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WGCNA_functions.R
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WGCNA_functions.R
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library(WGCNA)
library(flashClust)
library(genefilter)
#disableWGCNAThreads()
options(stringsAsFactors = FALSE)
#' @param data1 data.frame of normalised gene expression values with samples as columns and rows as genes.
#' @param outFile character. A file path for the soft power plots as .pdf
#' @param propGenes A numeric value indicating the proportion of most variable genes to be retained in the analysis between 0 and 1.
#' @return This function does not return any value. A pdf file is generated with two plots.
determineSoftPowerWGCNA <- function(data1, outFile, propGenes=1){
options(stringsAsFactors = FALSE)
# Remove bad genes (missing values, ...)
nGenesInput <- dim(data1)[1]
data1 <- data1[goodSamplesGenes(datExpr=t(data1))$goodGenes, ]
nGenesGood <- dim(data1)[1]
nGenesRemoved <- nGenesInput - nGenesGood
message(paste(nGenesRemoved, " genes filtered from dataset"))
propGenes <- round(propGenes * dim(data1)[1])
# Filter genes based in variance
keepGenesExpr1 <- rank(-rowVars(data1)) <= propGenes
data1 <- data1[keepGenesExpr1, ]
genesRetained <- dim(data1)[1]
message(paste(genesRetained, " genes retained in dataset"))
# plot powers to work out soft-power
# Choose a set of soft-thresholding powers
powers <- c(c(1:10), seq(from = 12, to=20, by=2))
# Call the network topology analysis function
sft <- pickSoftThreshold(t(data1), powerVector=powers, verbose=5)
# Plot the results:
png(outFile, width=180, height=125, units="mm", res=150)
par(mfrow = c(1,2))
cex1 <- 0.9
# Scale-free topology fit index as a function of the soft-thresholding power
plot(sft$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$fitIndices[,2],
labels=powers, cex=cex1, col="red")
# this line corresponds to using an R^2 cut-off of h
abline(h=0.80, col="red")
# Mean connectivity as a function of the soft-thresholding power
plot(sft$fitIndices[,1], sft$fitIndices[,5],
xlab="Soft Threshold (power)",
ylab="Mean Connectivity", type="n",
main = paste("Mean connectivity"))
text(sft$fitIndices[,1], sft$fitIndices[,5],
labels=powers, cex=cex1, col="red")
dev.off()
}
#' @param data1 data.frame of normalised gene expression values with samples as columns and rows as genes.
#' @param propGenes A numeric value indicating the proportion of most variable genes to be retained in the analysis between 0 and 1.
#' @param softPower Integer. The soft thresholding power used to construct the WGCNA network. This can be determined using the determineSoftPowerWGCNA function.
#' @param signedNetwork Logical. Keep track of the sign of the correlation in network construction?
#' @return A list of 3. data1 is a data.frame of the input data after filtering. geneTreeA1 is the gene tree constructed by WGCNA. dissTOMA1 is the distance matirx.
#' @description A wrapper function for constructing a WGCNA network
runWGCNA <- function(data1, propGenes, softPower=10, signedNetwork=TRUE){
options(stringsAsFactors = FALSE)
type <- ifelse(test=signedNetwork==TRUE, yes="signed", no="unsigned")
# Remove bad genes (missing values, ...)
nGenesInput <- dim(data1)[1]
data1 <- data1[goodSamplesGenes(datExpr=t(data1))$goodGenes, ]
nGenesGood <- dim(data1)[1]
nGenesRemoved <- nGenesInput - nGenesGood
message(paste(nGenesRemoved, " genes filtered from dataset"))
propGenes <- round(propGenes * dim(data1)[1])
# Filter genes based in variance
keepGenesExpr1 <- rank(-rowVars(data1)) <= propGenes
data1 <- data1[keepGenesExpr1, ]
genesRetained <- dim(data1)[1]
message(paste(genesRetained, " genes retained in dataset"))
# Run WGCNA on the datasets
datExprA1g <- data1
adjacencyA1 <- adjacency(t(datExprA1g),power=softPower,type=type)
diag(adjacencyA1) <- 0
dissTOMA1 <- 1-TOMsimilarity(adjacencyA1, TOMType=type)
geneTreeA1 <- flashClust(as.dist(dissTOMA1), method="average")
# Return the relevant objects
return(list(data1 = data1, geneTreeA1 = geneTreeA1,
dissTOMA1 = dissTOMA1))
}
plotModulesCut <- function(referenceDataset, outFile, minClusterSize=30){
# Seperate list objects
mColorh=NULL
for (ds in 0:3){
tree <- cutreeHybrid(dendro = referenceDataset[["geneTreeA1"]],
pamStage=FALSE, minClusterSize = minClusterSize,
cutHeight = 0.99, deepSplit = ds,
distM = referenceDataset[["dissTOMA1"]])
mColorh <- cbind(mColorh, labels2colors(tree$labels))
}
pdf(outFile, height=10, width=25)
plotDendroAndColors(referenceDataset[["geneTreeA1"]], mColorh,
paste("dpSplt =",0:3), main = "",
dendroLabels=FALSE)
dev.off()
}
calculateModuleEigengenes <- function(referenceDataset, split, minClusterSize=30){
# Seperate list objects
mColorh=NULL
for (ds in 0:3){
tree <- cutreeHybrid(dendro = referenceDataset[["geneTreeA1"]],
pamStage=FALSE, minClusterSize = minClusterSize,
cutHeight = 0.99, deepSplit = ds,
distM = referenceDataset[["dissTOMA1"]])
mColorh <- cbind(mColorh, labels2colors(tree$labels))
}
modulesA1 <- mColorh[ ,split] # (Chosen based on plot)
PCs <- moduleEigengenes(t(referenceDataset$data1), colors=modulesA1)
ME <- PCs$eigengenes
rownames(ME) <- colnames(referenceDataset$data1)
ME
}
geneModuleColors <- function(referenceDataset, split, minClusterSize=30){
# Seperate list objects
mColorh=NULL
for (ds in 0:3){
tree <- cutreeHybrid(dendro = referenceDataset[["geneTreeA1"]],
pamStage=FALSE, minClusterSize = minClusterSize,
cutHeight = 0.99, deepSplit = ds,
distM = referenceDataset[["dissTOMA1"]])
mColorh <- cbind(mColorh, labels2colors(tree$labels))
}
modules <- mColorh[ ,split] # (Chosen based on plot)
modules
}
moduleHubGenes <- function(referenceDataset, MEs, nGenes, split=1){
message("ranking genes")
kMEs <- signedKME(datExpr=t(referenceDataset$data1), datME=MEs)
# rank the genes for each module on kMEs
rankGenes <- function(x){
kMErank <- rank(-kMEs[ ,x])
genes <- rownames(kMEs)
genes <- genes[order(kMErank)]
genes[1:nGenes]
}
topGenes <- lapply(1:ncol(kMEs), rankGenes)
# Get the top results in a data.frame
topGenes <- do.call(cbind, topGenes)
colnames(topGenes) <- substr(colnames(kMEs), start=4, stop=30)
return(topGenes)
}
moduleMembership <- function(referenceDataset, MEs, split=1, kME_threshold=0.7){
message("calculating kME's")
kMEs <- signedKME(datExpr=t(referenceDataset$data1), datME=MEs)
# Get gene names with kME > kME_threshold
modGenes <- function(x){
rownames(kMEs[abs(kMEs[ ,x]) > kME_threshold, ])
}
geneLists <- lapply(X=1:ncol(kMEs), FUN=modGenes)
names(geneLists) <- substr(colnames(kMEs), start=4, stop=20)
return(geneLists)
}
presStatsWGCNA <- function(referenceDataset, data2, colors, split=2, type="signed",
nPermutations=100, minClusterSize=30,
greyName="grey", qVal=FALSE){
# Remove bad genes (missing values, ...)
data2 <- data2[goodSamplesGenes(datExpr=t(data2))$goodGenes, ]
# # Seperate list objects
# mColorh=NULL
# for (ds in 0:3){
# tree <- cutreeHybrid(dendro = referenceDataset[["geneTreeA1"]],
# pamStage=FALSE, minClusterSize = minClusterSize,
# cutHeight = 0.99, deepSplit = ds,
# distM = referenceDataset[["dissTOMA1"]])
#
# mColorh <- cbind(mColorh, labels2colors(tree$labels))
# }
#
# modulesA1 <- mColorh[ ,split] # (Chosen based on plot)
geneModules <- cbind(rownames(referenceDataset$data1), colors)
### Quantify module preservation
data1 <- referenceDataset[["data1"]]
multiExpr <- list(A1=list(data=t(data1)),
A2=list(data=t(data2)))
multiColor <- list(A1 = colors)
mp <- modulePreservation(multiData=multiExpr, multiColor=multiColor,
referenceNetworks=1, verbose=3,
calculateQvalue=qVal,
networkType=type,
nPermutations=nPermutations,
maxGoldModuleSize=100,
greyName=greyName
#maxModuleSize=400
)
stats <- mp$preservation$Z$ref.A1$inColumnsAlsoPresentIn.A2
stats <- stats[order(-stats[,2]), ]
print(stats[ ,1:3])
return(list(mp = mp, geneModules = geneModules))
}
moduleTraitCorrelations <- function(MEs, trait){
# Order traits based on clustering tree
d <- dist(x=t(trait))
traitTree <- hclust(d, method="a")
plot(traitTree, xlab="", ylab="", main="", sub="")
traitOrder <- traitTree$labels[traitTree$order]
trait <- trait[ ,match(traitOrder, colnames(trait))]
# Order modules based on clustering tree
d <- dist(x=t(MEs))
modTree <- hclust(d, method="a")
plot(modTree, xlab="", ylab="", main="", sub="")
modOrder <- modTree$labels[modTree$order]
MEs <- MEs[ ,match(modOrder, colnames(MEs))]
# Calculate module-trait correlations
moduleTraitCor <- cor(x=trait, y=MEs, use="pairwise.complete")
moduleTraitPvalue <- corPvalueStudent(moduleTraitCor, nrow(MEs))
return(list(moduleTraitCor=moduleTraitCor,
moduleTraitPvalue=moduleTraitPvalue))
}
plotModuleTraitCorrelations <- function(eigenTraitCor, pThresh, outFile){
moduleTraitCor <- eigenTraitCor[["moduleTraitCor"]]
moduleTraitPvalue <- eigenTraitCor[["moduleTraitPvalue"]]
## *** Experimental: organse traits by clustering correlation values
d <- dist(x=moduleTraitCor)
traitTree <- hclust(d, method="a")
d <- dist(x=t(moduleTraitCor))
moduleTree <- hclust(d, method="a")
moduleTraitCor <- moduleTraitCor[traitTree$order, moduleTree$order]
moduleTraitPvalue <- moduleTraitPvalue[traitTree$order, moduleTree$order]
# Put the p-values in a text matrix for plotting
textMatrix <- paste(signif(moduleTraitCor, 2), "\n(",
signif(moduleTraitPvalue, 1), ")", sep = "")
# Change text matrix to only contain significant p-values
textMatrix <- signif(moduleTraitPvalue, 1)
textMatrix[textMatrix >= pThresh] <- ""
dim(textMatrix) <- dim(moduleTraitCor)
# Reorder the the plot by dendrogram of correlation values
# Display the correlation values within a heatmap plot
myColRamp <- colorRampPalette(colors=c("#1F8A70", "#FFFFFF", "#FD7400"))
# Graphical parameters
par(mar = c(6, 8.5, 3, 3), cex.lab=0.5)
modSymbols <- convertColorToLabel(substr(x=colnames(moduleTraitCor), 3, 30), prefix="M")
pdf(outFile)
labeledHeatmap(Matrix=moduleTraitCor,
xColorLabels=TRUE,
xLabels=colnames(moduleTraitCor),
yLabels=rownames(moduleTraitCor),
xSymbols=modSymbols,
textMatrix=textMatrix,
colors=myColRamp(50),
main=paste("Gene Module - Clinical Variable Correlations"),
cex.text=0.5,
cex.lab.y=0.5,
cex.lab.x=0.7)
dev.off()
}
# Convert module colors to module names (so thing look more professional)
convertColorToLabel <- function(colors, n=100, prefix="mod"){
# Get the WGCNA colors
cols <- standardColors(n)
labels <- 1:n
labels <- paste(prefix, labels, sep="")
colToLabel <- data.frame(colors = cols, labels = labels)
# Add grey as label 0
grey <- c("grey", paste(prefix, "0", sep=""))
colToLabel <- rbind(grey, colToLabel)
# Function to return corresponding label for a given color
convertFunc <- function(x){
colToLabel[colToLabel$colors == x, ]$labels
}
# Apply the function to a list of colors
unlist(lapply(colors, convertFunc))
}