chooseOneHubInEachModule: Chooses a single hub gene in each module

In WGCNA: Weighted Correlation Network Analysis

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 columns 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.

Author(s)

Jeremy Miller

Examples

## 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

WGCNA documentation built on Sept. 18, 2024, 5:08 p.m.