WGCNA: an R package for weighted correlation network analysis

 

Reference:

Overview: [PPT]

Webpage: http://www.genetics.ucla.edu/labs/horvath/CoexpressionNetwork/Rpackages/WGCNA/

Tutorials: http://www.genetics.ucla.edu/labs/horvath/CoexpressionNetwork/Rpackages/WGCNA/Tutorials/index.html

 

Prerequisites

The WGCNA package requires the following packages to be installed: stats, fields, impute, grDevices, dynamicTreeCut (1.20 or higher), qvalue, utils, and flashClust.

install.packages(c("fields", "impute", "dynamicTreeCut", "qvalue", "flashClust", "Hmisc") )

 

WGCNA installation

·  For Windows, R version 2.10.0 and higher: WGCNA_0.86.zip

·  For Windows, R version 2.9.2 and lower: WGCNA_0.86.zip

·  Source (Linux, Mac etc): WGCNA_0.86.tar.gz

·  Reference manual in pdf format

·  Quick reference: overview table of most important functions

 

1.      Download the appropriate package file and save it in a directory of your choice.

2.      At the prompt, type (replace path/to/file with the directory and filename of the downloaded package)

 

install.packages("path/to/file", repos = NULL)

 

For Mac users: add the argument

 

type = "source"

 

If you do not have sufficient privileges, you can either (1) ask your system administrator to install the packages for you, or (2) create your own personal R library.

 

install.packages("path/to/file", repos = NULL, lib = "path/to/library")

 

Note that if you chose to go this route, you will need to specify your library location every time you load the package, by using

library(package, lib.loc = "path/to/library")

 

Other installation

Install GO database for the species

The organism-speci_c packages have names of the form org.Xx.eg.db, where Xx stands for organism code, for example, Mm for mouse, Hs for human, etc

source("http://bioconductor.org/biocLite.R")

biocLite("org.Mm.eg.db")

 

 

WGCNA tutorial

Data description and download

The data are gene expression measurements from livers of female mouse of a specific F2 intercross. For a detailed description of the data and the biological implications we refer the reader to Ghazalpour et al (2006), Integrating Genetics and Network Analysis to Characterize Genes Related to Mouse Weight (link to paper; link to additional information). Note that the data set contains 3600 measured expression profiles. In addition to the expression data, several physiological quantitative traits were measured for the mice. Please download the following

and unzip them in a folder of your choice.

 

Procedure

  1. Data input and cleaning

 

Load expression data

# 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 package

library(WGCNA);

# The following setting is important, do not omit.

options(stringsAsFactors = FALSE);

#Read in the female liver data set

femData = read.csv("LiverFemale3600.csv");

# Take a quick look at what is in the data set:

dim(femData);

names(femData);

 

Remove the auxiliary data and transpose the expression data for further analysis

datExpr0 = as.data.frame(t(femData[, -c(1:8)]));

names(datExpr0) = femData$substanceBXH;

rownames(datExpr0) = names(femData)[-c(1:8)];

 

Check data for excessive missing values and identi_cation of outlier microarray

Samples

gsg = goodSamplesGenes(datExpr0, verbose = 3);

gsg$allOK

 

if (!gsg$allOK)

{

# Remove the offending genes and samples from the data:

datExpr0 = datExpr0[gsg$goodSamples, gsg$goodGenes];

}

 

      Check outliers

sampleTree = flashClust(dist(datExpr0), method = "average");

# Plot the sample tree: Open a graphic output window of size 12 by 9 inches

# The user should change the dimensions if the window is too large or too small.

sizeGrWindow(12,9)

par(cex = 0.6);

par(mar = c(0,4,2,0))

plot(sampleTree, main = "Sample clustering to detect outliers", sub="", xlab="", cex.lab = 1.5,

cex.axis = 1.5, cex.main = 2)

 

      Remove outliers

# Plot a line to show the cut

abline(h = 15, col = "red");

# Determine cluster under the line

clust = cutreeStatic(sampleTree, cutHeight = 15, minSize = 10)

table(clust)

# clust 1 contains the samples we want to keep.

keepSamples = (clust==1)

datExpr = datExpr0[keepSamples, ]

 

nGenes = ncol(datExpr)

nSamples = nrow(datExpr)

colnames(datExpr)

rownames(datExpr)

 

      Load trait data

traitData = read.csv("ClinicalTraits.csv");

dim(traitData)

names(traitData)

# remove columns that hold information we do not need.

allTraits = traitData[, -c(31, 16)];          #remove note and comment

allTraits = allTraits[, c(2, 11:36) ];

dim(allTraits)

names(allTraits)

# Form a data frame analogous to expression data that will hold the clinical traits.

femaleSamples = rownames(datExpr);

traitRows = match(femaleSamples, allTraits$Mice);

datTraits = allTraits[traitRows, -1];

rownames(datTraits) = allTraits[traitRows, 1];

collectGarbage();

 

Visualize how the clinical traits relate to the relate to the sample dendrogram

# Re-cluster samples

sampleTree2 = flashClust(dist(datExpr), method = "average")

# Convert traits to color representation ( white: low, red: high, grey: missing entry)

traitColors = numbers2colors(datTraits, signed = FALSE);

# Plot the sample dendrogram and the colors underneath.

plotDendroAndColors(sampleTree2, traitColors, groupLabels = names(datTraits),

                 main = "Sample dendrogram and trait heatmap")

 

  1. Network construction and module detection

 

Automatic, one-step network construction and module detection

net = blockwiseModules(datExpr, power = 6, minModuleSize = 30,

reassignThreshold = 0, mergeCutHeight = 0.25,

numericLabels = TRUE, pamRespectsDendro = FALSE,

saveTOMs = TRUE, saveTOMFileBase = "femaleMouseTOM", verbose = 3)

 

The structure of network

str(net)

 

To see how many modules were identifed and what the module sizes are,

table(net$color)

 

The dendrogram can be displayed together with the color assignment using the following code

# open a graphics window

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)

 

Save the module information necessary for subsequent analysis.

moduleLabels = net$colors

moduleColors = labels2colors(net$colors)

MEs = net$MEs;

geneTree = net$dendrograms[[1]];

save(MEs, moduleLabels, moduleColors, geneTree,

file = "FemaleLiver-02-networkConstruction-auto.RData")

 

  1.  Relating modules to external clinical traits

 

Compute 1st principal componet of each module as its eigengenes. Correlate eigengene external traits and look for the most significant associations.

# Define numbers of genes and samples

nGenes = ncol(datExpr);

nSamples = nrow(datExpr);

# calculate eigengenes (1st principal component) of modules

MEs0 = moduleEigengenes(datExpr, moduleColors)$eigengenes

MEs = orderMEs(MEs0)

moduleTraitCor = cor(MEs, datTraits, use = "p");

moduleTraitPvalue = corPvalueStudent(moduleTraitCor, nSamples);

 

A suitable graphical representation

# Open a graphic window with specified dimensions

sizeGrWindow(10,6)

# Will display correlations and their p-values

textMatrix = paste(signif(moduleTraitCor, 2), "\n(", signif(moduleTraitPvalue, 1), ")", sep = "");

dim(textMatrix) = dim(moduleTraitCor)

par(mar = c(6, 8.5, 3, 3));

# Display the correlation values within a heatmap plot

labeledHeatmap(Matrix = moduleTraitCor, xLabels = names(datTraits), yLabels = names(MEs), ySymbols = names(MEs), colorLabels = FALSE, colors = greenWhiteRed(50), textMatrix = textMatrix, setStdMargins = FALSE, cex.text = 0.5, zlim = c(-1,1), main = paste("Module-trait relationships"))

 

 

  1.  Interfacing network analysis with other data such as functional annotation and gene ontology

 

Get LocusLinkID(entrez ID)

# Read in the probe annotation

annot = read.csv(file = "GeneAnnotation.csv");

# Match probes in the data set to the probe IDs in the annotation file

probes = names(datExpr)

probes2annot = match(probes, annot$substanceBXH)

# Get the corresponding Locuis Link IDs

# As background in the enrichment analysis, we will use all probes in the analysis

allLLIDs = annot$LocusLinkID[probes2annot];

 

Install GO database for the species

The organism-speci_c packages have names of the form org.Xx.eg.db, where Xx stands for organism code, for example, Mm for mouse, Hs for human, etc

source("http://bioconductor.org/biocLite.R")

biocLite("org.Mm.eg.db")

 

Load GO database and do GO enrichments

library (org.Mm.eg.db)

GOenr = GOenrichmentAnalysis(moduleColors, allLLIDs, organism = "mouse", nBestP = 10);

 

The function runs for a while and returns a long list, where the most interesting component of which is an enrichment table containing the 10 best terms for each module present in moduleColors.

tab = GOenr$bestPTerms[[4]]$enrichment

names(tab)

 

Write the results into a file

write.table(tab, file = "GOEnrichmentTable.csv", sep = ",", quote = TRUE, row.names = FALSE)