WGCNA: plotNetworkHeatmap – R documentation

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WGCNA

plotNetworkHeatmap

Network heatmap plot

Description

Network heatmap plot.

Usage

plotNetworkHeatmap(
  datExpr, 
  plotGenes, 
  weights = NULL,
  useTOM = TRUE, 
  power = 6, 
  networkType = "unsigned", 
  main = "Heatmap of the network")

Arguments

`datExpr`	a data frame containing expression data, with rows corresponding to samples and columns to genes. Missing values are allowed and will be ignored.
`plotGenes`	a character vector giving the names of genes to be included in the plot. The names will be matched against `names(datExpr)`.
`weights`	optional observation weights for `datExpr` to be used in correlation calculation. A matrix of the same dimensions as `datExpr`, containing non-negative weights. Only used with Pearson correlation.
`useTOM`	logical: should TOM be plotted (`TRUE`), or correlation-based adjacency (`FALSE`)?
`power`	soft-thresholding power for network construction.
`networkType`	a character string giving the newtork type. Recognized values are (unique abbreviations of) `"unsigned"`, `"signed"`, and `"signed hybrid"`.
`main`	main title for the plot.

Details

The function constructs a network from the given expression data (selected by plotGenes) using the soft-thresholding procedure, optionally calculates Topological Overlap (TOM) and plots a heatmap of the network.

Note that all network calculations are done in one block and may fail due to memory allocation issues for large numbers of genes.

Value

None.

Author(s)

Steve Horvath

References

Bin Zhang and Steve Horvath (2005) "A General Framework for Weighted Gene Co-Expression Network Analysis", Statistical Applications in Genetics and Molecular Biology: Vol. 4: No. 1, Article 17