6_network_visualization.R

#=====================================================================================
#
#  Code chunk 1
#
#=====================================================================================


# Display the current working directory
getwd();
# If necessary, change the path below to the directory where the data files are stored. 
# "." means current directory. On Windows use a forward slash / instead of the usual \.
workingDir = ".";
setwd(workingDir); 
# Load the WGCNA package
library(WGCNA)
# The following setting is important, do not omit.
options(stringsAsFactors = FALSE);
# Load the expression and trait data saved in the first part
lnames = load(file = "FemaleLiver-01-dataInput.RData");
#The variable lnames contains the names of loaded variables.
lnames
# Load network data saved in the second part.
lnames = load(file = "FemaleLiver-02-networkConstruction-auto.RData");
lnames
nGenes = ncol(datExpr)
nSamples = nrow(datExpr)


#=====================================================================================
#
#  Code chunk 2
#
#=====================================================================================


# Calculate topological overlap anew: this could be done more efficiently by saving the TOM
# calculated during module detection, but let us do it again here.
dissTOM = 1-TOMsimilarityFromExpr(datExpr, power = 6);
# Transform dissTOM with a power to make moderately strong connections more visible in the heatmap
plotTOM = dissTOM^7;
# Set diagonal to NA for a nicer plot
diag(plotTOM) = NA;
# Call the plot function
sizeGrWindow(9,9)
TOMplot(plotTOM, geneTree, moduleColors, main = "Network heatmap plot, all genes")


#=====================================================================================
#
#  Code chunk 3
#
#=====================================================================================


nSelect = 400
# For reproducibility, we set the random seed
set.seed(10);
select = sample(nGenes, size = nSelect);
selectTOM = dissTOM[select, select];
# There's no simple way of restricting a clustering tree to a subset of genes, so we must re-cluster.
selectTree = hclust(as.dist(selectTOM), method = "average")
selectColors = moduleColors[select];
# Open a graphical window
sizeGrWindow(9,9)
# Taking the dissimilarity to a power, say 10, makes the plot more informative by effectively changing 
# the color palette; setting the diagonal to NA also improves the clarity of the plot
plotDiss = selectTOM^7;
diag(plotDiss) = NA;
TOMplot(plotDiss, selectTree, selectColors, main = "Network heatmap plot, selected genes")


#=====================================================================================
#
#  Code chunk 4
#
#=====================================================================================


# Recalculate module eigengenes
MEs = moduleEigengenes(datExpr, moduleColors)$eigengenes
# Isolate weight from the clinical traits
weight = as.data.frame(datTraits$weight_g);
names(weight) = "weight"
# Add the weight to existing module eigengenes
MET = orderMEs(cbind(MEs, weight))
# Plot the relationships among the eigengenes and the trait
sizeGrWindow(5,7.5);
par(cex = 0.9)
plotEigengeneNetworks(MET, "", marDendro = c(0,4,1,2), marHeatmap = c(3,4,1,2), cex.lab = 0.8, xLabelsAngle
= 90)


#=====================================================================================
#
#  Code chunk 5
#
#=====================================================================================


# Plot the dendrogram
sizeGrWindow(6,6);
par(cex = 1.0)
plotEigengeneNetworks(MET, "Eigengene dendrogram", marDendro = c(0,4,2,0),
                      plotHeatmaps = FALSE)
# Plot the heatmap matrix (note: this plot will overwrite the dendrogram plot)
par(cex = 1.0)
plotEigengeneNetworks(MET, "Eigengene adjacency heatmap", marHeatmap = c(3,4,2,2),
                      plotDendrograms = FALSE, xLabelsAngle = 90)