
MOFA was able to do this batch subtraction without directly using the sample
 labels (sample labels were used implicitly to select which factor to subtract)
\end_layout

\begin_layout Itemize
Similarity of results shows that batch correction can't get much better
 than ComBat (despite ComBat ignoring time point)
\end_layout

\begin_layout Subsection
MOFA does some interesting stuff but is mostly confirmatory in this context
\end_layout

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

\begin_layout Plain Layout
MOFA should be a footnote to something else, not its own point
\end_layout

\end_inset


\end_layout

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

\begin_layout Plain Layout
Combine with previous subsection
\end_layout

\end_inset


\end_layout

\begin_layout Itemize
MOFA shows great promise for accelerating discovery of major biological
 effects in multi-omics datasets
\end_layout

\begin_deeper
\begin_layout Itemize
MOFA successfully separates biologically relevant patterns of variation
 from technical confounding factors without knowing the sample labels, by
 finding latent factors that explain variation across multiple data sets.
\end_layout

\begin_layout Itemize
MOFA was added to this analysis late and played primarily a confirmatory
 role, but it was able to confirm earlier conclusions with much less prior
 information (no sample labels) and much less analyst effort/input
\end_layout

\begin_layout Itemize
Less input from analyst means less opportunity to introduce unwanted bias
 into results
\end_layout

\begin_layout Itemize
MOFA confirmed that the already-implemented batch correction in the RNA-seq
 data was already performing as well as possible given the limitations of
 the data
\end_layout

\end_deeper
\begin_layout Section
Results
\end_layout

\begin_layout Standard
\begin_inset Note Note
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.
\end_layout

\begin_layout Plain Layout
Not every interesting result needs to be in here.
 Chapter should tell a story.
 
\end_layout

\end_inset


\end_layout

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

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

\end_inset


\end_layout

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

\begin_layout Plain Layout
Histone Mark
\end_layout

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

\begin_layout Plain Layout
# Peaks
\end_layout

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

\begin_layout Plain Layout
Mean peak width
\end_layout

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

\begin_layout Plain Layout
genome coverage
\end_layout

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

\begin_layout Plain Layout
FRiP
\end_layout

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

\begin_layout Plain Layout
H3K4me2
\end_layout

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

\begin_layout Plain Layout
14965
\end_layout

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

\begin_layout Plain Layout
3970
\end_layout

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

\begin_layout Plain Layout
1.92%
\end_layout

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

\begin_layout Plain Layout
14.2%
\end_layout

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

\begin_layout Plain Layout
H3K4me3
\end_layout

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

\begin_layout Plain Layout
6163
\end_layout

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

\begin_layout Plain Layout
2946
\end_layout

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

\begin_layout Plain Layout
0.588%
\end_layout

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

\begin_layout Plain Layout
6.57%
\end_layout

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

\begin_layout Plain Layout
H3K27me3
\end_layout

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

\begin_layout Plain Layout
18139
\end_layout

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

\begin_layout Plain Layout
18967
\end_layout

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

\begin_layout Plain Layout
11.1%
\end_layout

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

\begin_layout Plain Layout
22.5%
\end_layout

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

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout

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

\end_inset

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

\end_inset


\end_layout

\end_inset


\end_layout

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

\end_inset

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

\end_inset

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

\end_inset

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

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

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

\begin_layout Plain Layout
Ensure this figure uses the peak calls from the new analysis.
\end_layout

\end_inset


\end_layout

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

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

\end_inset


\end_layout

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

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

\end_inset


\end_layout

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

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

\end_inset

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

\end_inset


\end_layout

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

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout

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

\end_inset

Enrichment of peaks in promoter neighborhoods.
 
\series default
This plot shows the distribution of distances from each annotated transcription
 start site in the genome to the nearest called peak.
 Each line represents one combination of histone mark, cell type, and time
 point.
 Distributions are smoothed using kernel density estimation [CITE?].
 Transcription start sites that occur 
\emph on
within
\emph default
 peaks were excluded from this plot to avoid a large spike at zero that
 would overshadow the rest of the distribution.
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

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

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

\begin_layout Plain Layout
Histone mark
\end_layout

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

\begin_layout Plain Layout
Effective promoter radius
\end_layout

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

\begin_layout Plain Layout
H3K4me2
\end_layout

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

\begin_layout Plain Layout
1 kb
\end_layout

\end_inset
</cell>
</row>
<row>
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\series bold
\begin_inset CommandInset label
LatexCommand label
name "tab:effective-promoter-radius"

\end_inset

Effective promoter radius for each histone mark.

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

\end_inset

.
\end_layout

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\end_layout

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\end_layout

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\end_layout

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\begin_inset Flex TODO Note (inline)
status open

\begin_layout Plain Layout
Problem: the effective promoter radius concept is an interesting result
 on its own, hence its placement here.
 However, it is also important in the methods section, which comes first.
 What do? Refer forward to this section? Move this section to Methods?
\end_layout

\end_inset


\end_layout

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

\end_inset

.
 The majority of each density distribution is flat, representing the background
 density of peaks genome-wide.
 Each distribution has a peak near zero, representing an enrichment of peaks
 close transcription start site (TSS) positions relative to the remainder
 of the genome.
 Interestingly, the 
\begin_inset Quotes eld
\end_inset

radius
\begin_inset Quotes erd
\end_inset

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

\end_inset

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

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

kbp.
 These 
\begin_inset Quotes eld
\end_inset

effective promoter radii
\begin_inset Quotes erd
\end_inset

 were used to define the promoter regions for all further analyses.
\end_layout

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Clarify that radius depends on histone mark but 
\emph on
not
\emph default
 experimental condition.
 
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Consider also showing figure for distance to nearest peak center, and reference
 median peak size once that is known.
\end_layout

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\begin_layout Subsection
H3K4 and H3K27 promoter methylation has broadly the expected correlation
 with gene expression
\end_layout

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This section can easily be cut, especially if I can't find those plots.
\end_layout

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\begin_layout Itemize
H3K4 is correlated with higher expression, and H3K27 is correlated with
 lower expression genome-wide
\end_layout

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Grr, gotta find these figures.
 Maybe in the old analysis? At least one of these plots is definitely in
 Sarah's paper.
\end_layout

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\end_layout

\begin_layout Itemize
Figures showing these correlations: box/violin plots of expression distributions
 with every combination of peak presence/absence in promoter
\end_layout

\begin_layout Itemize
Appropriate statistical tests showing significant differences in expected
 directions
\end_layout

\begin_layout Subsection
RNA-seq and H3K4 methylation patterns in naive and memory show convergence
 at day 14
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Number of significant promoters
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Est.
 differentially modified promoters
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\series bold
\begin_inset CommandInset label
LatexCommand label
name "tab:Number-signif-promoters"

\end_inset

Number of differentially modified promoters between naive and memory cells
 at each time point after activation.
 
\series default
This table shows both the number of differentially modified promoters detected
 at a 10% FDR threshold (left half), and the total number of differentially
 modified promoters as estimated using the method of 
\begin_inset CommandInset citation
LatexCommand cite
key "Phipson2013"
literal "false"

\end_inset

 (right half).
\end_layout

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}
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placement p
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\align center
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\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


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

\end_inset

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

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	groupId pcoa-prom-subfig

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PCoA plot of H3K4me3 promoters, after subtracting surrogate variables
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PCoA plot of H3K27me3 promoters, after subtracting surrogate variables
\end_layout

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RNA-seq PCoA showing principal coordiantes 2 and 3.
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PCoA plots for promoter ChIP-seq and expression RNA-seq data
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Check up on figure refs in this paragraph
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Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:PCoA-promoters"
plural "false"
caps "false"
noprefix "false"

\end_inset

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

\end_inset

.
 For all histone marks, evidence of differential modification between naive
 and memory samples was detected at every time point except day 14.
 The day 14 convergence pattern is also present in the RNA-seq data (Figure
 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:RNA-PCA-group"
plural "false"
caps "false"
noprefix "false"

\end_inset

), albiet in the 2nd and 3rd principal coordinates, indicating that it is
 not the most dominant pattern driving gene expression.
 Taken together, the data show that promoter histone methylation for these
 3 histone marks and RNA expression for naive and memory cells are most
 similar at day 14, the furthest time point after activation.
 MOFA was also able to capture this day 14 convergence pattern in latent
 factor 5 (Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:mofa-lf-scatter"
plural "false"
caps "false"
noprefix "false"

\end_inset

), which accounts for shared variation across all 3 histone marks and the
 RNA-seq data, confirming that this is a coordinated pattern across all
 4 data sets.
\end_layout

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

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For the figures in this section, 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.
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	filename graphics/CD4-csaw/ChIP-seq/H3K4me2-neighborhood-clusters-CROP.png
	lyxscale 25
	width 30col%
	groupId covprof-subfig

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\series bold
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name "fig:H3K4me2-neighborhood-clusters"

\end_inset

Average relative coverage for each bin in each cluster
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	lyxscale 25
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	groupId covprof-subfig

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

\end_inset

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

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Gene expression grouped by promoter coverage clusters.
\end_layout

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

kbp upstream to 5
\begin_inset space ~
\end_inset

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

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

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

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

shape
\begin_inset Quotes erd
\end_inset

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

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sideways false
status collapsed

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

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout

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

\end_inset

Average relative coverage for each bin in each cluster
\end_layout

\end_inset


\end_layout

\end_inset


\begin_inset space \hfill{}
\end_inset


\begin_inset Float figure
wide false
sideways false
status collapsed

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

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout

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

\end_inset

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

\end_inset


\end_layout

\end_inset


\begin_inset space \hfill{}
\end_inset


\begin_inset Float figure
wide false
sideways false
status collapsed

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

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout

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

\end_inset

Gene expression grouped by promoter coverage clusters.
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout

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

kbp upstream to 5
\begin_inset space ~
\end_inset

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

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

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

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

shape
\begin_inset Quotes erd
\end_inset

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

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Standard
\begin_inset ERT
status open

\begin_layout Plain Layout


\backslash
end{landscape}
\end_layout

\begin_layout Plain Layout

}
\end_layout

\end_inset


\end_layout

\begin_layout Itemize
H3K4me peaks seem to correlate with increased expression as long as they
 are anywhere near the TSS
\end_layout

\begin_layout Itemize
H3K27me3 peaks can have different correlations to gene expression depending
 on their position relative to TSS (e.g.
 upstream vs downstream) Results consistent with 
\begin_inset CommandInset citation
LatexCommand cite
key "Young2011"
literal "false"

\end_inset


\end_layout

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

\begin_layout Plain Layout
Show the figures where the negative result ended this line of inquiry
\end_layout

\end_inset


\end_layout

\begin_layout Section
Discussion
\end_layout

\begin_layout Subsection
Effective promoter radius
\end_layout

\begin_layout Itemize
"Promoter radius" is not constant and must be defined empirically for a
 given data set.
 Coverage within promoter radius has an expression correlation as well
\end_layout

\begin_layout Itemize
Further study required to demonstarte functional consequences of effective
 promoter radius (e.g.
 show diminished association with gene expression outside radius)
\end_layout

\begin_layout Subsection
Convergence
\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 colwidth

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout

\series bold
LaMere 2016 Figure 8, reproduced with permission.
\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
Look up some more references for these histone marks being involved in memory
 differentiation.
 (Ask Sarah)
\end_layout

\end_inset


\end_layout

\begin_layout Itemize
Naive-to-memory convergence implies that naive cells are differentiating
 into memory cells, and that gene expression and H3K4/K27 methylation are
 involved in this differentiation
\end_layout

\begin_deeper
\begin_layout Itemize
Convergence is consistent with Lamere2016 fig 8 
\begin_inset CommandInset citation
LatexCommand cite
key "LaMere2016"
literal "false"

\end_inset

 (which was created without the benefit of SVA)
\end_layout

\begin_layout Itemize
H3K27me3, canonically regarded as a deactivating mark, seems to have a more
 complex effect
\end_layout

\end_deeper
\begin_layout Subsection
Positional
\end_layout

\begin_layout Itemize
TSS positional coverage, hints of something interesting but no clear conclusions
\end_layout

\begin_layout Subsection
Workflow
\end_layout

\begin_layout Standard
\begin_inset ERT
status open

\begin_layout Plain Layout


\backslash
afterpage{
\end_layout

\begin_layout Plain Layout


\backslash
begin{landscape}
\end_layout

\end_inset


\end_layout

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

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename graphics/CD4-csaw/rulegraphs/rulegraph-all.pdf
	lyxscale 50
	width 100col%
	height 95theight%

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout
\begin_inset CommandInset label
LatexCommand label
name "fig:rulegraph"

\end_inset


\series bold
Dependency graph of steps in reproducible workflow
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Standard
\begin_inset ERT
status open

\begin_layout Plain Layout


\backslash
end{landscape}
\end_layout

\begin_layout Plain Layout

}
\end_layout

\end_inset


\end_layout

\begin_layout Itemize
Discuss advantages of developing using a reproducible workflow
\end_layout

\begin_deeper
\begin_layout Itemize
Decision-making based on trying every option and running the workflow downstream
 to see the effects
\end_layout

\end_deeper
\begin_layout Subsection
Data quality issues limit conclusions
\end_layout

\begin_layout Chapter
Improving array-based analyses of transplant rejection by optimizing data
 preprocessing
\end_layout

\begin_layout Standard
\begin_inset Note Note
status open

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

\end_inset


\end_layout

\begin_layout Section
Approach
\end_layout

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

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

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

\end_inset


\end_layout

\begin_layout Standard
Microarrays, bead arrays, and similar assays produce raw data in the form
 of fluorescence intensity measurements, with the each intensity measurement
 proportional to the abundance of some fluorescently-labelled target DNA
 or RNA sequence that base pairs to a specific probe sequence.
 However, these measurements for each probe are also affected my many technical
 confounding factors, such as the concentration of target material, strength
 of off-target binding, and the sensitivity of the imaging sensor.
 Some array designs also use multiple probe sequences for each target.
 Hence, extensive pre-processing of array data is necessary to normalize
 out the effects of these technical factors and summarize the information
 from multiple probes to arrive at a single usable estimate of abundance
 or other relevant quantity, such as a ratio of two abundances, for each
 target.
\end_layout

\begin_layout Standard
The choice of pre-processing algorithms used in the analysis of an array
 data set can have a large effect on the results of that analysis.
 However, despite their importance, these steps are often neglected or rushed
 in order to get to the more scientifically interesting analysis steps involving
 the actual biology of the system under study.
 Hence, it is often possible to achieve substantial gains in statistical
 power, model goodness-of-fit, or other relevant performance measures, by
 checking the assumptions made by each preprocessing step and choosing specific
 normalization methods tailored to the specific goals of the current analysis.
\end_layout

\begin_layout Subsection
Normalization for clinical microarray classifiers must be single-channel
\end_layout

\begin_layout Subsubsection
Standard normalization methods are unsuitable for clinical application
\end_layout

\begin_layout Standard
As the cost of performing microarray assays falls, there is increasing interest
 in using genomic assays for diagnostic purposes, such as distinguishing
 healthy transplants (TX) from transplants undergoing acute rejection (AR)
 or acute dysfunction with no rejection (ADNR).
 However, the the standard normalization algorithm used for microarray data,
 Robust Multi-chip Average (RMA) 
\begin_inset CommandInset citation
LatexCommand cite
key "Irizarry2003a"
literal "false"

\end_inset

, is not applicable in a clinical setting.
 Two of the steps in RMA, quantile normalization and probe summarization
 by median polish, depend on every array in the data set being normalized.
 This means that adding or removing any arrays from a data set changes the
 normalized values for all arrays, and data sets that have been normalized
 separately cannot be compared to each other.
 Hence, when using RMA, any arrays to be analyzed together must also be
 normalized together, and the set of arrays included in the data set must
 be held constant throughout an analysis.
\end_layout

\begin_layout Standard
These limitations present serious impediments to the use of arrays as a
 diagnostic tool.
 When training a classifier, the samples to be classified must not be involved
 in any step of the training process, lest their inclusion bias the training
 process.
 Once a classifier is deployed in a clinical setting, the samples to be
 classified will not even 
\emph on
exist
\emph default
 at the time of training, so including them would be impossible even if
 it were statistically justifiable.
 Therefore, any machine learning application for microarrays demands that
 the normalized expression values computed for an array must depend only
 on information contained within that array.
 This would ensure that each array's normalization is independent of every
 other array, and that arrays normalized separately can still be compared
 to each other without bias.
 Such a normalization is commonly referred to as 
\begin_inset Quotes eld
\end_inset

single-channel normalization
\begin_inset Quotes erd
\end_inset

.
\end_layout

\begin_layout Subsubsection
Several strategies are available to meet clinical normalization requirements
\end_layout

\begin_layout Standard
Frozen RMA (fRMA) addresses these concerns by replacing the quantile normalizati
on and median polish with alternatives that do not introduce inter-array
 dependence, allowing each array to be normalized independently of all others
 
\begin_inset CommandInset citation
LatexCommand cite
key "McCall2010"
literal "false"

\end_inset

.
 Quantile normalization is performed against a pre-generated set of quantiles
 learned from a collection of 850 publically available arrays sampled from
 a wide variety of tissues in the Gene Expression Omnibus (GEO).
 Each array's probe intensity distribution is normalized against these pre-gener
ated quantiles.
 The median polish step is replaced with a robust weighted average of probe
 intensities, using inverse variance weights learned from the same public
 GEO data.
 The result is a normalization that satisfies the requirements mentioned
 above: each array is normalized independently of all others, and any two
 normalized arrays can be compared directly to each other.
\end_layout

\begin_layout Standard
One important limitation of fRMA is that it requires a separate reference
 data set from which to learn the parameters (reference quantiles and probe
 weights) that will be used to normalize each array.
 These parameters are specific to a given array platform, and pre-generated
 parameters are only provided for the most common platforms, such as Affymetrix
 hgu133plus2.
 For a less common platform, such as hthgu133pluspm, is is necessary to
 learn custom parameters from in-house data before fRMA can be used to normalize
 samples on that platform 
\begin_inset CommandInset citation
LatexCommand cite
key "McCall2011"
literal "false"

\end_inset

.
\end_layout

\begin_layout Standard
One other option is the aptly-named Single Channel Array Normalization (SCAN),
 which adapts a normalization method originally designed for tiling arrays
 
\begin_inset CommandInset citation
LatexCommand cite
key "Piccolo2012"
literal "false"

\end_inset

.
 SCAN is truly single-channel in that it does not require a set of normalization
 paramters estimated from an external set of reference samples like fRMA
 does.
\end_layout

\begin_layout Subsection
Heteroskedasticity must be accounted for in methylation array data 
\end_layout

\begin_layout Subsubsection
Methylation array preprocessing induces heteroskedasticity
\end_layout

\begin_layout Standard
DNA methylation arrays are a relatively new kind of assay that uses microarrays
 to measure the degree of methylation on cytosines in specific regions arrayed
 across the genome.
 First, bisulfite treatment converts all unmethylated cytosines to uracil
 (which then become thymine after amplication) while leaving methylated
 cytosines unaffected.
 Then, each target region is interrogated with two probes: one binds to
 the original genomic sequence and interrogates the level of methylated
 DNA, and the other binds to the same sequence with all cytosines replaced
 by thymidines and interrogates the level of unmethylated DNA.
\end_layout

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

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename graphics/methylvoom/sigmoid.pdf
	lyxscale 50
	width 100col%
	groupId colwidth

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout
\begin_inset CommandInset label
LatexCommand label
name "fig:Sigmoid-beta-m-mapping"

\end_inset


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

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Standard
After normalization, these two probe intensities are summarized in one of
 two ways, each with advantages and disadvantages.
 β
\series bold
 
\series default
values, interpreted as fraction of DNA copies methylated, range from 0 to
 1.
 β
\series bold
 
\series default
values are conceptually easy to interpret, but the constrained range makes
 them unsuitable for linear modeling, and their error distributions are
 highly non-normal, which also frustrates linear modeling.
 M-values, interpreted as the log ratio of methylated to unmethylated copies,
 are computed by mapping the beta values from 
\begin_inset Formula $[0,1]$
\end_inset

 onto 
\begin_inset Formula $(-\infty,+\infty)$
\end_inset

 using a sigmoid curve (Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:Sigmoid-beta-m-mapping"
plural "false"
caps "false"
noprefix "false"

\end_inset

).
 This transformation results in values with better statistical perperties:
 the unconstrained range is suitable for linear modeling, and the error
 distributions are more normal.
 Hence, most linear modeling and other statistical testing on methylation
 arrays is performed using M-values.
\end_layout

\begin_layout Standard
However, the steep slope of the sigmoid transformation near 0 and 1 tends
 to over-exaggerate small differences in β values near those extremes, which
 in turn amplifies the error in those values, leading to a U-shaped trend
 in the mean-variance curve: extreme values have higher variances than values
 near the middle.
 This mean-variance dependency must be accounted for when fitting the linear
 model for differential methylation, or else the variance will be systematically
 overestimated for probes with moderate M-values and underestimated for
 probes with extreme M-values.
\end_layout

\begin_layout Subsubsection
The voom method for RNA-seq data can model M-value heteroskedasticity
\end_layout

\begin_layout Standard
RNA-seq read count data are also known to show heteroskedasticity, and the
 voom method was developed for modeling this heteroskedasticity by estimating
 the mean-variance trend in the data and using this trend to assign precision
 weights to each observation 
\begin_inset CommandInset citation
LatexCommand cite
key "Law2013"
literal "false"

\end_inset

.
 While methylation array data are not derived from counts and have a very
 different mean-variance relationship from that of typical RNA-seq data,
 the voom method makes no specific assumptions on the shape of the mean-variance
 relationship - it only assumes that the relationship is smooth enough to
 model using a lowess curve.
 Hence, the method is sufficiently general to model the mean-variance relationsh
ip in methylation array data.
 However, the standard implementation of voom assumes that the input is
 given in raw read counts, and it must be adapted to run on methylation
 M-values.
\end_layout

\begin_layout Section
Methods
\end_layout

\begin_layout Subsection
Evaluation of classifier performance with different normalization methods
\end_layout

\begin_layout Standard
For testing different expression microarray normalizations, a data set of
 157 hgu133plus2 arrays was used, consisting of blood samples from kidney
 transplant patients whose grafts had been graded as TX, AR, or ADNR via
 biopsy and histology (46 TX, 69 AR, 42 ADNR) 
\begin_inset CommandInset citation
LatexCommand cite
key "Kurian2014"
literal "true"

\end_inset

.
 Additionally, an external validation set of 75 samples was gathered from
 public GEO data (37 TX, 38 AR, no ADNR).
 
\end_layout

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

\begin_layout Plain Layout
Find appropriate GEO identifiers if possible.
 Kurian 2014 says GSE15296, but this seems to be different data.
 I also need to look up the GEO accession for the external validation set.
\end_layout

\end_inset


\end_layout

\begin_layout Standard
To evaluate the effect of each normalization on classifier performance,
 the same classifier training and validation procedure was used after each
 normalization method.
 The PAM package was used to train a nearest shrunken centroid classifier
 on the training set and select the appropriate threshold for centroid shrinking.
 Then the trained classifier was used to predict the class probabilities
 of each validation sample.
 From these class probabilities, ROC curves and area-under-curve (AUC) values
 were generated 
\begin_inset CommandInset citation
LatexCommand cite
key "Turck2011"
literal "false"

\end_inset

.
 Each normalization was tested on two different sets of training and validation
 samples.
 For internal validation, the 115 TX and AR arrays in the internal set were
 split at random into two equal sized sets, one for training and one for
 validation, each containing the same numbers of TX and AR samples as the
 other set.
 For external validation, the full set of 115 TX and AR samples were used
 as a training set, and the 75 external TX and AR samples were used as the
 validation set.
 Thus, 2 ROC curves and AUC values were generated for each normalization
 method: one internal and one external.
 Because the external validation set contains no ADNR samples, only classificati
on of TX and AR samples was considered.
 The ADNR samples were included during normalization but excluded from all
 classifier training and validation.
 This ensures that the performance on internal and external validation sets
 is directly comparable, since both are performing the same task: distinguising
 TX from AR.
\end_layout

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

\begin_layout Plain Layout
Summarize the get.best.threshold algorithm for PAM threshold selection, or
 just put the code online?
\end_layout

\end_inset


\end_layout

\begin_layout Standard
Six different normalization strategies were evaluated.
 First, 2 well-known non-single-channel normalization methods were considered:
 RMA and dChip 
\begin_inset CommandInset citation
LatexCommand cite
key "Li2001,Irizarry2003a"
literal "false"

\end_inset

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

\end_inset

.
 Post-processing with GRSN does not turn RMA or dChip into single-channel
 methods, but it may help mitigate batch effects and is therefore useful
 as a benchmark.
 Lastly, the two single-channel normalization methods, fRMA and SCAN, were
 tested 
\begin_inset CommandInset citation
LatexCommand cite
key "McCall2010,Piccolo2012"
literal "false"

\end_inset

.
 When evaluting internal validation performance, only the 157 internal samples
 were normalized; when evaluating external validation performance, all 157
 internal samples and 75 external samples were normalized together.
\end_layout

\begin_layout Standard
For demonstrating the problem with separate normalization of training and
 validation data, one additional normalization was performed: the internal
 and external sets were each normalized separately using RMA, and the normalized
 data for each set were combined into a single set with no further attempts
 at normalizing between the two sets.
 The represents approximately how RMA would have to be used in a clinical
 setting, where the samples to be classified are not available at the time
 the classifier is trained.
\end_layout

\begin_layout Subsection
Generating custom fRMA vectors for hthgu133pluspm array platform
\end_layout

\begin_layout Standard
In order to enable fRMA normalization for the hthgu133pluspm array platform,
 custom fRMA normalization vectors were trained using the frmaTools package
 
\begin_inset CommandInset citation
LatexCommand cite
key "McCall2011"
literal "false"

\end_inset

.
 Separate vectors were created for two types of samples: kidney graft biopsy
 samples and blood samples from graft recipients.
 For training, a 341 kidney biopsy samples from 2 data sets and 965 blood
 samples from 5 data sets were used as the reference set.
 Arrays were groups into batches based on unique combinations of sample
 type (blood or biopsy), diagnosis (TX, AR, etc.), data set, and scan date.
 Thus, each batch represents arrays of the same kind that were run together
 on the same day.
 For estimating the probe inverse variance weights, frmaTools requires equal-siz
ed batches, which means a batch size must be chosen, and then batches smaller
 than that size must be ignored, while batches larger than the chosen size
 must be downsampled.
 This downsampling is performed randomly, so the sampling process is repeated
 5 times and the resulting normalizations are compared to each other.
\end_layout

\begin_layout Standard
To evaluate the consistency of the generated normalization vectors, the
 5 fRMA vector sets generated from 5 random batch samplings were each used
 to normalize the same 20 randomly selected samples from each tissue.
 Then the normalized expression values for each probe on each array were
 compared across all normalizations.
 Each fRMA normalization was also compared against the normalized expression
 values obtained by normalizing the same 20 samples with ordinary RMA.
\end_layout

\begin_layout Subsection
Modeling methylation array M-value heteroskedasticy in linear models with
 modified voom implementation
\end_layout

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

\begin_layout Plain Layout
Put code on Github and reference it.
\end_layout

\end_inset


\end_layout

\begin_layout Standard
To investigate the whether DNA methylation could be used to distinguish
 between healthy and dysfunctional transplants, a data set of 78 Illumina
 450k methylation arrays from human kidney graft biopsies was analyzed for
 differential metylation between 4 transplant statuses: healthy transplant
 (TX), transplants undergoing acute rejection (AR), acute dysfunction with
 no rejection (ADNR), and chronic allograpft nephropathy (CAN).
 The data consisted of 33 TX, 9 AR, 8 ADNR, and 28 CAN samples.
 The uneven group sizes are a result of taking the biopsy samples before
 the eventual fate of the transplant was known.
 Each sample was additionally annotated with a donor ID (anonymized), Sex,
 Age, Ethnicity, Creatinine Level, and Diabetes diagnosois (all samples
 in this data set came from patients with either Type 1 or Type 2 diabetes).
 
\end_layout

\begin_layout Standard
The intensity data were first normalized using subset-quantile within array
 normalization (SWAN) 
\begin_inset CommandInset citation
LatexCommand cite
key "Maksimovic2012"
literal "false"

\end_inset

, then converted to intensity ratios (beta values) 
\begin_inset CommandInset citation
LatexCommand cite
key "Aryee2014"
literal "false"

\end_inset

.
 Any probes binding to loci that overlapped annotated SNPs were dropped,
 and the annotated sex of each sample was verified against the sex inferred
 from the ratio of median probe intensities for the X and Y chromosomes.
 Then, the ratios were transformed to M-values.
\end_layout

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

\begin_layout Plain Layout
\align center
\begin_inset Tabular
<lyxtabular version="3" rows="4" columns="6">
<features tabularvalignment="middle">
<column alignment="center" valignment="top">
<column alignment="center" valignment="top">
<column alignment="center" valignment="top">
<column alignment="center" valignment="top">
<column alignment="center" valignment="top">
<column alignment="center" valignment="top">
<row>
<cell alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
Analysis
\end_layout

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

\begin_layout Plain Layout
random effect
\end_layout

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

\begin_layout Plain Layout
eBayes
\end_layout

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

\begin_layout Plain Layout
SVA
\end_layout

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

\begin_layout Plain Layout
weights
\end_layout

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

\begin_layout Plain Layout
voom
\end_layout

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

\begin_layout Plain Layout
A
\end_layout

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

\begin_layout Plain Layout
Yes
\end_layout

\end_inset
</cell>
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\begin_layout Plain Layout

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

\end_inset

Summary of analysis variants for methylation array data.
 
\series default
Each analysis included a different set of steps to adjust or account for
 various systematic features of the data.
 Random effect: The model included a random effect accounting for correlation
 between samples from the same patient 
\begin_inset CommandInset citation
LatexCommand cite
key "Smyth2005a"
literal "false"

\end_inset

; eBayes: Empirical bayes squeezing of per-probe variances toward the mean-varia
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 "Law2013"
literal "false"

\end_inset

.
 See the text for a more detailed explanation of each step.
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Standard
From the M-values, a series of parallel analyses was performed, each adding
 additional steps into the model fit to accomodate a feature of the data
 (see Table 
\begin_inset CommandInset ref
LatexCommand ref
reference "tab:Summary-of-meth-analysis"
plural "false"
caps "false"
noprefix "false"

\end_inset

).
 For analysis A, a 
\begin_inset Quotes eld
\end_inset

basic
\begin_inset Quotes erd
\end_inset

 linear modeling analysis was performed, compensating for known confounders
 by including terms for the factor of interest (transplant status) as well
 as the known biological confounders: sex, age, ethnicity, and diabetes.
 Since some samples came from the same patients at different times, the
 intra-patient correlation was modeled as a random effect, estimating a
 shared correlation value across all probes 
\begin_inset CommandInset citation
LatexCommand cite
key "Smyth2005a"
literal "false"

\end_inset

.
 Then the linear model was fit, and the variance was modeled using empirical
 Bayes squeezing toward the mean-variance trend 
\begin_inset CommandInset citation
LatexCommand cite
key "Ritchie2015"
literal "false"

\end_inset

.
 Finally, t-tests or F-tests were performed as appropriate for each test:
 t-tests for single contrasts, and F-tests for multiple contrasts.
 P-values were corrected for multiple testing using the Benjamini-Hochberg
 procedure for FDR control 
\begin_inset CommandInset citation
LatexCommand cite
key "Benjamini1995"
literal "false"

\end_inset

.
\end_layout

\begin_layout Standard
For the analysis B, surrogate variable analysis (SVA) was used to infer
 additional unobserved sources of heterogeneity in the data 
\begin_inset CommandInset citation
LatexCommand cite
key "Leek2007"
literal "false"

\end_inset

.
 These surrogate variables were added to the design matrix before fitting
 the linear model.
 In addition, sample quality weights were estimated from the data and used
 during linear modeling to down-weight the contribution of highly variable
 arrays while increasing the weight to arrays with lower variability
\begin_inset CommandInset citation
LatexCommand cite
key "Ritchie2006"
literal "false"

\end_inset

.
 The remainder of the analysis proceeded as in analysis A.
 For analysis C, the voom method was adapted to run on methylation array
 data and used to model and correct for the mean-variance trend using individual
 observation weights 
\begin_inset CommandInset citation
LatexCommand cite
key "Law2013"
literal "false"

\end_inset

, which were combined with the sample weights 
\begin_inset CommandInset citation
LatexCommand cite
key "Liu2015,Ritchie2006"
literal "false"

\end_inset

.
 Each time weights were used, they were estimated once before estimating
 the random effect correlation value, and then the weights were re-estimated
 taking the random effect into account.
 The remainder of the analysis proceeded as in analysis B.
\end_layout

\begin_layout Section
Results
\end_layout

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\begin_inset Flex TODO Note (inline)
status open

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Improve subsection titles in this section
\end_layout

\end_inset


\end_layout

\begin_layout Subsection
fRMA eliminates unwanted dependence of classifier training on normalization
 strategy caused by RMA
\end_layout

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\begin_inset Flex TODO Note (inline)
status open

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Write figure legends
\end_layout

\end_inset


\end_layout

\begin_layout Subsubsection
Separate normalization with RMA introduces unwanted biases in classification
\end_layout

\begin_layout Standard
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wide false
sideways false
status collapsed

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename graphics/PAM/predplot.pdf
	lyxscale 50
	width 100col%
	groupId colwidth

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\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout
\begin_inset CommandInset label
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name "fig:Classifier-probabilities-RMA"

\end_inset


\series bold
Classifier probabilities on validation samples when normalized with RMA
 together vs.
 separately.
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Standard
To demonstrate the problem with non-single-channel normalization methods,
 we considered the problem of training a classifier to distinguish TX from
 AR using the samples from the internal set as training data, evaluating
 performance on the external set.
 First, training and evaluation were performed after normalizing all array
 samples together as a single set using RMA, and second, the internal samples
 were normalized separately from the external samples and the training and
 evaluation were repeated.
 For each sample in the validation set, the classifier probabilities from
 both classifiers were plotted against each other (Fig.
 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:Classifier-probabilities-RMA"
plural "false"
caps "false"
noprefix "false"

\end_inset

).
 As expected, separate normalization biases the classifier probabilities,
 resulting in several misclassifications.
 In this case, the bias from separate normalization causes the classifier
 to assign a lower probability of AR to every sample.
 
\end_layout

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

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	groupId roc-pam

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ROC curves for PAM on internal validation data
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ROC curves for PAM on external validation data
\end_layout

\end_inset


\end_layout

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

\end_inset

ROC curves for PAM using different normalization strategies
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Standard
\begin_inset Float table
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\align center
\begin_inset Tabular
<lyxtabular version="3" rows="7" columns="4">
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Normalization
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Single-channel?
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Internal Val.
 AUC
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External Val.
 AUC
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RMA
\end_layout

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</cell>
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\end_layout

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\begin_inset Text

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0.852
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\begin_inset Text

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0.713
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\color none
dChip
\end_layout

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\begin_inset Text

\begin_layout Plain Layout
No
\end_layout

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

\begin_layout Plain Layout

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0.891
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0.657
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\color none
RMA + GRSN
\end_layout

\end_inset
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\begin_inset Text

\begin_layout Plain Layout
No
\end_layout

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

\begin_layout Plain Layout

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0.816
\end_layout

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

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0.750
\end_layout

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<row>
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\begin_inset Text

\begin_layout Plain Layout

\family roman
\series medium
\shape up
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\emph off
\bar no
\strikeout off
\xout off
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\noun off
\color none
dChip + GRSN
\end_layout

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

\begin_layout Plain Layout
No
\end_layout

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

\begin_layout Plain Layout

\family roman
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0.875
\end_layout

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

\begin_layout Plain Layout

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0.642
\end_layout

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</cell>
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fRMA
\end_layout

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</cell>
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\begin_inset Text

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

\begin_layout Plain Layout

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0.863
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<cell alignment="center" valignment="top" topline="true" leftline="true" rightline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout

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\color none
0.718
\end_layout

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SCAN
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0.853
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<cell alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" rightline="true" usebox="none">
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0.689
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\begin_layout Plain Layout
\begin_inset CommandInset label
LatexCommand label
name "tab:AUC-PAM"

\end_inset


\series bold
AUC values for internal and external validation with 6 different normalization
 strategies.

\series default
 Only fRMA and SCAN are single-channel normalizations.
 The other 4 normalizations are for comparison.
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Standard
For internal validation, the 6 methods' AUC values ranged from 0.816 to 0.891,
 as shown in Table 
\begin_inset CommandInset ref
LatexCommand ref
reference "tab:AUC-PAM"
plural "false"
caps "false"
noprefix "false"

\end_inset

.
 Among the non-single-channel normalizations, dChip outperformed RMA, while
 GRSN reduced the AUC values for both dChip and RMA.
 Both single-channel methods, fRMA and SCAN, slightly outperformed RMA,
 with fRMA ahead of SCAN.
 However, the difference between RMA and fRMA is still quite small.
 Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:ROC-PAM-int"
plural "false"
caps "false"
noprefix "false"

\end_inset

 shows that the ROC curves for RMA, dChip, and fRMA look very similar and
 relatively smooth, while both GRSN curves and the curve for SCAN have a
 more jagged appearance.
\end_layout

\begin_layout Standard
For external validation, as expected, all the AUC values are lower than
 the internal validations, ranging from 0.642 to 0.750 (Table 
\begin_inset CommandInset ref
LatexCommand ref
reference "tab:AUC-PAM"
plural "false"
caps "false"
noprefix "false"

\end_inset

).
 With or without GRSN, RMA shows its dominance over dChip in this more challengi
ng test.
 Unlike in the internal validation, GRSN actually improves the classifier
 performance for RMA, although it does not for dChip.
 Once again, both single-channel methods perform about on par with RMA,
 with fRMA performing slightly better and SCAN performing a bit worse.
 Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:ROC-PAM-ext"
plural "false"
caps "false"
noprefix "false"

\end_inset

 shows the ROC curves for the external validation test.
 As expected, none of them are as clean-looking as the internal validation
 ROC curves.
 The curves for RMA, RMA+GRSN, and fRMA all look similar, while the other
 curves look more divergent.
\end_layout

\begin_layout Subsection
fRMA with custom-generated vectors enables normalization on hthgu133pluspm
\end_layout

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	filename graphics/frma-pax-bx/batchsize_batches.pdf
	lyxscale 50
	height 35theight%
	groupId frmatools-subfig

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\begin_inset CommandInset label
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name "fig:batch-size-batches"

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\series bold
Number of batches usable in fRMA probe weight learning as a function of
 batch size.
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

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

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename graphics/frma-pax-bx/batchsize_samples.pdf
	lyxscale 50
	height 35theight%
	groupId frmatools-subfig

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout
\begin_inset CommandInset label
LatexCommand label
name "fig:batch-size-samples"

\end_inset


\series bold
Number of samples usable in fRMA probe weight learning as a function of
 batch size.
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout

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

\end_inset

Effect of batch size selection on number of batches and number of samples
 included in fRMA probe weight learning.
 
\series default
For batch sizes ranging from 3 to 15, the number of batches (a) and samples
 (b) included in probe weight training were plotted for biopsy (BX) and
 blood (PAX) samples.
 The selected batch size, 5, is marked with a dotted vertical line.
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Standard
In order to enable use of fRMA to normalize hthgu133pluspm, a custom set
 of fRMA vectors was created.
 First, an appropriate batch size was chosen by looking at the number of
 batches and number of samples included as a function of batch size (Figure
 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:frmatools-batch-size"
plural "false"
caps "false"
noprefix "false"

\end_inset

).
 For a given batch size, all batches with fewer samples that the chosen
 size must be ignored during training, while larger batches must be randomly
 downsampled to the chosen size.
 Hence, the number of samples included for a given batch size equals the
 batch size times the number of batches with at least that many samples.
 From Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:batch-size-samples"
plural "false"
caps "false"
noprefix "false"

\end_inset

, it is apparent that that a batch size of 8 maximizes the number of samples
 included in training.
 Increasing the batch size beyond this causes too many smaller batches to
 be excluded, reducing the total number of samples for both tissue types.
 However, a batch size of 8 is not necessarily optimal.
 The article introducing frmaTools concluded that it was highly advantageous
 to use a smaller batch size in order to include more batches, even at the
 expense of including fewer total samples in training 
\begin_inset CommandInset citation
LatexCommand cite
key "McCall2011"
literal "false"

\end_inset

.
 To strike an appropriate balance between more batches and more samples,
 a batch size of 5 was chosen.
 For both blood and biopsy samples, this increased the number of batches
 included by 10, with only a modest reduction in the number of samples compared
 to a batch size of 8.
 With a batch size of 5, 26 batches of biopsy samples and 46 batches of
 blood samples were available.
\end_layout

\begin_layout Standard
\begin_inset Float figure
wide false
sideways false
status 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/frma-pax-bx/M-BX-violin.pdf
	lyxscale 40
	width 45col%
	groupId m-violin

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout
\begin_inset CommandInset label
LatexCommand label
name "fig:m-bx-violin"

\end_inset


\series bold
Violin plot of inter-normalization log ratios for biopsy samples.
 
\end_layout

\end_inset


\end_layout

\end_inset


\begin_inset space \hfill{}
\end_inset


\begin_inset Float figure
wide false
sideways false
status collapsed

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

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout
\begin_inset CommandInset label
LatexCommand label
name "fig:m-pax-violin"

\end_inset


\series bold
Violin plot of inter-normalization log ratios for blood samples.
 
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout

\series bold
Violin plot of log ratios between normalizations for 20 biopsy samples.
 
\series default
Each of 20 randomly selected samples was normalized with RMA and with 5
 different sets of fRMA vectors.
 The distribution of log ratios between normalized expression values, aggregated
 across all 20 arrays, was plotted for each pair of normalizations.
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Standard
Since fRMA training requires equal-size batches, larger batches are downsampled
 randomly.
 This introduces a nondeterministic step in the generation of normalization
 vectors.
 To show that this randomness does not substantially change the outcome,
 the random downsampling and subsequent vector learning was repeated 5 times,
 with a different random seed each time.
 20 samples were selected at random as a test set and normalized with each
 of the 5 sets of fRMA normalization vectors as well as ordinary RMA, and
 the normalized expression values were compared across normalizations.
 Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:m-bx-violin"
plural "false"
caps "false"
noprefix "false"

\end_inset

 shows a summary of these comparisons for biopsy samples.
 Comparing RMA to each of the 5 fRMA normalizations, the distribution of
 log ratios is somewhat wide, indicating that the normalizations disagree
 on the expression values of a fair number of probe sets.
 In contrast, comparisons of fRMA against fRMA, the vast mojority of probe
 sets have very small log ratios, indicating a very high agreement between
 the normalized values generated by the two normalizations.
 This shows that the fRMA normalization's behavior is not very sensitive
 to the random downsampling of larger batches during training.
\end_layout

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

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

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename graphics/frma-pax-bx/MA-BX-RMA.fRMA-RASTER.png
	lyxscale 10
	width 45col%
	groupId ma-frma

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout
\begin_inset CommandInset label
LatexCommand label
name "fig:ma-bx-rma-frma"

\end_inset


\series bold
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


\series bold
fRMA vs fRMA for biopsy samples.
 
\series default
Two different fRMA normalizations using vectors from two different batch
 samplings were compared.
\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


\series bold
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


\series bold
fRMA vs fRMA for blood samples.
 
\series default
Two different fRMA normalizations using vectors from two different batch
 samplings were compared.
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout

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

\end_inset

Representative MA plots comparing RMA and custom fRMA normalizations.
 
\series default
For each plot, 20 samples were normalized using 2 different normalizations,
 and then averages and log ratios were computed between the two different
 normalizations for every probe.
 Density of points is represented by darkness of shading, and individual
 outlier points are plotted.
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Standard
Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:ma-bx-rma-frma"
plural "false"
caps "false"
noprefix "false"

\end_inset

 shows an MA plot of the RMA-normalized values against the fRMA-normalized
 values for the same probe sets and arrays, corresponding to the first row
 of Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:m-bx-violin"
plural "false"
caps "false"
noprefix "false"

\end_inset

.
 This MA plot shows that not only is there a wide distribution of M-values,
 but the trend of M-values is dependent on the average normalized intensity.
 This is expected, since the overall trend represents the differences in
 the quantile normalization step.
 When running RMA, only the quantiles for these specific 20 arrays are used,
 while for fRMA the quantile distribution is taking from all arrays used
 in training.
 Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:ma-bx-frma-frma"
plural "false"
caps "false"
noprefix "false"

\end_inset

 shows a similar MA plot comparing 2 different fRMA normalizations, correspondin
g to the 6th row of Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:m-bx-violin"
plural "false"
caps "false"
noprefix "false"

\end_inset

.
 The MA plot is very tightly centered around zero with no visible trend.
 Figures 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:m-pax-violin"
plural "false"
caps "false"
noprefix "false"

\end_inset

, 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:MA-PAX-rma-frma"
plural "false"
caps "false"
noprefix "false"

\end_inset

, and 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:ma-bx-frma-frma"
plural "false"
caps "false"
noprefix "false"

\end_inset

 show exactly the same information for the blood samples, once again comparing
 the normalized expression values between normalizations for all probe sets
 across 20 randomly selected test arrays.
 Once again, there is a wider distribution of log ratios between RMA-normalized
 values and fRMA-normalized, and a much tighter distribution when comparing
 different fRMA normalizations to each other, indicating that the fRMA training
 process is robust to random batch downsampling for the blood samples as
 well.
\end_layout

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

\begin_layout Standard
\begin_inset ERT
status open

\begin_layout Plain Layout


\backslash
afterpage{
\end_layout

\begin_layout Plain Layout


\backslash
begin{landscape}
\end_layout

\end_inset


\end_layout

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

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

\begin_layout Plain Layout
Fix axis labels: 
\begin_inset Quotes eld
\end_inset

log2 M-value
\begin_inset Quotes erd
\end_inset

 is redundant because M-values are already log scale
\end_layout

\end_inset


\end_layout

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

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename graphics/methylvoom/unadj.dupcor/meanvar-trends-PAGE1-CROP-RASTER.png
	lyxscale 15
	width 30col%
	groupId voomaw-subfig

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout

\series bold
\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

\series bold
\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

\series bold
\begin_inset CommandInset label
LatexCommand label
name "fig:meanvar-sva-voomaw"

\end_inset

Mean-variance trend after voom modeling in analysis C.
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout

\series bold
Mean-variance trend modeling in methylation array data.
 
\series default
The log2(standard deviation) for each probe is plotted against the probe's
 average M-value across all samples as a black point, with some transparency
 to make overplotting more visible, since there are about 450,000 points.
 Density of points is also indicated by the dark blue contour lines.
 The prior variance trend estimated by eBayes is shown in light blue, while
 the lowess trend of the points is shown in red.
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Standard
\begin_inset ERT
status open

\begin_layout Plain Layout


\backslash
end{landscape}
\end_layout

\begin_layout Plain Layout

}
\end_layout

\end_inset


\end_layout

\begin_layout Standard
Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:meanvar-basic"
plural "false"
caps "false"
noprefix "false"

\end_inset

 shows the relationship between the mean M-value and the standard deviation
 calculated for each probe in the methylation array data set.
 A few features of the data are apparent.
 First, the data are very strongly bimodal, with peaks in the density around
 M-values of +4 and -4.
 These modes correspond to methylation sites that are nearly 100% methylated
 and nearly 100% unmethylated, respectively.
 The strong bomodality indicates that a majority of probes interrogate sites
 that fall into one of these two categories.
 The points in between these modes represent sites that are either partially
 methylated in many samples, or are fully methylated in some samples and
 fully unmethylated in other samples, or some combination.
 The next visible feature of the data is the W-shaped variance trend.
 The upticks in the variance trend on either side are expected, based on
 the sigmoid transformation exaggerating small differences at extreme M-values
 (Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:Sigmoid-beta-m-mapping"
plural "false"
caps "false"
noprefix "false"

\end_inset

).
 However, the uptick in the center is interesting: it indicates that sites
 that are not constitutitively methylated or unmethylated have a higher
 variance.
 This could be a genuine biological effect, or it could be spurious noise
 that is only observable at sites with varying methylation.
\end_layout

\begin_layout Standard
In Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:meanvar-sva-aw"
plural "false"
caps "false"
noprefix "false"

\end_inset

, we see the mean-variance trend for the same methylation array data, this
 time with surrogate variables and sample quality weights estimated from
 the data and included in the model.
 As expected, the overall average variance is smaller, since the surrogate
 variables account for some of the variance.
 In addition, the uptick in variance in the middle of the M-value range
 has disappeared, turning the W shape into a wide U shape.
 This indicates that the excess variance in the probes with intermediate
 M-values was explained by systematic variations not correlated with known
 covariates, and these variations were modeled by the surrogate variables.
 The result is a nearly flat variance trend for the entire intermediate
 M-value range from about -3 to +3.
 In contrast, the excess variance at the extremes was not 
\begin_inset Quotes eld
\end_inset

absorbed
\begin_inset Quotes erd
\end_inset

 by the surrogate variables and remains in the plot, indicating that this
 variation has no systematic component: probes with extreme M-values are
 uniformly more variable across all samples, as expected.
 
\end_layout

\begin_layout Standard
Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:meanvar-sva-voomaw"
plural "false"
caps "false"
noprefix "false"

\end_inset

 shows the mean-variance trend after fitting the model with the observation
 weights assigned by voom based on the mean-variance trend shown in Figure
 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:meanvar-sva-aw"
plural "false"
caps "false"
noprefix "false"

\end_inset

.
 As expected, the weights exactly counteract the trend in the data, resulting
 in a nearly flat trend centered vertically at 1 (i.e.
 0 on the log scale).
 This shows that the observations with extreme M-values have been appropriately
 down-weighted to account for the fact that the noise in those observations
 has been amplified by the non-linear M-value transformation.
 In turn, this gives relatively more weight to observervations in the middle
 region, which are more likely to correspond to probes measuring interesting
 biology (not constitutively methylated or unmethylated).
\end_layout

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

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

\begin_layout Plain Layout
Covariate
\end_layout

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

\begin_layout Plain Layout
Test used
\end_layout

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

\begin_layout Plain Layout
p-value
\end_layout

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

\begin_layout Plain Layout
Transplant Status
\end_layout

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

\begin_layout Plain Layout
F-test
\end_layout

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

\begin_layout Plain Layout
0.404
\end_layout

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

\begin_layout Plain Layout
Diabetes Diagnosis
\end_layout

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

\begin_layout Plain Layout
t-test
\end_layout

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

\begin_layout Plain Layout
0.00106
\end_layout

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

\begin_layout Plain Layout
Sex
\end_layout

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

\begin_layout Plain Layout
t-test
\end_layout

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

\begin_layout Plain Layout
0.148
\end_layout

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

\begin_layout Plain Layout
Age
\end_layout

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

\begin_layout Plain Layout
linear regression
\end_layout

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

\begin_layout Plain Layout
0.212
\end_layout

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

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout

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

\end_inset

Association of sample weights with clinical covariates in methylation array
 data.
 
\series default
Computed sample quality log weights were tested for significant association
 with each of the variables in the model (1st column).
 An appropriate test was selected for each variable (2nd column).
 P-values for significant association are shown in the 3rd column.
\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
Redo the sample weight boxplot with notches and without fill colors (and
 update the legend)
\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/unadj.dupcor.sva.voomaw/sample-weights-PAGE3-CROP.pdf
	lyxscale 50
	width 100col%
	groupId colwidth

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout
\begin_inset CommandInset label
LatexCommand label
name "fig:diabetes-sample-weights"

\end_inset


\series bold
Boxplot of sample quality weights grouped by diabetes diagnosis.
 
\series default
Sample were grouped based on diabetes diagnosis, and the distribution of
 sample quality weights for each diagnosis was plotted.
\end_layout

\end_inset


\end_layout

\begin_layout Plain Layout

\end_layout

\end_inset


\end_layout

\begin_layout Standard
To determine whether any of the known experimental factors had an impact
 on data quality, the sample quality weights estimated from the data were
 tested for association with each of the experimental factors (Table 
\begin_inset CommandInset ref
LatexCommand ref
reference "tab:weight-covariate-tests"
plural "false"
caps "false"
noprefix "false"

\end_inset

).
 Diabetes diagnosis was found to have a potentially significant association
 with the sample weights, with a t-test p-value of 
\begin_inset Formula $1.06\times10^{-3}$
\end_inset

.
 Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:diabetes-sample-weights"
plural "false"
caps "false"
noprefix "false"

\end_inset

 shows the distribution of sample weights grouped by diabetes diagnosis.
 The samples from patients with Type 2 diabetes were assigned significantly
 lower weights than those from patients with Type 1 diabetes.
 This indicates that the type 2 diabetes samples had an overall higher variance
 on average across all probes.
 
\end_layout

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

\begin_layout Plain Layout
\align center
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Analysis
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Number of probes significant at 10% FDR.
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10,063
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966
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name "tab:methyl-est-nonnull"

\end_inset

Estimated number of non-null tests, using the method of 
\begin_inset CommandInset citation
LatexCommand cite
key "Phipson2013"
literal "false"

\end_inset

.
\end_layout

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\end_layout

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\begin_layout Plain Layout
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\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 (
\begin_inset CommandInset ref
LatexCommand ref
reference "tab:methyl-num-signif"
plural "false"
caps "false"
noprefix "false"

\end_inset

) and the estimated total number of probes that are differentially methylated
 (
\begin_inset CommandInset ref
LatexCommand ref
reference "tab:methyl-est-nonnull"
plural "false"
caps "false"
noprefix "false"

\end_inset

).
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\end_layout

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	filename graphics/methylvoom/unadj.dupcor/pval-histograms-PAGE1.pdf
	lyxscale 33
	width 30col%
	groupId meth-pval-hist

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\series bold
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AR vs.
 TX, Analysis A
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ADNR vs.
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CAN vs.
 TX, Analysis A
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AR vs.
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ADNR vs.
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CAN vs.
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AR vs.
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CAN vs.
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\begin_layout Plain Layout

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

\end_inset

Probe p-value histograms for each contrast in each analysis.
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Standard
Table 
\begin_inset CommandInset ref
LatexCommand ref
reference "tab:methyl-num-signif"
plural "false"
caps "false"
noprefix "false"

\end_inset

 shows the number of significantly differentially methylated probes reported
 by each analysis for each comparison of interest at an FDR of 10%.
 As expected, the more elaborate analyses, B and C, report more significant
 probes than the more basic analysis A, consistent with the conclusions
 above that the data contain hidden systematic variations that must be modeled.
 Table 
\begin_inset CommandInset ref
LatexCommand ref
reference "tab:methyl-est-nonnull"
plural "false"
caps "false"
noprefix "false"

\end_inset

 shows the estimated number differentially methylated probes for each test
 from each analysis.
 This was computed by estimating the proportion of null hypotheses that
 were true using the method of 
\begin_inset CommandInset citation
LatexCommand cite
key "Phipson2013"
literal "false"

\end_inset

 and subtracting that fraction from the total number of probes, yielding
 an estimate of the number of null hypotheses that are false based on the
 distribution of p-values across the entire dataset.
 Note that this does not identify which null hypotheses should be rejected
 (i.e.
 which probes are significant); it only estimates the true number of such
 probes.
 Once again, analyses B and C result it much larger estimates for the number
 of differentially methylated probes.
 In this case, analysis C, the only analysis that includes voom, estimates
 the largest number of differentially methylated probes for all 3 contrasts.
 If the assumptions of all the methods employed hold, then this represents
 a gain in statistical power over the simpler analysis A.
 Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:meth-p-value-histograms"
plural "false"
caps "false"
noprefix "false"

\end_inset

 shows the p-value distributions for each test, from which the numbers in
 Table 
\begin_inset CommandInset ref
LatexCommand ref
reference "tab:methyl-est-nonnull"
plural "false"
caps "false"
noprefix "false"

\end_inset

 were generated.
 The distributions for analysis A all have a dip in density near zero, which
 is a strong sign of a poor model fit.
 The histograms for analyses B and C are more well-behaved, with a uniform
 component stretching all the way from 0 to 1 representing the probes for
 which the null hypotheses is true (no differential methylation), and a
 zero-biased component representing the probes for which the null hypothesis
 is false (differentially methylated).
 These histograms do not indicate any major issues with the model fit.
\end_layout

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

\begin_layout Plain Layout
Maybe include the PCA plots before/after SVA effect subtraction?
\end_layout

\end_inset


\end_layout

\begin_layout Section
Discussion
\end_layout

\begin_layout Subsection
fRMA achieves clinically applicable normalization without sacrificing classifica
tion performance
\end_layout

\begin_layout Standard
As shown in Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:Classifier-probabilities-RMA"
plural "false"
caps "false"
noprefix "false"

\end_inset

, improper normalization, particularly separate normalization of training
 and test samples, leads to unwanted biases in classification.
 In a controlled experimental context, it is always possible to correct
 this issue by normalizing all experimental samples together.
 However, because it is not feasible to normalize all samples together in
 a clinical context, a single-channel normalization is required is required.
 
\end_layout

\begin_layout Standard
The major concern in using a single-channel normalization is that non-single-cha
nnel methods can share information between arrays to improve the normalization,
 and single-channel methods risk sacrificing the gains in normalization
 accuracy that come from this information sharing.
 In the case of RMA, this information sharing is accomplished through quantile
 normalization and median polish steps.
 The need for information sharing in quantile normalization can easily be
 removed by learning a fixed set of quantiles from external data and normalizing
 each array to these fixed quantiles, instead of the quantiles of the data
 itself.
 As long as the fixed quantiles are reasonable, the result will be similar
 to standard RMA.
 However, there is no analogous way to eliminate cross-array information
 sharing in the median polish step, so fRMA replaces this with a weighted
 average of probes on each array, with the weights learned from external
 data.
 This step of fRMA has the greatest potential to diverge from RMA un undesirable
 ways.
\end_layout

\begin_layout Standard
However, when run on real data, fRMA performed at least as well as RMA in
 both the internal validation and external validation tests.
 This shows that fRMA can be used to normalize individual clinical samples
 in a class prediction context without sacrificing the classifier performance
 that would be obtained by using the more well-established RMA for normalization.
 The other single-channel normalization method considered, SCAN, showed
 some loss of AUC in the external validation test.
 Based on these results, fRMA is the preferred normalization for clinical
 samples in a class prediction context.
\end_layout

\begin_layout Subsection
Robust fRMA vectors can be generated for new array platforms
\end_layout

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

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

850
\begin_inset Quotes erd
\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Standard
The published fRMA normalization vectors for the hgu133plus2 platform were
 generated from a set of about 850 samples chosen from a wide range of tissues,
 which the authors determined was sufficient to generate a robust set of
 normalization vectors that could be applied across all tissues 
\begin_inset CommandInset citation
LatexCommand cite
key "McCall2010"
literal "false"

\end_inset

.
 Since we only had hthgu133pluspm for 2 tissues of interest, our needs were
 more modest.
 Even using only 130 samples in 26 batches of 5 samples each for kidney
 biopsies, we were able to train a robust set of fRMA normalization vectors
 that were not meaningfully affected by the random selection of 5 samples
 from each batch.
 As expected, the training process was just as robust for the blood samples
 with 230 samples in 46 batches of 5 samples each.
 Because these vectors were each generated using training samples from a
 single tissue, they are not suitable for general use, unlike the vectors
 provided with fRMA itself.
 They are purpose-built for normalizing a specific type of sample on a specific
 platform.
 This is a mostly acceptable limitation in the context of developing a machine
 learning classifier for diagnosing a disease based on samples of a specific
 tissue.
\end_layout

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

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

\end_inset


\end_layout

\begin_layout Subsection
Methylation array data can be successfully analyzed using existing techniques,
 but machine learning poses additional challenges
\end_layout

\begin_layout Standard
Both analysis strategies B and C both yield a reasonable analysis, with
 a mean-variance trend that matches the expected behavior for the non-linear
 M-value transformation (Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:meanvar-sva-aw"
plural "false"
caps "false"
noprefix "false"

\end_inset

) and well-behaved p-value distributions (Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:meth-p-value-histograms"
plural "false"
caps "false"
noprefix "false"

\end_inset

).
 These two analyses also yield similar numbers of significant probes (Table
 
\begin_inset CommandInset ref
LatexCommand ref
reference "tab:methyl-num-signif"
plural "false"
caps "false"
noprefix "false"

\end_inset

) and similar estimates of the number of differentially methylated probes
 (Table 
\begin_inset CommandInset ref
LatexCommand ref
reference "tab:methyl-est-nonnull"
plural "false"
caps "false"
noprefix "false"

\end_inset

).
 The main difference between these two analyses is the method used to account
 for the mean-variance trend.
 In analysis B, the trend is estimated and applied at the probe level: each
 probe's estimated variance is squeezed toward the trend using an empirical
 Bayes procedure (Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:meanvar-sva-aw"
plural "false"
caps "false"
noprefix "false"

\end_inset

).
 In analysis C, the trend is still estimated at the probe level, but instead
 of estimating a single variance value shared across all observations for
 a given probe, the voom method computes an initial estiamte of the variance
 for each observation individually based on where its model-fitted M-value
 falls on the trend line and then assigns inverse-variance weights to model
 the difference in variance between observations.
 An overall variance is still estimated for each probe using the same empirical
 Bayes method, but now the residual trend is flat (Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:meanvar-sva-voomaw"
plural "false"
caps "false"
noprefix "false"

\end_inset

), indicating that the mean-variance trend is adequately modeled by scaling
 the estimated variance for each observation using the weights computed
 by voom.
 
\end_layout

\begin_layout Standard
The difference between the standard empirical Bayes trended variance modeling
 (analysis B) and voom (analysis C) is analogous to the difference between
 a t-test with equal variance and a t-test with unequal variance, except
 that the unequal group variances used in the latter test are estimated
 based on the mean-variance trend from all the probes rather than the data
 for the specific probe being tested, thus stabilizing the group variance
 estimates by sharing information between probes.
 Allowing voom to model the variance using observation weights in this manner
 allows the linear model fit to concentrate statistical power where it will
 do the most good.
 For example, if a particular probe's M-values are always at the extreme
 of the M-value range (e.g.
 less than -4) for ADNR samples, but the M-values for that probe in TX and
 CAN samples are within the flat region of the mean-variance trend (between
 -3 and +3), voom is able to down-weight the contribution of the high-variance
 M-values from the ADNR samples in order to gain more statistical power
 while testing for differential methylation between TX and CAN.
 In contrast, modeling the mean-variance trend only at the probe level would
 combine the high-variance ADNR samples and lower-variance samples from
 other conditions and estimate an intermediate variance for this probe.
 In practice, analysis B shows that this approach is adequate, but the voom
 approach in analysis C is at least as good on all model fit criteria and
 yields a larger estimate for the number of differentially methylated genes,
 
\emph on
and
\emph default
 it matches up better with the theoretical 
\end_layout

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

\begin_layout Standard
This preliminary anlaysis suggests that some degree of differential methylation
 exists between TX and each of the three types of transplant disfunction
 studied.
 Hence, it may be feasible to train a classifier to diagnose transplant
 disfunction from DNA methylation array data.
 However, the major importance of both SVA and sample quality weighting
 for proper modeling of this data poses significant challenges for any attempt
 at a machine learning on data of similar quality.
 While these are easily used in a modeling context with full sample information,
 neither of these methods is directly applicable in a machine learning context,
 where the diagnosis is not known ahead of time.
 If a machine learning approach for methylation-based diagnosis is to be
 pursued, it will either require machine-learning-friendly methods to address
 the same systematic trends in the data that SVA and sample quality weighting
 address, or it will require higher quality data with substantially less
 systematic perturbation of the data.
\end_layout

\begin_layout Chapter
Globin-blocking for more effective blood RNA-seq analysis in primate animal
 model
\end_layout

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

\begin_layout Plain Layout
Choose between above and the paper title: Optimizing yield of deep RNA sequencin
g for gene expression profiling by globin reduction of peripheral blood
 samples from cynomolgus monkeys (Macaca fascicularis).
\end_layout

\end_inset


\end_layout

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

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

\end_inset


\end_layout

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

\begin_layout Plain Layout
Preprint then cite the paper
\end_layout

\end_inset


\end_layout

\begin_layout Section*
Abstract
\end_layout

\begin_layout Paragraph
Background
\end_layout

\begin_layout Standard
Primate blood contains high concentrations of globin messenger RNA.
 Globin reduction is a standard technique used to improve the expression
 results obtained by DNA microarrays on RNA from blood samples.
 However, with whole transcriptome RNA-sequencing (RNA-seq) quickly replacing
 microarrays for many applications, the impact of globin reduction for RNA-seq
 has not been previously studied.
 Moreover, no off-the-shelf kits are available for globin reduction in nonhuman
 primates.
 
\end_layout

\begin_layout Paragraph
Results
\end_layout

\begin_layout Standard
Here we report a protocol for RNA-seq in primate blood samples that uses
 complimentary oligonucleotides to block reverse transcription of the alpha
 and beta globin genes.
 In test samples from cynomolgus monkeys (Macaca fascicularis), this globin
 blocking protocol approximately doubles the yield of informative (non-globin)
 reads by greatly reducing the fraction of globin reads, while also improving
 the consistency in sequencing depth between samples.
 The increased yield enables detection of about 2000 more genes, significantly
 increases the correlation in measured gene expression levels between samples,
 and increases the sensitivity of differential gene expression tests.
\end_layout

\begin_layout Paragraph
Conclusions
\end_layout

\begin_layout Standard
These results show that globin blocking significantly improves the cost-effectiv
eness of mRNA sequencing in primate blood samples by doubling the yield
 of useful reads, allowing detection of more genes, and improving the precision
 of gene expression measurements.
 Based on these results, a globin reducing or blocking protocol is recommended
 for all RNA-seq studies of primate blood samples.
\end_layout

\begin_layout Section
Approach
\end_layout

\begin_layout Standard
\begin_inset Note Note
status open

\begin_layout Plain Layout
Consider putting some of this in the Intro chapter
\end_layout

\begin_layout Itemize
Cynomolgus monkeys as a model organism
\end_layout

\begin_deeper
\begin_layout Itemize
Highly related to humans
\end_layout

\begin_layout Itemize
Small size and short life cycle - good research animal
\end_layout

\begin_layout Itemize
Genomics resources still in development
\end_layout

\end_deeper
\begin_layout Itemize
Inadequacy of existing blood RNA-seq protocols
\end_layout

\begin_deeper
\begin_layout Itemize
Existing protocols use a separate globin pulldown step, slowing down processing
\end_layout

\end_deeper
\end_inset


\end_layout

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

\end_inset

.
 The advantages are even greater for study of model organisms with no well-estab
lished array platforms available, such as the cynomolgus monkey (Macaca
 fascicularis).
 High fractions of globin mRNA are naturally present in mammalian peripheral
 blood samples (up to 70% of total mRNA) and these are known to interfere
 with the results of array-based expression profiling 
\begin_inset CommandInset citation
LatexCommand cite
key "Winn2010"
literal "false"

\end_inset

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

\end_inset

.
 In the present report, we evaluated globin reduction using custom blocking
 oligonucleotides for deep RNA-seq of peripheral blood samples from a nonhuman
 primate, cynomolgus monkey, using the Illumina technology platform.
 We demonstrate that globin reduction significantly improves the cost-effectiven
ess of RNA-seq in blood samples.
 Thus, our protocol offers a significant advantage to any investigator planning
 to use RNA-seq for gene expression profiling of nonhuman primate blood
 samples.
 Our method can be generally applied to any species by designing complementary
 oligonucleotide blocking probes to the globin gene sequences of that species.
 Indeed, any highly expressed but biologically uninformative transcripts
 can also be blocked to further increase sequencing efficiency and value
 
\begin_inset CommandInset citation
LatexCommand cite
key "Arnaud2016"
literal "false"

\end_inset

.
\end_layout

\begin_layout Section
Methods
\end_layout

\begin_layout Subsection
Sample collection
\end_layout

\begin_layout Standard
All research reported here was done under IACUC-approved protocols at the
 University of Miami and complied with all applicable federal and state
 regulations and ethical principles for nonhuman primate research.
 Blood draws occurred between 16 April 2012 and 18 June 2015.
 The experimental system involved intrahepatic pancreatic islet transplantation
 into Cynomolgus monkeys with induced diabetes mellitus with or without
 concomitant infusion of mesenchymal stem cells.
 Blood was collected at serial time points before and after transplantation
 into PAXgene Blood RNA tubes (PreAnalytiX/Qiagen, Valencia, CA) at the
 precise volume:volume ratio of 2.5 ml whole blood into 6.9 ml of PAX gene
 additive.
\end_layout

\begin_layout Subsection
Globin Blocking
\end_layout

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

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

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

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

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

\begin_layout Subsection
RNA-seq Library Preparation 
\end_layout

\begin_layout Standard
Sequencing libraries were prepared with 200ng total RNA from each sample.
 Polyadenylated mRNA was selected from 200 ng aliquots of cynomologus blood-deri
ved total RNA using Ambion Dynabeads Oligo(dT)25 beads (Invitrogen) following
 manufacturer’s recommended protocol.
 PolyA selected RNA was then combined with 8 pmol of HBA1/2 (site 1), 8
 pmol of HBA1/2 (site 2), 12 pmol of HBB (site 1) and 12 pmol of HBB (site
 2) oligonucleotides.
 In addition, 20 pmol of RT primer containing a portion of the Illumina
 adapter sequence (B-oligo-dTV: GAGTTCCTTGGCACCCGAGAATTCCATTTTTTTTTTTTTTTTTTTV)
 and 4 µL of 5X First Strand buffer (250 mM Tris-HCl pH 8.3, 375 mM KCl,
 15mM MgCl2) were added in a total volume of 15 µL.
 The RNA was fragmented by heating this cocktail for 3 minutes at 95°C and
 then placed on ice.
 This was followed by the addition of 2 µL 0.1 M DTT, 1 µL RNaseOUT, 1 µL
 10mM dNTPs 10% biotin-16 aminoallyl-2’- dUTP and 10% biotin-16 aminoallyl-2’-
 dCTP (TriLink Biotech, San Diego, CA), 1 µL Superscript II (200U/ µL, Thermo-Fi
sher).
 A second “unblocked” library was prepared in the same way for each sample
 but replacing the blocking oligos with an equivalent volume of water.
 The reaction was carried out at 25°C for 15 minutes and 42°C for 40 minutes,
 followed by incubation at 75°C for 10 minutes to inactivate the reverse
 transcriptase.
\end_layout

\begin_layout Standard
The cDNA/RNA hybrid molecules were purified using 1.8X Ampure XP beads (Agencourt
) following supplier’s recommended protocol.
 The cDNA/RNA hybrid was eluted in 25 µL of 10 mM Tris-HCl pH 8.0, and then
 bound to 25 µL of M280 Magnetic Streptavidin beads washed per recommended
 protocol (Thermo-Fisher).
 After 30 minutes of binding, beads were washed one time in 100 µL 0.1N NaOH
 to denature and remove the bound RNA, followed by two 100 µL washes with
 1X TE buffer.
\end_layout

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

\begin_layout Standard
The magnetic streptavidin beads were resuspended in 34 µL nuclease-free
 water and added directly to a PCR tube.
 The two Illumina protocol-specified PCR primers were added at 0.53 µM (Illumina
 TruSeq Universal Primer 1 and Illumina TruSeq barcoded PCR primer 2), along
 with 40 µL 2X KAPA HiFi Hotstart ReadyMix (KAPA, Willmington MA) and thermocycl
ed as follows: starting with 98°C (2 min-hold); 15 cycles of 98°C, 20sec;
 60°C, 30sec; 72°C, 30sec; and finished with a 72°C (2 min-hold).
\end_layout

\begin_layout Standard
PCR products were purified with 1X Ampure Beads following manufacturer’s
 recommended protocol.
 Libraries were then analyzed using the Agilent TapeStation and quantitation
 of desired size range was performed by “smear analysis”.
 Samples were pooled in equimolar batches of 16 samples.
 Pooled libraries were size selected on 2% agarose gels (E-Gel EX Agarose
 Gels; Thermo-Fisher).
 Products were cut between 250 and 350 bp (corresponding to insert sizes
 of 130 to 230 bps).
 Finished library pools were then sequenced on the Illumina NextSeq500 instrumen
t with 75 base read lengths.
 
\end_layout

\begin_layout Subsection
Read alignment and counting
\end_layout

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

\end_inset

.
 Counts of uniquely mapped reads were obtained for every gene in each sample
 with the “featureCounts” function from the Rsubread package, using each
 of the three possibilities for the “strandSpecific” option: sense, antisense,
 and unstranded 
\begin_inset CommandInset citation
LatexCommand cite
key "Liao2014"
literal "false"

\end_inset

.
 A few artifacts in the cynomolgus genome annotation complicated read counting.
 First, no ortholog is annotated for alpha globin in the cynomolgus genome,
 presumably because the human genome has two alpha globin genes with nearly
 identical sequences, making the orthology relationship ambiguous.
 However, two loci in the cynomolgus genome are as “hemoglobin subunit alpha-lik
e” (LOC102136192 and LOC102136846).
 LOC102136192 is annotated as a pseudogene while LOC102136846 is annotated
 as protein-coding.
 Our globin reduction protocol was designed to include blocking of these
 two genes.
 Indeed, these two genes have almost the same read counts in each library
 as the properly-annotated HBB gene and much larger counts than any other
 gene in the unblocked libraries, giving confidence that reads derived from
 the real alpha globin are mapping to both genes.
 Thus, reads from both of these loci were counted as alpha globin reads
 in all further analyses.
 The second artifact is a small, uncharacterized non-coding RNA gene (LOC1021365
91), which overlaps the HBA-like gene (LOC102136192) on the opposite strand.
 If counting is not performed in stranded mode (or if a non-strand-specific
 sequencing protocol is used), many reads mapping to the globin gene will
 be discarded as ambiguous due to their overlap with this ncRNA gene, resulting
 in significant undercounting of globin reads.
 Therefore, stranded sense counts were used for all further analysis in
 the present study to insure that we accurately accounted for globin transcript
 reduction.
 However, we note that stranded reads are not necessary for RNA-seq using
 our protocol in standard practice.
 
\end_layout

\begin_layout Subsection
Normalization and Exploratory Data Analysis
\end_layout

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

\end_inset

.
 Log2 counts per million values (logCPM) were calculated using the cpm function
 in edgeR for individual samples and aveLogCPM function for averages across
 groups of samples, using those functions’ default prior count values to
 avoid taking the logarithm of 0.
 Genes were considered “present” if their average normalized logCPM values
 across all libraries were at least -1.
 Normalizing for gene length was unnecessary because the sequencing protocol
 is 3’-biased and hence the expected read count for each gene is related
 to the transcript’s copy number but not its length.
\end_layout

\begin_layout Standard
In order to assess the effect of blocking on reproducibility, Pearson and
 Spearman correlation coefficients were computed between the logCPM values
 for every pair of libraries within the globin-blocked (GB) and unblocked
 (non-GB) groups, and edgeR's “estimateDisp” function was used to compute
 negative binomial dispersions separately for the two groups 
\begin_inset CommandInset citation
LatexCommand cite
key "Chen2014"
literal "false"

\end_inset

.
\end_layout

\begin_layout Subsection
Differential Expression Analysis
\end_layout

\begin_layout Standard
All tests for differential gene expression were performed using edgeR, by
 first fitting a negative binomial generalized linear model to the counts
 and normalization factors and then performing a quasi-likelihood F-test
 with robust estimation of outlier gene dispersions 
\begin_inset CommandInset citation
LatexCommand cite
key "Lund2012,Phipson2016"
literal "false"

\end_inset

.
 To investigate the effects of globin blocking on each gene, an additive
 model was fit to the full data with coefficients for globin blocking and
 SampleID.
 To test the effect of globin blocking on detection of differentially expressed
 genes, the GB samples and non-GB samples were each analyzed independently
 as follows: for each animal with both a pre-transplant and a post-transplant
 time point in the data set, the pre-transplant sample and the earliest
 post-transplant sample were selected, and all others were excluded, yielding
 a pre-/post-transplant pair of samples for each animal (N=7 animals with
 paired samples).
 These samples were analyzed for pre-transplant vs.
 post-transplant differential gene expression while controlling for inter-animal
 variation using an additive model with coefficients for transplant and
 animal ID.
 In all analyses, p-values were adjusted using the Benjamini-Hochberg procedure
 for FDR control 
\begin_inset CommandInset citation
LatexCommand cite
key "Benjamini1995"
literal "false"

\end_inset

.
\end_layout

\begin_layout Standard
\begin_inset Note Note
status open

\begin_layout Itemize
New blood RNA-seq protocol to block reverse transcription of globin genes
\end_layout

\begin_layout Itemize
Blood RNA-seq time course after transplants with/without MSC infusion
\end_layout

\end_inset


\end_layout

\begin_layout Section
Results
\end_layout

\begin_layout Subsection
Globin blocking yields a larger and more consistent fraction of useful reads
\end_layout

\begin_layout Standard
\begin_inset ERT
status open

\begin_layout Plain Layout


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\align center
\begin_inset Tabular
<lyxtabular version="3" rows="4" columns="7">
<features tabularvalignment="middle">
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<column alignment="center" valignment="top">
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<column alignment="center" valignment="top">
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\begin_layout Plain Layout

\end_layout

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Percent of Total Reads
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<cell multicolumn="2" alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
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Percent of Genic Reads
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</cell>
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</cell>
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GB
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" usebox="none">
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Non-globin Reads
\end_layout

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</cell>
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\begin_inset Text

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Globin Reads
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</cell>
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All Genic Reads
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</cell>
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All Aligned Reads
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Non-globin Reads
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Globin Reads
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<row>
<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
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Yes
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</cell>
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50.4% ± 6.82
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\end_inset
</cell>
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3.48% ± 2.94
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\end_inset
</cell>
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53.9% ± 6.81
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\end_inset
</cell>
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89.7% ± 2.40
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\end_inset
</cell>
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93.5% ± 5.25
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</cell>
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6.49% ± 5.25
\end_layout

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

\begin_layout Plain Layout

\family roman
\series medium
\shape up
\size normal
\emph off
\bar no
\strikeout off
\xout off
\uuline off
\uwave off
\noun off
\color none
No
\end_layout

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

\begin_layout Plain Layout

\family roman
\series medium
\shape up
\size normal
\emph off
\bar no
\strikeout off
\xout off
\uuline off
\uwave off
\noun off
\color none
26.3% ± 8.95
\end_layout

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

\begin_layout Plain Layout

\family roman
\series medium
\shape up
\size normal
\emph off
\bar no
\strikeout off
\xout off
\uuline off
\uwave off
\noun off
\color none
44.6% ± 16.6
\end_layout

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

\begin_layout Plain Layout

\family roman
\series medium
\shape up
\size normal
\emph off
\bar no
\strikeout off
\xout off
\uuline off
\uwave off
\noun off
\color none
70.1% ± 9.38
\end_layout

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

\begin_layout Plain Layout

\family roman
\series medium
\shape up
\size normal
\emph off
\bar no
\strikeout off
\xout off
\uuline off
\uwave off
\noun off
\color none
90.7% ± 5.16
\end_layout

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

\begin_layout Plain Layout

\family roman
\series medium
\shape up
\size normal
\emph off
\bar no
\strikeout off
\xout off
\uuline off
\uwave off
\noun off
\color none
38.8% ± 17.1
\end_layout

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

\begin_layout Plain Layout

\family roman
\series medium
\shape up
\size normal
\emph off
\bar no
\strikeout off
\xout off
\uuline off
\uwave off
\noun off
\color none
61.2% ± 17.1
\end_layout

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

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout

\series bold
\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

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
The objective of the present study was to validate a new protocol for deep
 RNA-seq of whole blood drawn into PaxGene tubes from cynomolgus monkeys
 undergoing islet transplantation, with particular focus on minimizing the
 loss of useful sequencing space to uninformative globin reads.
 The details of the analysis with respect to transplant outcomes and the
 impact of mesenchymal stem cell treatment will be reported in a separate
 manuscript (in preparation).
 To focus on the efficacy of our globin blocking protocol, 37 blood samples,
 16 from pre-transplant and 21 from post-transplant time points, were each
 prepped once with and once without globin blocking oligos, and were then
 sequenced on an Illumina NextSeq500 instrument.
 The number of reads aligning to each gene in the cynomolgus genome was
 counted.
 Table 1 summarizes the distribution of read fractions among the GB and
 non-GB libraries.
 In the libraries with no globin blocking, globin reads made up an average
 of 44.6% of total input reads, while reads assigned to all other genes made
 up an average of 26.3%.
 The remaining reads either aligned to intergenic regions (that include
 long non-coding RNAs) or did not align with any annotated transcripts in
 the current build of the cynomolgus genome.
 In the GB libraries, globin reads made up only 3.48% and reads assigned
 to all other genes increased to 50.4%.
 Thus, globin blocking resulted in a 92.2% reduction in globin reads and
 a 91.6% increase in yield of useful non-globin reads.
\end_layout

\begin_layout Standard
This reduction is not quite as efficient as the previous analysis showed
 for human samples by DeepSAGE (<0.4% globin reads after globin reduction)
 
\begin_inset CommandInset citation
LatexCommand cite
key "Mastrokolias2012"
literal "false"

\end_inset

.
 Nonetheless, this degree of globin reduction is sufficient to nearly double
 the yield of useful reads.
 Thus, globin blocking cuts the required sequencing effort (and costs) to
 achieve a target coverage depth by almost 50%.
 Consistent with this near doubling of yield, the average difference in
 un-normalized logCPM across all genes between the GB libraries and non-GB
 libraries is approximately 1 (mean = 1.01, median = 1.08), an overall 2-fold
 increase.
 Un-normalized values are used here because the TMM normalization correctly
 identifies this 2-fold difference as biologically irrelevant and removes
 it.
\end_layout

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

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

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout

\series bold
\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 globin blocking (GB).
\end_layout

\end_inset


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

\end_inset

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

\series default
 All reads in each sequencing library were aligned to the cyno genome, and
 the number of reads uniquely aligning to each gene was counted.
 For each sample, counts were summed separately for all globin genes and
 for the remainder of the genes (non-globin genes), and the fraction of
 genic reads aligned to non-globin genes was computed.
 Each point represents an individual sample.
 Gray + signs indicate the means for globin-blocked libraries and unblocked
 libraries.
 The overall distribution for each group is represented as a notched box
 plots.
 Points are randomly spread vertically to avoid excessive overlapping.
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Standard
Another important aspect is that the standard deviations in Table 
\begin_inset CommandInset ref
LatexCommand ref
reference "tab:Fractions-of-reads"
plural "false"
caps "false"
noprefix "false"

\end_inset

 are uniformly smaller in the GB samples than the non-GB ones, indicating
 much greater consistency of yield.
 This is best seen in the percentage of non-globin reads as a fraction of
 total reads aligned to annotated genes (genic reads).
 For the non-GB samples, this measure ranges from 10.9% to 80.9%, while for
 the GB samples it ranges from 81.9% to 99.9% (Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:Fraction-of-genic-reads"
plural "false"
caps "false"
noprefix "false"

\end_inset

).
 This means that for applications where it is critical that each sample
 achieve a specified minimum coverage in order to provide useful information,
 it would be necessary to budget up to 10 times the sequencing depth per
 sample without globin blocking, even though the average yield improvement
 for globin blocking is only 2-fold, because every sample has a chance of
 being 90% globin and 10% useful reads.
 Hence, the more consistent behavior of GB samples makes planning an experiment
 easier and more efficient because it eliminates the need to over-sequence
 every sample in order to guard against the worst case of a high-globin
 fraction.
\end_layout

\begin_layout Subsection
Globin blocking lowers the noise floor and allows detection of about 2000
 more 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
\begin_inset Float figure
wide false
sideways false
status collapsed

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

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout

\series bold
\begin_inset Argument 1
status collapsed

\begin_layout Plain Layout
Distributions of average group gene abundances when normalized separately
 or together.
\end_layout

\end_inset


\begin_inset CommandInset label
LatexCommand label
name "fig:logcpm-dists"

\end_inset

Distributions of average group gene abundances when normalized separately
 or together.

\series default
 All reads in each sequencing library were aligned to the cyno genome, and
 the number of reads uniquely aligning to each gene was counted.
 Genes with zero counts in all libraries were discarded.
 Libraries were normalized using the TMM method.
 Libraries were split into globin-blocked (GB) and non-GB groups and the
 average abundance for each gene in both groups, measured in log2 counts
 per million reads counted, was computed using the aveLogCPM function.
 The distribution of average gene logCPM values was plotted for both groups
 using a kernel density plot to approximate a continuous distribution.
 The logCPM GB distributions are marked in red, non-GB in blue.
 The black vertical line denotes the chosen detection threshold of -1.
 Top panel: Libraries were split into GB and non-GB groups first and normalized
 separately.
 Bottom panel: Libraries were all normalized together first and then split
 into groups.
\end_layout

\end_inset


\end_layout

\begin_layout Plain Layout

\end_layout

\end_inset


\end_layout

\begin_layout Standard
Since globin blocking yields more usable sequencing depth, it should also
 allow detection of more genes at any given threshold.
 When we looked at the distribution of average normalized logCPM values
 across all libraries for genes with at least one read assigned to them,
 we observed the expected bimodal distribution, with a high-abundance "signal"
 peak representing detected genes and a low-abundance "noise" peak representing
 genes whose read count did not rise above the noise floor (Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:logcpm-dists"
plural "false"
caps "false"
noprefix "false"

\end_inset

).
 Consistent with the 2-fold increase in raw counts assigned to non-globin
 genes, the signal peak for GB samples is shifted to the right relative
 to the non-GB signal peak.
 When all the samples are normalized together, this difference is normalized
 out, lining up the signal peaks, and this reveals that, as expected, the
 noise floor for the GB samples is about 2-fold lower.
 This greater separation between signal and noise peaks in the GB samples
 means that low-expression genes should be more easily detected and more
 precisely quantified than in the non-GB samples.
\end_layout

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

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

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout

\series bold
\begin_inset Argument 1
status collapsed

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

\end_inset


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

\end_inset

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

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

 counts per million reads counted) was computed by separate group normalization
 as described in Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:logcpm-dists"
plural "false"
caps "false"
noprefix "false"

\end_inset

 for both the GB and non-GB groups, as well as for all samples considered
 as one large group.
 For each every integer threshold from -2 to 3, the number of genes detected
 at or above that logCPM threshold was plotted for each group.
\end_layout

\end_inset


\end_layout

\begin_layout Plain Layout

\end_layout

\end_inset


\end_layout

\begin_layout Standard
Based on these distributions, we selected a detection threshold of -1, which
 is approximately the leftmost edge of the trough between the signal and
 noise peaks.
 This represents the most liberal possible detection threshold that doesn't
 call substantial numbers of noise genes as detected.
 Among the full dataset, 13429 genes were detected at this threshold, and
 22276 were not.
 When considering the GB libraries and non-GB libraries separately and re-comput
ing normalization factors independently within each group, 14535 genes were
 detected in the GB libraries while only 12460 were detected in the non-GB
 libraries.
 Thus, GB allowed the detection of 2000 extra genes that were buried under
 the noise floor without GB.
 This pattern of at least 2000 additional genes detected with GB was also
 consistent across a wide range of possible detection thresholds, from -2
 to 3 (see Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:Gene-detections"
plural "false"
caps "false"
noprefix "false"

\end_inset

).
\end_layout

\begin_layout Subsection
Globin blocking does not add significant additional noise or decrease sample
 quality
\end_layout

\begin_layout Standard
One potential worry is that the globin blocking protocol could perturb the
 levels of non-globin genes.
 There are two kinds of possible perturbations: systematic and random.
 The former is not a major concern for detection of differential expression,
 since a 2-fold change in every sample has no effect on the relative fold
 change between samples.
 In contrast, random perturbations would increase the noise and obscure
 the signal in the dataset, reducing the capacity to detect differential
 expression.
\end_layout

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

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename graphics/Globin Paper/figure4 - maplot-colored.pdf
	lyxscale 50
	width 100col%
	groupId colwidth

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout
\begin_inset Argument 1
status collapsed

\begin_layout Plain Layout
MA plot showing effects of globin blocking on each gene's abundance.
\end_layout

\end_inset


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

\end_inset


\series bold
MA plot showing effects of globin blocking on each gene's abundance.
 
\series default
All libraries were normalized together as described in Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:logcpm-dists"
plural "false"
caps "false"
noprefix "false"

\end_inset

, and genes with an average logCPM below -1 were filtered out.
 Each remaining gene was tested for differential abundance with respect
 to globin blocking (GB) using edgeR’s quasi-likelihod F-test, fitting a
 negative binomial generalized linear model to table of read counts in each
 library.
 For each gene, edgeR reported average abundance (logCPM), 
\begin_inset Formula $\log_{2}$
\end_inset

 fold change (logFC), p-value, and Benjamini-Hochberg adjusted false discovery
 rate (FDR).
 Each gene's logFC was plotted against its logCPM, colored by FDR.
 Red points are significant at ≤10% FDR, and blue are not significant at
 that threshold.
 The alpha and beta globin genes targeted for blocking are marked with large
 triangles, while all other genes are represented as small points.
\end_layout

\end_inset


\end_layout

\begin_layout Plain Layout

\end_layout

\end_inset


\end_layout

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

\begin_layout Plain Layout
Standardize on 
\begin_inset Quotes eld
\end_inset

log2
\begin_inset Quotes erd
\end_inset

 notation
\end_layout

\end_inset


\end_layout

\begin_layout Standard
The data do indeed show small systematic perturbations in gene levels (Figure
 
\begin_inset CommandInset ref
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 log fold changes: HBD and
 LOC1021365.
 HBD, delta globin, is most likely targeted by the blocking oligos due to
 high sequence homology with the other globin genes.
 LOC1021365 is the aforementioned ncRNA that is reverse-complementary to
 one of the alpha-like genes and that would be expected to be removed during
 the globin blocking step.
 All other genes appear in a cluster centered vertically at 0, and the vast
 majority of genes in this cluster show an absolute log2(FC) of 0.5 or less.
 Nevertheless, many of these small perturbations are still statistically
 significant, indicating that the globin blocking oligos likely cause very
 small but non-zero systematic perturbations in measured gene expression
 levels.
\end_layout

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

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename graphics/Globin Paper/figure5 - corrplot.pdf
	lyxscale 50
	width 70col%

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout

\series bold
\begin_inset Argument 1
status collapsed

\begin_layout Plain Layout
Comparison of inter-sample gene abundance correlations with and without
 globin blocking.
\end_layout

\end_inset


\begin_inset CommandInset label
LatexCommand label
name "fig:gene-abundance-correlations"

\end_inset

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

\series default
 All libraries were normalized together as described in Figure 2, and genes
 with an average abundance (logCPM, log2 counts per million reads counted)
 less than -1 were filtered out.
 Each gene’s logCPM was computed in each library using the edgeR cpm function.
 For each pair of biological samples, the Pearson correlation between those
 samples' GB libraries was plotted against the correlation between the same
 samples’ non-GB libraries.
 Each point represents an unique pair of samples.
 The solid gray line shows a quantile-quantile plot of distribution of GB
 correlations vs.
 that of non-GB correlations.
 The thin dashed line is the identity line, provided for reference.
\end_layout

\end_inset


\end_layout

\begin_layout Plain Layout

\end_layout

\end_inset


\end_layout

\begin_layout Standard
To evaluate the possibility of globin blocking causing random perturbations
 and reducing sample quality, we computed the Pearson correlation between
 logCPM values for every pair of samples with and without GB and plotted
 them against each other (Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:gene-abundance-correlations"
plural "false"
caps "false"
noprefix "false"

\end_inset

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

\end_inset

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

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

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

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

\begin_layout Plain Layout

\end_layout

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

\begin_layout Plain Layout

\end_layout

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

\begin_layout Plain Layout

\series bold
No Globin Blocking
\end_layout

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

\begin_layout Plain Layout

\end_layout

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

\begin_layout Plain Layout

\end_layout

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

\begin_layout Plain Layout

\end_layout

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

\begin_layout Plain Layout

\end_layout

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

\begin_layout Plain Layout

\series bold
Up
\end_layout

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

\begin_layout Plain Layout

\series bold
NS
\end_layout

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

\begin_layout Plain Layout

\series bold
Down
\end_layout

\end_inset
</cell>
</row>
<row>
<cell multirow="3" alignment="center" valignment="middle" topline="true" bottomline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout

\series bold
Globin-Blocking
\end_layout

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

\begin_layout Plain Layout

\series bold
Up
\end_layout

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

\begin_layout Plain Layout

\family roman
\series medium
\shape up
\size normal
\emph off
\bar no
\strikeout off
\xout off
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\noun off
\color none
231
\end_layout

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

\begin_layout Plain Layout

\family roman
\series medium
\shape up
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\emph off
\bar no
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\color none
515
\end_layout

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

\begin_layout Plain Layout

\family roman
\series medium
\shape up
\size normal
\emph off
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\color none
2
\end_layout

\end_inset
</cell>
</row>
<row>
<cell multirow="4" alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout

\end_layout

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

\begin_layout Plain Layout

\series bold
NS
\end_layout

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

\begin_layout Plain Layout

\family roman
\series medium
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\color none
160
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</cell>
<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout

\family roman
\series medium
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\color none
11235
\end_layout

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status open

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Comparison of significantly differentially expressed genes with and without
 globin blocking.
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LatexCommand label
name "tab:Comparison-of-significant"

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Comparison of significantly differentially expressed genes with and without
 globin blocking.

\series default
 Up, Down: Genes significantly up/down-regulated in post-transplant samples
 relative to pre-transplant samples, with a false discovery rate of 10%
 or less.
 NS: Non-significant genes (false discovery rate greater than 10%).
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To compare performance on differential gene expression tests, we took subsets
 of both the GB and non-GB libraries with exactly one pre-transplant and
 one post-transplant sample for each animal that had paired samples available
 for analysis (N=7 animals, N=14 samples in each subset).
 The same test for pre- vs.
 post-transplant differential gene expression was performed on the same
 7 pairs of samples from GB libraries and non-GB libraries, in each case
 using an FDR of 10% as the threshold of significance.
 Out of 12954 genes that passed the detection threshold in both subsets,
 358 were called significantly differentially expressed in the same direction
 in both sets; 1063 were differentially expressed in the GB set only; 296
 were differentially expressed in the non-GB set only; 2 genes were called
 significantly up in the GB set but significantly down in the non-GB set;
 and the remaining 11235 were not called differentially expressed in either
 set.
 These data are summarized in Table 
\begin_inset CommandInset ref
LatexCommand ref
reference "tab:Comparison-of-significant"
plural "false"
caps "false"
noprefix "false"

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

\begin_layout Standard
The key point is that the GB data results in substantially more differentially
 expressed calls than the non-GB data.
 Since there is no gold standard for this dataset, it is impossible to be
 certain whether this is due to under-calling of differential expression
 in the non-GB samples or over-calling in the GB samples.
 However, given that both datasets are derived from the same biological
 samples and have nearly equal BCVs, it is more likely that the larger number
 of DE calls in the GB samples are genuine detections that were enabled
 by the higher sequencing depth and measurement precision of the GB samples.
 Note that the same set of genes was considered in both subsets, so the
 larger number of differentially expressed gene calls in the GB data set
 reflects a greater sensitivity to detect significant differential gene
 expression and not simply the larger total number of detected genes in
 GB samples described earlier.
\end_layout

\begin_layout Section
Discussion
\end_layout

\begin_layout Standard
The original experience with whole blood gene expression profiling on DNA
 microarrays demonstrated that the high concentration of globin transcripts
 reduced the sensitivity to detect genes with relatively low expression
 levels, in effect, significantly reducing the sensitivity.
 To address this limitation, commercial protocols for globin reduction were
 developed based on strategies to block globin transcript amplification
 during labeling or physically removing globin transcripts by affinity bead
 methods 
\begin_inset CommandInset citation
LatexCommand cite
key "Winn2010"
literal "false"

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.
 More recently, using the latest generation of labeling protocols and arrays,
 it was determined that globin reduction was no longer necessary to obtain
 sufficient sensitivity to detect differential transcript expression 
\begin_inset CommandInset citation
LatexCommand cite
key "NuGEN2010"
literal "false"

\end_inset

.
 However, we are not aware of any publications using these currently available
 protocols the with latest generation of microarrays that actually compare
 the detection sensitivity with and without globin reduction.
 However, in practice this has now been adopted generally primarily driven
 by concerns for cost control.
 The main objective of our work was to directly test the impact of globin
 gene transcripts and a new globin blocking protocol for application to
 the newest generation of differential gene expression profiling determined
 using next generation sequencing.
 
\end_layout

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The challenge of doing global gene expression profiling in cynomolgus monkeys
 is that the current available arrays were never designed to comprehensively
 cover this genome and have not been updated since the first assemblies
 of the cynomolgus genome were published.
 Therefore, we determined that the best strategy for peripheral blood profiling
 was to do deep RNA-seq and inform the workflow using the latest available
 genome assembly and annotation 
\begin_inset CommandInset citation
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key "Wilson2013"
literal "false"

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.
 However, it was not immediately clear whether globin reduction was necessary
 for RNA-seq or how much improvement in efficiency or sensitivity to detect
 differential gene expression would be achieved for the added cost and work.
 
\end_layout

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

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.
 The approach to DeepSAGE involves two different restriction enzymes that
 purify and then tag small fragments of transcripts at specific locations
 and thus, significantly reduces the complexity of the transcriptome.
 Therefore, we could not determine how DeepSAGE results would translate
 to the common strategy in the field for assaying the entire transcript
 population by whole-transcriptome 3’-end RNA-seq.
 Furthermore, if globin reduction is necessary, we also needed a globin
 reduction method specific to cynomolgus globin sequences that would work
 an organism for which no kit is available off the shelf.
\end_layout

\begin_layout Standard
As mentioned above, the addition of globin blocking oligos has a very small
 impact on measured expression levels of gene expression.
 However, this is a non-issue for the purposes of differential expression
 testing, since a systematic change in a gene in all samples does not affect
 relative expression levels between samples.
 However, we must acknowledge that simple comparisons of gene expression
 data obtained by GB and non-GB protocols are not possible without additional
 normalization.
 
\end_layout

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

\begin_layout Chapter
Future Directions
\end_layout

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Consider per-chapter future directions.
 Check instructions.
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\begin_layout Itemize
Functional validation of effective promoter radius
\end_layout

\begin_layout Itemize
N-to-M convergence deserves further stufy of some kind
\end_layout

\begin_layout Itemize
Promoter positional coverage: follow up on hints of interesting patterns
\end_layout

\begin_layout Itemize
Study other epigenetic marks in more contexts
\end_layout

\begin_deeper
\begin_layout Itemize
DNA methylation, histone marks, chromatin accessibility & conformation in
 CD4 T-cells
\end_layout

\begin_layout Itemize
Also look at other types of lymphocytes: CD8 T-cells, B-cells, NK cells
\end_layout

\end_deeper
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Use CV or bootstrap to better evaluate classifiers
\end_layout

\begin_layout Itemize
fRMAtools could be adapted to not require equal-sized groups
\end_layout

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 Probably don't just want [1], [2], etc.
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\end_body
\end_document