This code has been adapted from the tutorials available at WGCNA website
Installing required packages: WGCNA requires the following packages to be installed, one of them is only available through bioconductor
install.packages(c("dynamicTreeCut", "cluster", "flashClust", "Hmisc", "reshape", "foreach", "doParallel") )
source("http://bioconductor.org/biocLite.R")
biocLite("impute")
install.packages("WGCNA")
Loading WGCNA library, and settings to allow parallel execution
library(WGCNA)
## Loading required package: dynamicTreeCut
## Loading required package: flashClust
##
## Attaching package: 'flashClust'
##
## The following object is masked from 'package:stats':
##
## hclust
## ==========================================================================
## *
## * Package WGCNA 1.41.1 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=12
## *
## * To set the environment variable in linux bash shell, type
## *
## * export ALLOW_WGCNA_THREADS=12
## *
## * 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
options(stringsAsFactors = FALSE);
enableWGCNAThreads()
## Allowing parallel execution with up to 11 working processes.
Loading the data: WGCNA requires genes be given in the columns
load("oed.RData")
dim(oed)
## [1] 17372 92
gene.names=rownames(oed)
trans.oed=t(oed)
For the purpose of this exercise, we focus on a smaller set of genes
n=500;
datExpr=trans.oed[,1:n]
dim(datExpr)
## [1] 92 500
SubGeneNames=gene.names[1:n]
Choosing a soft-threshold to fit a scale-free topology to the network
powers = c(c(1:10), seq(from = 12, to=20, by=2));
sft=pickSoftThreshold(datExpr,dataIsExpr = TRUE,powerVector = powers,corFnc = cor,corOptions = list(use = 'p'),networkType = "unsigned")
## Power SFT.R.sq slope truncated.R.sq mean.k. median.k. max.k.
## 1 1 0.719 -1.700 0.874 40.60 37.900 102.0
## 2 2 0.521 -0.707 0.679 15.00 12.500 40.0
## 3 3 0.814 -0.476 0.764 9.92 7.970 25.9
## 4 4 0.900 -0.671 0.887 7.73 5.790 23.6
## 5 5 0.803 -0.809 0.748 6.47 4.140 22.2
## 6 6 0.749 -0.901 0.677 5.63 3.610 21.1
## 7 7 0.880 -0.913 0.846 5.02 3.100 20.2
## 8 8 0.885 -0.928 0.853 4.55 2.610 19.4
## 9 9 0.849 -0.940 0.806 4.17 2.170 18.6
## 10 10 0.918 -0.940 0.896 3.86 1.890 18.0
## 11 12 0.897 -0.942 0.867 3.36 1.610 16.8
## 12 14 0.882 -0.949 0.849 2.99 1.360 15.8
## 13 16 0.819 -0.996 0.770 2.69 1.180 15.0
## 14 18 0.890 -0.974 0.858 2.44 0.997 14.3
## 15 20 0.885 -0.987 0.852 2.24 0.802 13.7
# Plot the results
sizeGrWindow(9, 5)
par(mfrow = c(1,2));
cex1 = 0.9;
# Scale-free topology fit index as a function of the soft-thresholding power
plot(sft$fitIndices[,1], -sign(sft$fitIndices[,3])*sft$fitIndices[,2],xlab="Soft Threshold (power)",ylab="Scale Free Topology Model Fit, signed R^2",type="n", main = paste("Scale independence"));
text(sft$fitIndices[,1], -sign(sft$fitIndices[,3])*sft$fitIndices[,2],labels=powers,cex=cex1,col="red");
# Red line corresponds to using an R^2 cut-off
abline(h=0.80,col="red")
# Mean connectivity as a function of the soft-thresholding power
plot(sft$fitIndices[,1], sft$fitIndices[,5],xlab="Soft Threshold (power)",ylab="Mean Connectivity", type="n",main = paste("Mean connectivity"))
text(sft$fitIndices[,1], sft$fitIndices[,5], labels=powers, cex=cex1,col="red")
Generating adjacency and TOM similarity matrices based on the selected softpower
softPower = 7;
#calclute the adjacency matrix
adj= adjacency(datExpr,type = "unsigned", power = softPower);
#turn adjacency matrix into topological overlap to minimize the effects of noise and spurious associations
TOM=TOMsimilarityFromExpr(datExpr,networkType = "unsigned", TOMType = "unsigned", power = softPower);
## TOM calculation: adjacency..
## ..will use 11 parallel threads.
## Fraction of slow calculations: 0.000000
## ..connectivity..
## ..matrix multiplication..
## ..normalization..
## ..done.
colnames(TOM) =rownames(TOM) =SubGeneNames
dissTOM=1-TOM
Module detection
#hierarchical clustering of the genes based on the TOM dissimilarity measure
geneTree = flashClust(as.dist(dissTOM),method="average");
#plot the resulting clustering tree (dendrogram)
plot(geneTree, xlab="", sub="",cex=0.3);
# Set the minimum module size
minModuleSize = 20;
# Module identification using dynamic tree cut
dynamicMods = cutreeDynamic(dendro = geneTree, method="tree", minClusterSize = minModuleSize);
#dynamicMods = cutreeDynamic(dendro = geneTree, distM = dissTOM, method="hybrid", deepSplit = 2, pamRespectsDendro = FALSE, minClusterSize = minModuleSize);
#the following command gives the module labels and the size of each module. Lable 0 is reserved for unassigned genes
table(dynamicMods)
## dynamicMods
## 0 1 2 3 4 5 6 7 8
## 263 44 40 33 27 26 24 23 20
#Plot the module assignment under the dendrogram; note: The grey color is reserved for unassigned genes
dynamicColors = labels2colors(dynamicMods)
table(dynamicColors)
## dynamicColors
## black blue brown green grey pink red
## 23 40 33 26 263 20 24
## turquoise yellow
## 44 27
plotDendroAndColors(geneTree, dynamicColors, "Dynamic Tree Cut", dendroLabels = FALSE, hang = 0.03, addGuide = TRUE, guideHang = 0.05, main = "Gene dendrogram and module colors")
#discard the unassigned genes, and focus on the rest
restGenes= (dynamicColors != "grey")
diss1=1-TOMsimilarityFromExpr(datExpr[,restGenes], power = softPower)
## TOM calculation: adjacency..
## ..will use 11 parallel threads.
## Fraction of slow calculations: 0.000000
## ..connectivity..
## ..matrix multiplication..
## ..normalization..
## ..done.
colnames(diss1) =rownames(diss1) =SubGeneNames[restGenes]
hier1=flashClust(as.dist(diss1), method="average" )
plotDendroAndColors(hier1, dynamicColors[restGenes], "Dynamic Tree Cut", dendroLabels = FALSE, hang = 0.03, addGuide = TRUE, guideHang = 0.05, main = "Gene dendrogram and module colors")
#set the diagonal of the dissimilarity to NA
diag(diss1) = NA;
#Visualize the Tom plot. Raise the dissimilarity matrix to the power of 4 to bring out the module structure
sizeGrWindow(7,7)
TOMplot(diss1, hier1, as.character(dynamicColors[restGenes]))
Extract modules
module_colors= setdiff(unique(dynamicColors), "grey")
for (color in module_colors){
module=SubGeneNames[which(dynamicColors==color)]
write.table(module, paste("module_",color, ".txt",sep=""), sep="\t", row.names=FALSE, col.names=FALSE,quote=FALSE)
}
Look at expression patterns of these genes, as they're clustered
module.order <- unlist(tapply(1:ncol(datExpr),as.factor(dynamicColors),I))
m<-t(t(datExpr[,module.order])/apply(datExpr[,module.order],2,max))
heatmap(t(m),zlim=c(0,1),col=gray.colors(100),Rowv=NA,Colv=NA,labRow=NA,scale="none",RowSideColors=dynamicColors[module.order])
We can now look at the module gene listings and try to interpret their functions .. for instance using http://amigo.geneontology.org/rte
Quantify module similarity by eigengene correlation. Eigengenes: Module representatives
MEList = moduleEigengenes(datExpr, colors = dynamicColors)
MEs = MEList$eigengenes
plotEigengeneNetworks(MEs, "", marDendro = c(0,4,1,2), marHeatmap = c(3,4,1,2))
