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Network.html
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<!DOCTYPE html>
<html xmlns="http://www.w3.org/1999/xhtml">
<head>
<meta charset="utf-8">
<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
<meta name="generator" content="pandoc" />
<meta name="author" content="Lluís Revilla" />
<meta name="date" content="2016-12-23" />
<title>Preparating data for the network creation with WGCNA</title>
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<h1 class="title">Preparating data for the network creation with WGCNA</h1>
<h4 class="author"><em><a href="mailto:[email protected]">Lluís Revilla</a></em></h4>
<h4 class="date"><em>2016-12-23</em></h4>
</div>
<h1>Contents</h1>
<div id="TOC">
<ul>
<li><a href="#load-data"><span class="toc-section-number">1</span> Load data</a></li>
<li><a href="#filtering"><span class="toc-section-number">2</span> Filtering</a></li>
<li><a href="#check-data"><span class="toc-section-number">3</span> Check data</a></li>
<li><a href="#export"><span class="toc-section-number">4</span> Export</a></li>
<li><a href="#network-building"><span class="toc-section-number">5</span> Network building</a></li>
<li><a href="#sessioninfo"><span class="toc-section-number">6</span> SessionInfo</a></li>
</ul>
</div>
<div id="load-data" class="section level1">
<h1><span class="header-section-number">1</span> Load data</h1>
<p>We start from the expression data we previously normalized for the differential gene expression analysis.</p>
<pre class="r"><code>load(file = "Expression_ALD.RData", verbose = TRUE)
## Loading objects:
## v1
## v2
## phenoData
load("ALD.RData", verbose = TRUE)
## Loading objects:
## fit
## fit2
## fit2.2
## fit.2
## design
## design2
## i.design
## i.design2
## dge
## ALD
## contrasts0
## v1
## v2
library("WGCNA")
## Loading required package: dynamicTreeCut
## Loading required package: fastcluster
##
## Attaching package: 'fastcluster'
## The following object is masked from 'package:stats':
##
## hclust
##
## ==========================================================================
## *
## * Package WGCNA 1.51 loaded.
## *
## * Important note: It appears that your system supports multi-threading,
## * but it is not enabled within WGCNA in R.
## * To allow multi-threading within WGCNA with all available cores, use
## *
## * allowWGCNAThreads()
## *
## * within R. Use disableWGCNAThreads() to disable threading if necessary.
## * Alternatively, set the following environment variable on your system:
## *
## * ALLOW_WGCNA_THREADS=<number_of_processors>
## *
## * for example
## *
## * ALLOW_WGCNA_THREADS=2
## *
## * To set the environment variable in linux bash shell, type
## *
## * export ALLOW_WGCNA_THREADS=2
## *
## * before running R. Other operating systems or shells will
## * have a similar command to achieve the same aim.
## *
## ==========================================================================
##
## Attaching package: 'WGCNA'
## The following object is masked from 'package:stats':
##
## cor</code></pre>
<p>Of the second file we are only interested in the design2 object.</p>
</div>
<div id="filtering" class="section level1">
<h1><span class="header-section-number">2</span> Filtering</h1>
<p>Firt we convert it to the format WGCNA requires (each row a sample, each column a gene). We</p>
<pre class="r"><code>data.wgcna <- t(v1$E)
vclin0 <- design2[, "Progression", drop = FALSE]</code></pre>
<p>We will make a network of the whole disease, another for the early steps from Normal to compensated cirrhosis and from non-severe alcoholic hepatitis to severe alcoholic hepatitis.</p>
<pre class="r"><code>early.patients <- phenoData$Status %in% c("ASH", "C.Comp", "Normal")
early.ALD <- data.wgcna[early.patients, ]
late.ALD <- data.wgcna[!early.patients, ]</code></pre>
</div>
<div id="check-data" class="section level1">
<h1><span class="header-section-number">3</span> Check data</h1>
<p>In order to see if the data really reflects what the model we can plot it in a PCA:</p>
<pre class="r"><code>library("ggbiplot")
dists <- function(data) {
as.dist(1 - WGCNA::cor(data, method = "spearman",
use = "pairwise.complete.obs"))
}
MDS.graph <- function(dists, outcome, col=NULL) {
cmd <- cmdscale(dists, eig = TRUE, add = TRUE)
perc.v <- round(cmd$eig/sum(cmd$eig)*100, 1)
plot(cmd$points[, 1], cmd$points[, 2], type = "n", main = "MDS",
xlab = paste0("PC1 (", perc.v[1], "% explained var.)"),
ylab = paste0("PC2 (", perc.v[2], "% explained var.)"))
text(cmd$points[,1 ], cmd$points[, 2], col = col, cex = 0.9,
labels = outcome)
invisible(cmd)
}
PCA.graph <- function(dists, outcome) {
pca <- prcomp(dists, scale. = TRUE)
pca.plo <- ggbiplot(pca, obs.scale = 1, var.scale = 1, ellipse = TRUE,
group = outcome, var.axes = FALSE, main = "PCA")
plot(pca.plo)
invisible(pca)
}
explore <- function(data, outcome, col, file) {
dists <- dists(data)
pdf(file)
PCA.graph(dists, outcome)
MDS.graph(dists, outcome, col)
dev.off()
}
colors <- c(rep("red", 18), rep("orange", 11), rep("yellow", 12), rep("pink", 9), rep("green", 10))
explore(data = v1$E, file = "Whole_set.pdf", outcome = phenoData$Status,
col = colors)
## png
## 2
early.vclin <- vclin0[rownames(phenoData) %in% rownames(early.ALD), , drop = FALSE]
explore(data = t(early.ALD), file = "Early_ALD.pdf",
outcome = phenoData$Status[30:60], col = colors[30:60])
## png
## 2
late.vclin <- vclin0[rownames(phenoData) %in% rownames(late.ALD), , drop = FALSE]
explore(data = t(late.ALD), file = "Late_ALD.pdf", outcome = phenoData$Status[1:29],
col = colors[1:29])
## png
## 2
d <- dists(v1$E)
PCA.graph(d, phenoData$Status)</code></pre>
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" /></p>
<p>As we can see on the MDS (and others not shown, but stored in the files) the data reflects the differences and confirms the model. Furthermore, it also confirms that dividing in this two steps also provides a good distinction between samples. However there is a clear outlier: the sample E49. It has clearly a different expression than other samples, maybe because she/he had other complications. In order to avoid introducing noise to the networks we remove this sample for the network constructions:</p>
<pre class="r"><code>data.wgcna <- data.wgcna[!"E49" == rownames(data.wgcna), ]
early.ALD <- early.ALD[!"E49" == rownames(early.ALD), ]</code></pre>
<p>Now we can save the data ready for WGCNA</p>
</div>
<div id="export" class="section level1">
<h1><span class="header-section-number">4</span> Export</h1>
<p>For an easy use with the TFM repository that will be used I rename the objects and store them separately:</p>
<pre class="r"><code>vclin <- vclin0
gsg <- goodSamplesGenes(data.wgcna)</code></pre>
<pre><code>## Flagging genes and samples with too many missing values...
## ..step 1</code></pre>
<pre class="r"><code>data.wgcna <- data.wgcna[gsg$goodSamples, gsg$goodGenes]
save(data.wgcna, vclin, file = "Whole_Network.RData")</code></pre>
<p>We can check that the whole data cohort has 60 samples and 13366 genes have non zero variance.</p>
<pre class="r"><code>gsg <- goodSamplesGenes(early.ALD)</code></pre>
<pre><code>## Flagging genes and samples with too many missing values...
## ..step 1</code></pre>
<pre class="r"><code>data.wgcna <- early.ALD[gsg$goodSamples, gsg$goodGenes]
vclin <- early.vclin
save(data.wgcna, vclin, file = "Early_Network.RData")</code></pre>
<p>The cohort of patients from the early state of the disease has 30 samples and 13366 genes have non zero variance.</p>
<pre class="r"><code>gsg <- goodSamplesGenes(late.ALD)</code></pre>
<pre><code>## Flagging genes and samples with too many missing values...
## ..step 1</code></pre>
<pre class="r"><code>data.wgcna <- late.ALD[gsg$goodSamples, gsg$goodGenes]
vclin <- late.vclin
save(data.wgcna, vclin, file = "Late_Network.RData")</code></pre>
<p>So the remaining samples are 29 patients and 13366 genes have non zero variance.</p>
<p>We have enough samples to build a robust network with WGCNA for each network. See the first <a href="https://labs.genetics.ucla.edu/horvath/CoexpressionNetwork/Rpackages/WGCNA/faq.html">FAQ</a> answer.</p>
</div>
<div id="network-building" class="section level1">
<h1><span class="header-section-number">5</span> Network building</h1>
<p>There are many option to find the co-expressed genes with WGCNA, here I will explain some of the most important:</p>
<ul>
<li>networkType This option changes how are negative correlations (You can also use a distance function)considered when building the adjacency matrix . There are three options:
<ul>
<li>“unsigned”: This option consider correlations independently of the sign.</li>
<li>“signed”: Considers as higher similarity those with positive correlation than with negative correlation</li>
<li>“signed hybrid”: Considers only the positive correlations.</li>
</ul></li>
<li>TOMType This option changes how are topological overlap matrices (TOM) calculated. There are two options:
<ul>
<li>“unsigned”: Don’t uses the sign of the correlations to build the TOM matrix from the adjacencies</li>
<li>“signed”: Uses the sign of the correlations to build the TOM matrix from the adjacencies</li>
</ul></li>
</ul>
<p>Depending on what is the question one wants to answer should choose between one of those.</p>
</div>
<div id="sessioninfo" class="section level1">
<h1><span class="header-section-number">6</span> SessionInfo</h1>
<p>Here are the packages and the versions used to analyse these data and build this page:</p>
<pre class="r"><code>sessionInfo()</code></pre>
<pre><code>## R version 3.3.2 (2016-10-31)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 14.04.5 LTS
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=es_ES.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=es_ES.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=es_ES.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=es_ES.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats graphics grDevices datasets utils methods base
##
## other attached packages:
## [1] WGCNA_1.51 fastcluster_1.1.21 dynamicTreeCut_1.63-1
## [4] limma_3.30.6 BiocStyle_2.2.1
##
## loaded via a namespace (and not attached):
## [1] splines_3.3.2 lattice_0.20-34 colorspace_1.3-1
## [4] htmltools_0.3.5 stats4_3.3.2 yaml_2.1.14
## [7] survival_2.40-1 foreign_0.8-67 DBI_0.5-1
## [10] BiocGenerics_0.20.0 RColorBrewer_1.1-2 matrixStats_0.51.0
## [13] foreach_1.4.3 plyr_1.8.4 stringr_1.1.0
## [16] munsell_0.4.3 gtable_0.2.0 codetools_0.2-15
## [19] memoise_1.0.0 evaluate_0.10 latticeExtra_0.6-28
## [22] Biobase_2.34.0 knitr_1.15.1 IRanges_2.8.1
## [25] doParallel_1.0.10 parallel_3.3.2 AnnotationDbi_1.36.0
## [28] preprocessCore_1.36.0 htmlTable_1.7 Rcpp_0.12.8
## [31] acepack_1.4.1 backports_1.0.4 scales_0.4.1
## [34] S4Vectors_0.12.0 Hmisc_4.0-0 gridExtra_2.2.1
## [37] impute_1.48.0 ggplot2_2.2.0 digest_0.6.10
## [40] stringi_1.1.2 grid_3.3.2 rprojroot_1.1
## [43] tools_3.3.2 magrittr_1.5 lazyeval_0.2.0
## [46] RSQLite_1.1-1 tibble_1.2 Formula_1.2-1
## [49] cluster_2.0.5 GO.db_3.4.0 Matrix_1.2-7.1
## [52] data.table_1.9.8 assertthat_0.1 rmarkdown_1.2
## [55] iterators_1.0.8 rpart_4.1-10 nnet_7.3-12</code></pre>
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