This mornings post is an summery of the work I did on Friday and finished up over the weekend. There was a lot of coding and great visualizations! Lauren gave us a WGCNA tutorial. Below is the R code.

source(“http://bioconductor.org/biocLite.R”)
biocLite(“impute”)
install.packages(“WGCNA”)

library(WGCNA);
# The following setting is important, do not omit.
options(stringsAsFactors = FALSE);
#Read in the female liver data set
# Take a quick look at what is in the data set:
dim(SSData);
names(SSData);

datExpr0 = as.data.frame(t(SSData

));
names(datExpr0) = SSData$Contig; rownames(datExpr0) = names(SSData) ; datExpr0 datExpr=datExpr0 gsg = goodSamplesGenes(datExpr0, verbose = 3); gsg$allOK
collectGarbage();

powers = c(c(1:10), seq(from = 12, to=20, by=2))
# Call the network topology analysis function
sft = pickSoftThreshold(datExpr, powerVector = powers, verbose = 5)
# 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”);
# this line corresponds to using an R^2 cut-off of h
abline(h=0.90,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”)

net = blockwiseModules(datExpr, power = 10,
TOMType = “unsigned”, minModuleSize = 9,
reassignThreshold = 0, mergeCutHeight = 0.25,
numericLabels = TRUE, pamRespectsDendro = FALSE,
saveTOMs = TRUE,
saveTOMFileBase = “femaleMouseTOM”,
verbose = 3)

sizeGrWindow(12, 9)
# Convert labels to colors for plotting
mergedColors = labels2colors(net$colors) # Plot the dendrogram and the module colors underneath plotDendroAndColors(net$dendrograms[[1]], mergedColors[net\$blockGenes[[1]]],
“Module colors”,
dendroLabels = FALSE, hang = 0.03,
addGuide = TRUE, guideHang = 0.05)

After that we worked on finding DEG’s that were interesting and categorizing them based on function for those with a high fold number. Overall, just continuing to sort out what is happening in the massive immune response the exposed animals display.