WGCNA (version 1.68)

chooseOneHubInEachModule: Chooses a single hub gene in each module

Description

chooseOneHubInEachModule returns one gene in each module with high connectivity, given a number of randomly selected genes to test.

Usage

chooseOneHubInEachModule(
   datExpr, 
   colorh,  
   numGenes = 100, 
   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.

numGenes

Th number of random genes to select per module. Higher number of genes increases the accuracy of hub selection but slows down the function.

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$allLabels)
hubs    = chooseOneHubInEachModule(dat1$datExpr, colorh)
hubs

# }