Progress on Ch3 methyl results and Ch2 methods

thesis.lyx

@@ -545,16 +545,73 @@ Analysis of coverage distribution shape within promoters, e.g.
 Methods
 \end_layout
 
-\begin_layout Itemize
-Re-analyze previously published CD4 ChIP-seq & RNA-seq data 
+\begin_layout Standard
+\begin_inset Float figure
+wide false
+sideways true
+status open
+
+\begin_layout Plain Layout
+\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 was written to analyze the raw ChIP-seq and RNA-seq
+ data from previous studies 
 \begin_inset CommandInset citation
 LatexCommand cite
-key "LaMere2016,Lamere2017"
+key "LaMere2016,LaMere2017"
 literal "true"
 
 \end_inset
 
+.
+ The workflow starts by retrieving the sequence reads from the Sequence
+ Read Archive (SRA) 
+\begin_inset CommandInset citation
+LatexCommand cite
+key "Leinonen2011"
+literal "false"
+
+\end_inset
+
+.
+\end_layout
 
+\begin_layout Itemize
+Re-analyze previously published CD4 ChIP-seq & RNA-seq data 
 \end_layout
 
 \begin_deeper
@@ -1413,7 +1470,7 @@ literal "false"
 
 \begin_layout Standard
 From the M-values, a series of parallel analyses was performed, each adding
- an additional step into the model fit to accomodate a feature of the data.
+ additional steps into the model fit to accomodate a feature of the data.
  First, a 
 \begin_inset Quotes eld
 \end_inset
@@ -1464,10 +1521,9 @@ literal "false"
 .
  These surrogate variables were added to the design matrix before fitting
  the linear model.
- For the third analysis, SVA was used, and in addition sample quality weights
- were estimated from the data and used during linear modeling to down-weight
- the controbution of highly variable arrays while increasing the weight
- to arrays with lower variability
+ 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"
@@ -1476,9 +1532,9 @@ literal "false"
 \end_inset
 
 .
- Lastly, the voom method was adapted to run on methylation array data and
- used to model the mean-variance trend as individual observation weights
- 
+ 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"
@@ -2949,6 +3005,19 @@ 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
@@ -2957,7 +3026,7 @@ status open
 
 \begin_layout Plain Layout
 \begin_inset Graphics
-	filename graphics/methylvoom/unadj.naive/meanvar-trends-RASTER.png
+	filename graphics/methylvoom/unadj.dupCor/meanvar-trends-PAGE1-RASTER.png
 	lyxscale 15
 	groupId raster-600ppi
 
@@ -2974,11 +3043,11 @@ status open
 \series bold
 \begin_inset CommandInset label
 LatexCommand label
-name "fig:meanvar-naive"
+name "fig:meanvar-basic"
 
 \end_inset
 
-Mean-variance trend with no adjustment
+Mean-variance trend with no SVA or weights
 \end_layout
 
 \end_inset
@@ -2991,17 +3060,777 @@ Mean-variance trend with no adjustment
 
 \end_layout
 
-\begin_layout Itemize
-Figure showing sample weights and their relations to
+\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 Itemize
-Figure showing MDS plot with and without SVA correction
+\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
 
-\begin_layout Itemize
-Figure and/or table showing improved p-value historgrams/number of significant
- genes (might need to get this from Padma)
+\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
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+status open
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+\align center
+\begin_inset Tabular
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+\begin_inset CommandInset label
+LatexCommand label
+name "tab:weight-covariate-tests"
+
+\end_inset
+
+Association of sample weights with clinical covariates.
+\end_layout
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+
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+</lyxtabular>
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+
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+\end_layout
+
+\begin_layout Plain Layout
+\begin_inset Caption Standard
+
+\begin_layout Plain Layout
+\begin_inset CommandInset label
+LatexCommand label
+name "tab:methyl-num-signif"
+
+\end_inset
+
+
+\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
+
+
+\end_layout
+
+\begin_layout Standard
+\begin_inset Float table
+wide false
+sideways false
+status open
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+\begin_layout Plain Layout
+\begin_inset Caption Standard
+
+\begin_layout Plain Layout
+\begin_inset CommandInset label
+LatexCommand label
+name "tab:methyl-est-nonnull"
+
+\end_inset
+
+
+\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