R: Topological overlap for a subset of a whole set of genes

subsetTOM {WGCNA}

R Documentation

Topological overlap for a subset of a whole set of genes

Description

This function calculates topological overlap of a subset of vectors with respect to a whole data set.

Usage

subsetTOM(
  datExpr, 
  subset,
  corFnc = "cor", corOptions = "use = 'p'", 
  weights = NULL,  
  networkType = "unsigned", 
  power = 6, 
  verbose = 1, indent = 0)

Arguments

`datExpr`	a data frame containing the expression data of the whole set, with rows corresponding to samples and columns to genes.
`subset`	a single logical or numeric vector giving the indices of the nodes for which the TOM is to be calculated.
`corFnc`	character string giving the correlation function to be used for the adjacency calculation. Recommended choices are `"cor"` and `"bicor"`, but other functions can be used as well.
`corOptions`	character string giving further options to be passed to the correlation function.
`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.
`networkType`	character string giving network type. Allowed values are (unique abbreviations of) `"unsigned"`, `"signed"`, `"signed hybrid"`. See `adjacency`.
`power`	soft-thresholding power for network construction.
`verbose`	integer level of verbosity. Zero means silent, higher values make the output progressively more and more verbose.
`indent`	indentation for diagnostic messages. Zero means no indentation, each unit adds two spaces.

Details

This function is designed to calculated topological overlaps of small subsets of large expression data sets, for example in individual modules.

Value

A matrix of dimensions n*n, where n is the number of entries selected by block.

Author(s)

Peter Langfelder

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