# GOing GOing GOne…

To be honest I’m not entirely sure what happened today.

We began our day in the computer lab looking at our transcriptome using Weighted Correlation Network Analysis (WGCNA), an R package that looks at patterns of co-expression to see possible groupings of similarly behaving genes.

The R script we used looks as follows:

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

1

));
names(datExpr0) = SSData$Contig; rownames(datExpr0) = names(SSData) 1 ; datExpr0 datExpr=datExpr0 gsg = goodSamplesGenes(datExpr0, verbose = 3); gsg$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)

This gives us a dendrogram of our transcriptome, grouping our genes into modules.

Try as I might, today’s attempts to do more with IPython were a bit pathetic…