WGCNA (version 1.61)

chooseTopHubInEachModule: Chooses the top hub gene in each module

Description

chooseTopHubInEachModule returns the gene in each module with the highest connectivity, looking at all genes in the expression file.

Usage

chooseTopHubInEachModule(
   datExpr, 
   colorh, 
   omitColors = "grey", 
   power = 2, 
   type = "signed", 
   ...)

Arguments

datExpr

Gene expression data with rows as samples and columns as genes.

colorh

The module assignments (color vectors) corresponding to the rows in datExpr.

omitColors

All colors in this character vector (default is "grey") are ignored by this function.

power

Power to use for the adjacency network (default = 2).

type

What type of network is being entered. Common choices are "signed" (default) and "unsigned". With "signed" negative correlations count against, whereas with "unsigned" negative correlations are treated identically as positive correlations.

…

Any other parameters accepted by the *adjacency* function

Value

Both functions output a character vector of genes, where the genes are the hub gene picked for each module, and the names correspond to the module in which each gene is a hub.

Examples

# NOT RUN {
## Example: first simulate some data.

MEturquoise = sample(1:100,50)
MEblue      = sample(1:100,50)
MEbrown     = sample(1:100,50)
MEyellow    = sample(1:100,50) 
MEgreen     = c(MEyellow[1:30], sample(1:100,20))
MEred	    = c(MEbrown [1:20], sample(1:100,30))
MEblack	    = c(MEblue  [1:25], sample(1:100,25))
ME     = data.frame(MEturquoise, MEblue, MEbrown, MEyellow, MEgreen, MEred, MEblack)
dat1   = simulateDatExpr(ME,300,c(0.2,0.1,0.08,0.051,0.05,0.042,0.041,0.3), signed=TRUE)
TOM1   = TOMsimilarityFromExpr(dat1$datExpr, networkType="signed")
colnames(TOM1) <- rownames(TOM1) <- colnames(dat1$datExpr)
tree1 <- tree2 <- fastcluster::hclust(as.dist(1-TOM1),method="average")
colorh = labels2colors(dat1$allLabels)
hubs    = chooseTopHubInEachModule(dat1$datExpr, colorh)
hubs

# }