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
\end_layout

\begin_layout Author
A thesis presented 
\begin_inset Newline newline
\end_inset

by
\begin_inset Newline newline
\end_inset

 Ryan C.
 Thompson
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\end_inset

 to
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\end_inset

 The Scripps Research Institute Graduate Program 
\begin_inset Newline newline
\end_inset

in partial fulfillment of the requirements for the degree of
\begin_inset Newline newline
\end_inset

 Doctor of Philosophy in the subject of Biology
\begin_inset Newline newline
\end_inset

 for
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\end_inset

 The Scripps Research Institute
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\end_inset

 La Jolla, California
\end_layout

\begin_layout Date
May 2019
\end_layout

\begin_layout Standard
[Copyright notice]
\end_layout

\begin_layout Standard
[Thesis acceptance form]
\end_layout

\begin_layout Standard
[Dedication]
\end_layout

\begin_layout Standard
[Acknowledgements]
\end_layout

\begin_layout Standard
\begin_inset CommandInset toc
LatexCommand tableofcontents

\end_inset


\end_layout

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\begin_inset FloatList table

\end_inset


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\begin_inset FloatList figure

\end_inset


\end_layout

\begin_layout Standard
[List of Abbreviations]
\end_layout

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

\begin_layout Plain Layout
Look into auto-generated nomenclature list: https://wiki.lyx.org/Tips/Nomenclature
\end_layout

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\end_layout

\begin_layout List of TODOs

\end_layout

\begin_layout Standard
[Abstract]
\end_layout

\begin_layout Chapter*
Abstract
\end_layout

\begin_layout Chapter
Introduction
\end_layout

\begin_layout Section
Background & Significance
\end_layout

\begin_layout Subsection
Biological motivation
\end_layout

\begin_layout Itemize
Rejection is the major long-term threat to organ and tissue grafts
\end_layout

\begin_deeper
\begin_layout Itemize
Common mechanisms of rejection 
\end_layout

\begin_layout Itemize
Effective immune suppression requires monitoring for rejection and tuning
 
\end_layout

\begin_layout Itemize
Current tests for rejection (tissue biopsy) are invasive and biased 
\end_layout

\begin_layout Itemize
A blood test based on microarrays would be less biased and invasive
\end_layout

\end_deeper
\begin_layout Itemize
Memory cells are resistant to immune suppression
\end_layout

\begin_deeper
\begin_layout Itemize
Mechanisms of resistance in memory cells are poorly understood 
\end_layout

\begin_layout Itemize
A better understanding of immune memory formation is needed
\end_layout

\end_deeper
\begin_layout Itemize
Mesenchymal stem cell infusion is a promising new treatment to prevent/delay
 rejection
\end_layout

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

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

\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.
\end_layout

\begin_layout Itemize
ChIP-seq Peak calling
\end_layout

\begin_deeper
\begin_layout Itemize
Cross-correlation analysis to determine fragment size
\end_layout

\begin_layout Itemize
Broad vs narrow peaks
\end_layout

\begin_layout Itemize
SICER for broad peaks
\end_layout

\begin_layout Itemize
IDR for biologically reproducible peaks
\end_layout

\begin_layout Itemize
csaw peak filtering guidelines for unbiased downstream analysis
\end_layout

\end_deeper
\begin_layout Itemize
Normalization is non-trivial and application-dependant
\end_layout

\begin_deeper
\begin_layout Itemize
Expression arrays: RMA & fRMA; why fRMA is needed
\end_layout

\begin_layout Itemize
Methylation arrays: M-value transformation approximates normal data but
 induces heteroskedasticity
\end_layout

\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
\end_layout

\end_deeper
\begin_layout Itemize
Limma: The standard linear modeling framework for genomics
\end_layout

\begin_deeper
\begin_layout Itemize
empirical Bayes variance modeling: limma's core feature
\end_layout

\begin_layout Itemize
edgeR & DESeq2: Extend with negative bonomial GLM for RNA-seq and other
 count data
\end_layout

\begin_layout Itemize
voom: Extend with precision weights to model mean-variance trend
\end_layout

\begin_layout Itemize
arrayWeights and duplicateCorrelation to handle complex variance structures
\end_layout

\end_deeper
\begin_layout Itemize
sva and ComBat for batch correction
\end_layout

\begin_layout Itemize
Factor analysis: PCA, MDS, MOFA
\end_layout

\begin_deeper
\begin_layout Itemize
Batch-corrected PCA is informative, but careful application is required
 to avoid bias
\end_layout

\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
\end_layout

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

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

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

\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
\end_layout

\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
\end_layout

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

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

\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
\begin_inset Flex TODO Note (inline)
status open

\begin_layout Plain Layout
Author list: Me, Sarah, Dan
\end_layout

\end_inset


\end_layout

\begin_layout Section
Approach
\end_layout

\begin_layout Itemize
CD4 T-cells are central to all adaptive immune responses and memory
\end_layout

\begin_layout Itemize
H3K4 and H3K27 methylation are major epigenetic regulators of gene expression
\end_layout

\begin_layout Itemize
Canonically, H3K4 is activating and H3K27 is inhibitory, but the reality
 is complex
\end_layout

\begin_layout Itemize
Looking at these marks during CD4 activation and memory should reveal new
 mechanistic details
\end_layout

\begin_layout Itemize
Test 
\begin_inset Quotes eld
\end_inset

poised promoter
\begin_inset Quotes erd
\end_inset

 hypothesis in which H3K4 and H3K27 are both methylated
\end_layout

\begin_layout Itemize
Expand scope of analysis beyond simple promoter counts
\end_layout

\begin_deeper
\begin_layout Itemize
Analyze peaks genome-wide, including in intergenic regions
\end_layout

\begin_layout Itemize
Analysis of coverage distribution shape within promoters, e.g.
 upstream vs downstream coverage
\end_layout

\end_deeper
\begin_layout Section
Methods
\end_layout

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

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\align center
\begin_inset Graphics
	filename graphics/CD4-csaw/rulegraphs/rulegraph-all.pdf
	width 100theight%

\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
A reproducible workflow 
\begin_inset CommandInset citation
LatexCommand cite
key "gh-cd4-csaw"
literal "false"

\end_inset

 was written to analyze the raw ChIP-seq and RNA-seq data from previous
 studies 
\begin_inset CommandInset citation
LatexCommand cite
key "LaMere2016,LaMere2017"
literal "true"

\end_inset

.
 Briefly, this data consists of RNA-seq and ChIP-seq from CD4 T-cells cultured
 from 4 donors.
 From each donor, naive and memory CD4 T-cells were isolated separately.
 Then cultures of both cells were activated [how?], and samples were taken
 at 4 time points: Day 0 (pre-activation), Day 1 (early activation), Day
 5 (peak activation), and Day 14 (post-activation).
 For each combination of cell type and time point, RNA was isolated, and
 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.
\end_layout

\begin_layout Standard
Sequence reads were retrieved from the Sequence Read Archive (SRA) 
\begin_inset CommandInset citation
LatexCommand cite
key "Leinonen2011"
literal "false"

\end_inset

.
 ChIP-seq (and input) reads were aligned to CRCh38 genome assembly using
 Bowtie 2 
\begin_inset CommandInset citation
LatexCommand cite
key "Langmead2012,Schneider2017,gh-hg38-ref"
literal "false"

\end_inset

.
 Artifact regions were annotated using a custom implementation of the GreyListCh
IP algorithm, and these 
\begin_inset Quotes eld
\end_inset

greylists
\begin_inset Quotes erd
\end_inset

 were merged with the ENCODE blacklist 
\begin_inset CommandInset citation
LatexCommand cite
key "greylistchip,Amemiya2019,Dunham2012"
literal "false"

\end_inset

.
 Any read or peak overlapping one of these regions was regarded as artifactual
 and excluded from downstream analyses.
 
\end_layout

\begin_layout Standard
Peaks are called using epic, an implementation of the SICER algorithm 
\begin_inset CommandInset citation
LatexCommand cite
key "Zang2009,gh-epic"
literal "false"

\end_inset

.
 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 
\begin_inset CommandInset citation
LatexCommand cite
key "Zhang2008"
literal "false"

\end_inset

.
 
\end_layout

\begin_layout Itemize
Re-analyze previously published CD4 ChIP-seq & RNA-seq data 
\end_layout

\begin_deeper
\begin_layout Itemize
Completely reimplement analysis from scratch as a reproducible workflow
\end_layout

\begin_layout Itemize
Use newly published methods & algorithms not available during the original
 analysis: SICER, csaw, MOFA, ComBat, sva, GREAT, and more
\end_layout

\end_deeper
\begin_layout Itemize
SICER, IDR, csaw, & GREAT to call ChIP-seq peaks genome-wide, perform differenti
al abundance analysis, and relate those peaks to gene expression
\end_layout

\begin_layout Itemize
Promoter counts in sliding windows around each gene's highest-expressed
 TSS to investigate coverage distribution within promoters
\end_layout

\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

\end_inset


\end_layout

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

\begin_layout Itemize
Figures comparing MACS (non-broad peak caller) to SICER/epic (broad peak
 caller)
\end_layout

\begin_deeper
\begin_layout Itemize
Compare peak sizes and number of called peaks
\end_layout

\begin_layout Itemize
Show representative IDR consistency plots for both
\end_layout

\end_deeper
\begin_layout Itemize
IDR analysis shows that SICER-called peaks are much more reproducible between
 biological replicates
\end_layout

\begin_layout Itemize
Each histone mark is enriched within a certain radius of gene TSS positions,
 but that radius is different for each mark (figure)
\end_layout

\begin_layout Subsection
RNA-seq has a large confounding batch effect
\end_layout

\begin_layout Itemize
RNA-seq batch effect can be partially corrected, but still induces uncorrectable
 biases in downstream analysis
\end_layout

\begin_deeper
\begin_layout Itemize
Figure showing MDS plot before & after ComBat
\end_layout

\begin_layout Itemize
Figure relating sample weights to batches, cell types, time points, etc.,
 showing that one batch is significantly worse quality
\end_layout

\begin_layout Itemize
Figures showing p-value histograms for within-batch and cross-batch contrasts,
 showing that cross-batch contrasts have attenuated signal, as do comparisons
 within the bad batch
\end_layout

\end_deeper
\begin_layout Subsection
ChIP-seq must be corrected for hidden confounding factors
\end_layout

\begin_layout Itemize
Figures showing pre- and post-SVA MDS plots for each histone mark
\end_layout

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

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

\begin_layout Itemize
H3K4 is correlated with higher expression, and H3K27 is correlated with
 lower expression genome-wide
\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
MOFA recovers biologically relevant variation from blind analysis by correlating
 across datasets
\end_layout

\begin_layout Itemize
MOFA 
\begin_inset CommandInset citation
LatexCommand cite
key "Argelaguet2018"
literal "false"

\end_inset

 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_deeper
\begin_layout Itemize
Figure: show percent-variance-explained plot from MOFA and PCA-like plots
 for the relevant latent factors
\end_layout

\begin_layout Itemize
MOFA analysis also shows that batch effect correction can't get much better
 than it already is (Figure comparing blind MOFA batch correction to ComBat
 correction)
\end_layout

\end_deeper
\begin_layout Subsection
Naive-to-memory convergence observed in H3K4 and RNA-seq data, not in H3K27me3
\end_layout

\begin_layout Itemize
H3K4 and RNA-seq data show clear evidence of naive convergence with memory
 between days 1 and 5 (MDS plot figure, also compare with last figure from
 
\begin_inset CommandInset citation
LatexCommand cite
key "LaMere2016"
literal "false"

\end_inset

)
\end_layout

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

\begin_layout Plain Layout
Note that Sarah has granted permission to use her figures
\end_layout

\end_inset


\end_layout

\begin_layout Itemize
Table of numbers of genes different between N & M at each time point, showing
 dwindling differences at later time points, consistent with convergence
\end_layout

\begin_layout Itemize
Similar figure for H3K27me3 showing lack of convergence
\end_layout

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

\begin_layout Itemize
"Promoter radius" is not constant and must be defined empirically for a
 given data set
\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 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
\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 Itemize
Naive-to-memory convergence implies that naive cells are differentiating
 into memory cells, and that gene expression and H3K4 methylation are involved
 in this differentiation while H3K27me3 is less involved
\end_layout

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

\begin_layout Itemize
Discuss advantages of developing using a reproducible workflow
\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
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 ararys, 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

\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 collapsed

\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 collapsed

\begin_layout Plain Layout
Summarize the get.best.threshold algorithm for PAM threshold selection
\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
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.
 First, a 
\begin_inset Quotes eld
\end_inset

basic
\begin_inset Quotes erd
\end_inset

 linear modeling analysis was performed, compensating for known features
 of the data using existing tools.
 A design matrix was prepared including terms for the factor of interest
 as well as the known biological confounders: sex, age, ethnicity, and diabetes.
 Since some samples came from the same patients at differen times, the intra-pat
ient 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 a appropriate for each test:
 t-tests for single contrasts, and F-tests for multiple contrasts.
\end_layout

\begin_layout Standard
For the second analysis, 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

.
 For the third analysis, the voom method was adapted to run on methylation
 array data and used to model the mean-variance trend as 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"
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.
\end_layout

\begin_layout Section
Results
\end_layout

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

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

\end_inset


\end_layout

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

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

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

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename graphics/PAM/predplot.pdf
	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: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 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

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

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename graphics/PAM/ROC-TXvsAR-internal.pdf
	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:ROC-PAM-int"

\end_inset

ROC curves for PAM on internal validation data using different normalization
 strategies
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

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

\begin_layout Plain Layout
\align center
\begin_inset Tabular
<lyxtabular version="3" rows="7" columns="4">
<features tabularvalignment="middle">
<column alignment="center" valignment="top">
<column alignment="center" valignment="top">
<column alignment="center" valignment="top">
<column alignment="center" valignment="top">
<row>
<cell alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout

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\series medium
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\emph off
\bar no
\strikeout off
\xout off
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\noun off
\color none
Normalization
\end_layout

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

\begin_layout Plain Layout
Single-channel?
\end_layout

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

\begin_layout Plain Layout

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Internal Val.
 AUC
\end_layout

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

\begin_layout Plain Layout
External Val.
 AUC
\end_layout

\end_inset
</cell>
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RMA
\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

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0.852
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0.713
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dChip
\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

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\begin_layout Plain Layout

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RMA + GRSN
\end_layout

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

\begin_layout Plain Layout
No
\end_layout

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\color none
dChip + GRSN
\end_layout

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

\begin_layout Plain Layout
No
\end_layout

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fRMA
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Yes
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SCAN
\end_layout

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</cell>
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\begin_inset Text

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Yes
\end_layout

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

\begin_layout Plain Layout

\family roman
\series medium
\shape up
\size normal
\emph off
\bar no
\strikeout off
\xout off
\uuline off
\uwave off
\noun off
\color none
0.689
\end_layout

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

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout
\begin_inset CommandInset label
LatexCommand label
name "tab: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
\begin_inset Float figure
wide false
sideways false
status open

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename graphics/PAM/ROC-TXvsAR-external.pdf
	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:ROC-PAM-ext"

\end_inset

ROC curve for PAM on external validation data using different normalization
 strategies
\end_layout

\end_inset


\end_layout

\end_inset


\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

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

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename graphics/frma-pax-bx/batchsize_batches.pdf

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

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

\end_inset


\series bold
Effect of batch size selection on number of batches included in fRMA probe
 weight learning.
 
\series default
For batch sizes ranging from 3 to 15, the number of batches with at least
 that many samples was 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
\begin_inset Float figure
wide false
sideways false
status open

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename graphics/frma-pax-bx/batchsize_samples.pdf

\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
Effect of batch size selection on number of samples included in fRMA probe
 weight learning.
 
\series default
For batch sizes ranging from 3 to 15, the number of samples included in
 probe weight training was 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 (Figures
 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:batch-size-batches"
plural "false"
caps "false"
noprefix "false"

\end_inset

 and 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:batch-size-samples"
plural "false"
caps "false"
noprefix "false"

\end_inset

, respectively).
 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
\align center
\begin_inset Graphics
	filename graphics/frma-pax-bx/M-BX-violin.pdf
	lyxscale 40
	height 80theight%
	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 log ratios between normalizations for 20 biopsy samples.
 
\series default
Each of 20 randomly selected biopsy samples was normalized with RMA and
 with 5 different sets of fRMA vectors.
 This shows the distribution of log ratios between normalized expression
 values, aggregated across all 20 arrays.
\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 Graphics
	filename graphics/frma-pax-bx/MA-BX-RMA.fRMA.pdf
	lyxscale 50
	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
Representative MA plot comparing RMA against fRMA for 20 biopsy samples.
 
\series default
Averages and log ratios were computed for every probe in each of 20 biopsy
 samples between RMA normalization and fRMA.
 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
\begin_inset Float figure
wide false
sideways false
status open

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename graphics/frma-pax-bx/MA-BX-fRMA.fRMA.pdf
	lyxscale 50
	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
Representative MA plot comparing different fRMA vectors for 20 biopsy samples.
 
\series default
Averages and log ratios were computed for every probe in each of 20 biopsy
 samples between fRMA normalizations using vectors from two different batch
 samplings.
 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 Standard
\begin_inset Float figure
wide false
sideways false
status open

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename graphics/frma-pax-bx/M-PAX-violin.pdf
	lyxscale 40
	height 80theight%
	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 log ratios between normalizations for 20 blood samples.
 
\series default
Each of 20 randomly selected blood samples was normalized with RMA and with
 5 different sets of fRMA vectors.
 This shows the distribution of log ratios between normalized expression
 values, aggregated across all 20 arrays.
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

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

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename graphics/frma-pax-bx/MA-PAX-RMA.fRMA.pdf
	lyxscale 50
	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
Representative MA plot comparing RMA against fRMA for 20 blood samples.
 
\series default
Averages and log ratios were computed for every probe in each of 20 blood
 samples between RMA normalization and fRMA.
 Density of points is represented by darkness of shading, and individual
 outlier points are plotted.
\end_layout

\end_inset


\end_layout

\begin_layout Plain Layout

\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/frma-pax-bx/MA-PAX-fRMA.fRMA.pdf
	lyxscale 50
	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
Representative MA plot comparing different fRMA vectors for 20 blood samples.
 
\series default
Averages and log ratios were computed for every probe in each of 20 blood
 samples between fRMA normalizations using vectors from two different batch
 samplings.
 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 Subsection
Adapting voom to methylation array data improves model fit
\end_layout

\begin_layout Itemize
voom, precision weights, and sva improved model fit
\end_layout

\begin_deeper
\begin_layout Itemize
Also increased sensitivity for detecting differential methylation
\end_layout

\end_deeper
\begin_layout Itemize
Figure showing (a) heteroskedasticy without voom, (b) voom-modeled mean-variance
 trend, and (c) homoskedastic mean-variance trend after running voom
\end_layout

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

\begin_layout Plain Layout
Write figure legends
\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 Graphics
	filename graphics/methylvoom/unadj.dupCor/meanvar-trends-PAGE1-RASTER.png
	lyxscale 15
	groupId raster-600ppi

\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 with no SVA or weights
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

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

\begin_layout Plain Layout
\begin_inset Graphics
	filename graphics/methylvoom/unadj.dupcor.sva.aw/meanvar-trends-PAGE1-RASTER.png
	lyxscale 15
	groupId raster-600ppi

\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 with no SVA and sample quality weights.
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

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

\begin_layout Plain Layout
\begin_inset Graphics
	filename graphics/methylvoom/unadj.dupCor.sva.voomaw/meanvar-trends-PAGE1-RASTER.png
	lyxscale 15
	groupId raster-600ppi

\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:voom-sva-voomaw"

\end_inset

Mean-variance trend modelled by voom, with SVA and sample weights.
 
\series default
The y-axis is the square root of the standard deviation for each probe,
 because this is the scale on which voom fits its lowess curve.
\end_layout

\end_inset


\end_layout

\end_inset


\begin_inset Float figure
wide false
sideways false
status open

\begin_layout Plain Layout
\begin_inset Graphics
	filename graphics/methylvoom/unadj.dupCor.sva.voomaw/meanvar-trends-PAGE2-RASTER.png
	lyxscale 15
	groupId raster-600ppi

\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

Residual mean-variance trend after modeling with SVA, sample weights, and
 voom.
\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="5" 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
Covariate
\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" 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" 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" 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" rightline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
0.212
\end_layout

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

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout
\begin_inset CommandInset label
LatexCommand label
name "tab:weight-covariate-tests"

\end_inset

Association of sample weights with clinical covariates.
\end_layout

\end_inset


\end_layout

\begin_layout Plain Layout

\end_layout

\end_inset


\end_layout

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

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

\begin_layout Plain Layout

\end_layout

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

\begin_layout Plain Layout
Analysis
\end_layout

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

\begin_layout Plain Layout

\end_layout

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

\begin_layout Plain Layout

\end_layout

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

\begin_layout Plain Layout
Contrast
\end_layout

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

\begin_layout Plain Layout
A
\end_layout

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

\begin_layout Plain Layout
B
\end_layout

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

\begin_layout Plain Layout
C
\end_layout

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

\begin_layout Plain Layout
TX vs AR
\end_layout

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

\begin_layout Plain Layout
0
\end_layout

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

\begin_layout Plain Layout
25
\end_layout

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

\begin_layout Plain Layout
22
\end_layout

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

\begin_layout Plain Layout
TX vs ADNR
\end_layout

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

\begin_layout Plain Layout
7
\end_layout

\end_inset
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338
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TX vs CAN
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0
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231
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278
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\begin_inset CommandInset label
LatexCommand label
name "tab:methyl-num-signif"

\end_inset


\series bold
Number of probes significant at 10% FDR for each contrast in each analysis.
\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
Cite the pi0 estimation method from propTrueNull
\end_layout

\end_inset


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

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\begin_inset Tabular
<lyxtabular version="3" rows="5" columns="4">
<features tabularvalignment="middle">
<column alignment="center" valignment="top">
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Analysis
\end_layout

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Contrast
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A
\end_layout

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B
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C
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TX vs AR
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0
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10,063
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11,225
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TX vs ADNR
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27
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12,674
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13,086
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TX vs CAN
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966
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20,039
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20,955
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</lyxtabular>

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\begin_inset CommandInset label
LatexCommand label
name "tab:methyl-est-nonnull"

\end_inset


\series bold
Estimated number of non-null tests for each contrast in each analysis.
\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
Re-generate p-value histograms for all relevant contrasts in a single figure.
\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-build for normalizing a specific type of sample on a specific
 platform.
\end_layout

\begin_layout Subsection
voom
\end_layout

\begin_layout Itemize
Methods like voom designed for RNA-seq can also help with array analysis
\end_layout

\begin_layout Itemize
Extracting and modeling confounders common to many features improves model
 correspondence to known biology
\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 correction 
\begin_inset CommandInset citation
LatexCommand cite
key "Benjamini1995"
literal "false"

\end_inset

.
\end_layout

\begin_layout Standard
\begin_inset Note Note
status open

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

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

\end_inset


\end_layout

\begin_layout Section
Results
\end_layout

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

\begin_layout Standard
The objective of the present study was to validate a new protocol for deep
 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 open

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename graphics/Globin Paper/figure1 - globin-fractions.pdf

\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

\begin_layout Plain Layout

\end_layout

\end_inset


\end_layout

\begin_layout Standard
\begin_inset Float table
placement p
wide false
sideways true
status open

\begin_layout Plain Layout
\align center
\begin_inset Tabular
<lyxtabular version="3" rows="4" columns="7">
<features tabularvalignment="middle">
<column alignment="center" valignment="top">
<column alignment="center" valignment="top">
<column alignment="center" valignment="top">
<column alignment="center" valignment="top">
<column alignment="center" valignment="top">
<column alignment="center" valignment="top">
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<row>
<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout

\end_layout

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\family roman
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\color none
Percent of Total Reads
\end_layout

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

\begin_layout Plain Layout

\end_layout

\end_inset
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\begin_layout Plain Layout

\end_layout

\end_inset
</cell>
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\begin_layout Plain Layout

\end_layout

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</cell>
<cell multicolumn="1" alignment="center" valignment="top" topline="true" leftline="true" rightline="true" usebox="none">
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\begin_layout Plain Layout

\family roman
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Percent of Genic Reads
\end_layout

\end_inset
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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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\color none
Non-globin Reads
\end_layout

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

\family roman
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Globin Reads
\end_layout

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

\begin_layout Plain Layout

\family roman
\series medium
\shape up
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\emph off
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\color none
All Genic Reads
\end_layout

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

\family roman
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All Aligned Reads
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<cell alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" usebox="none">
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\begin_layout Plain Layout

\family roman
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Non-globin Reads
\end_layout

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

\begin_layout Plain Layout

\family roman
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\color none
Globin Reads
\end_layout

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

\begin_layout Plain Layout

\family roman
\series medium
\shape up
\size normal
\emph off
\bar no
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\color none
Yes
\end_layout

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

\begin_layout Plain Layout

\family roman
\series medium
\shape up
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\color none
50.4% ± 6.82
\end_layout

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

\begin_layout Plain Layout

\family roman
\series medium
\shape up
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\color none
3.48% ± 2.94
\end_layout

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

\begin_layout Plain Layout

\family roman
\series medium
\shape up
\size normal
\emph off
\bar no
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\uwave off
\noun off
\color none
53.9% ± 6.81
\end_layout

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

\begin_layout Plain Layout

\family roman
\series medium
\shape up
\size normal
\emph off
\bar no
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\color none
89.7% ± 2.40
\end_layout

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

\family roman
\series medium
\shape up
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\emph off
\bar no
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\color none
93.5% ± 5.25
\end_layout

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

\begin_layout Plain Layout

\family roman
\series medium
\shape up
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\emph off
\bar no
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\color none
6.49% ± 5.25
\end_layout

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

\begin_layout Plain Layout

\family roman
\series medium
\shape up
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\emph off
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\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
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\emph off
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\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
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44.6% ± 16.6
\end_layout

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

\family roman
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\shape up
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\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
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\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

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\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

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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

\begin_layout Plain Layout

\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 open

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename graphics/Globin Paper/figure2 - aveLogCPM-colored.pdf

\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 open

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename graphics/Globin Paper/figure3 - detection.pdf

\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 open

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename graphics/Globin Paper/figure4 - maplot-colored.pdf

\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 open

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename graphics/Globin Paper/figure5 - corrplot.pdf

\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


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\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 open

\begin_layout Plain Layout
\align center
\begin_inset Tabular
<lyxtabular version="3" rows="5" columns="5">
<features tabularvalignment="middle">
<column alignment="center" valignment="top">
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\series bold
No Globin Blocking
\end_layout

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

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\series bold
NS
\end_layout

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\begin_inset Text

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\series bold
Down
\end_layout

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<cell multirow="3" alignment="center" valignment="middle" topline="true" bottomline="true" leftline="true" usebox="none">
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\series bold
Globin-Blocking
\end_layout

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\series bold
Up
\end_layout

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231
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515
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2
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160
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11235
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136
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0
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548
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\series bold
\begin_inset Argument 1
status open

\begin_layout Plain Layout
Comparison of significantly differentially expressed genes with and without
 globin blocking.
\end_layout

\end_inset


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

\end_inset

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

\series default
 Up, Down: Genes significantly up/down-regulated in post-transplant samples
 relative to pre-transplant samples, with a false discovery rate of 10%
 or less.
 NS: Non-significant genes (false discovery rate greater than 10%).
\end_layout

\end_inset


\end_layout

\begin_layout Plain Layout

\end_layout

\end_inset


\end_layout

\begin_layout Standard
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"

\end_inset

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

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

\begin_layout Section
Discussion
\end_layout

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

\end_inset

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

\end_inset

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

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

\end_inset

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

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

\end_inset

.
 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

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

\begin_layout Plain Layout
Consider per-chapter future directions.
 Check instructions.
\end_layout

\end_inset


\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 lymphocytes: CD8 T-cells, B-cells, NK cells
\end_layout

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

\begin_layout Standard
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status open

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% Use "References" instead of "Bibliography" 
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\backslash
renewcommand{
\backslash
bibname}{References}
\end_layout

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\begin_inset Flex TODO Note (inline)
status open

\begin_layout Plain Layout
Check bib entry formatting & sort order
\end_layout

\end_inset


\end_layout

\begin_layout Standard
\begin_inset CommandInset bibtex
LatexCommand bibtex
btprint "btPrintCited"
bibfiles "refs,code-refs"
options "bibtotoc,unsrt"

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\end_layout

\end_body
\end_document