simulateModule: Simulate a gene co-expression module

Description

Simulation of a single gene co-expression module.

Usage

simulateModule(
  ME, 
  nGenes, 
  nNearGenes = 0, 
  minCor = 0.3, maxCor = 1, corPower = 1, 
  signed = FALSE, propNegativeCor = 0.3, 
  geneMeans = NULL,
  verbose = 0, indent = 0)

Value

A matrix containing the expression data with rows corresponding to samples and columns to genes.

Arguments

ME: seed module eigengene.
nGenes: number of genes in the module to be simulated. Must be non-zero.
nNearGenes: number of genes to be simulated with low correlation with the seed eigengene.
minCor: minimum correlation of module genes with the eigengene. See details.
maxCor: maximum correlation of module genes with the eigengene. See details.
corPower: controls the dropoff of gene-eigengene correlation. See details.
signed: logical: should the genes be simulated as belonging to a signed network? If TRUE, all genes will be simulated to have positive correlation with the eigengene. If FALSE, a proportion given by propNegativeCor will be simulated with negative correlations of the same absolute values.
propNegativeCor: proportion of genes to be simulated with negative gene-eigengene correlations. Only effective if signed is FALSE.
geneMeans: optional vector of length nGenes giving desired mean expression for each gene. If not given, the returned expression profiles will have mean zero.
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.

Author

Peter Langfelder

Details

Module genes are simulated around the eigengene by choosing them such that their (expected) correlations with the seed eigengene decrease progressively from (just below) maxCor to minCor. The genes are otherwise independent from one another. The variable corPower determines how fast the correlation drops towards minCor. Higher powers lead to a faster frop-off; corPower must be above zero but need not be integer.

If signed is FALSE, the genes are simulated so as to be part of an unsigned network module, that is some genes will be simulated with a negative correlation with the seed eigengene (but of the same absolute value that a positively correlated gene would be simulated with). The proportion of genes with negative correlation is controlled by propNegativeCor.

Optionally, the function can also simulate genes that are "near" the module, meaning they are simulated with a low but non-zero correlation with the seed eigengene. The correlations run between minCor and zero.

References

A short description of the simulation method can also be found in the Supplementary Material to the article

Langfelder P, Horvath S (2007) Eigengene networks for studying the relationships between co-expression modules. BMC Systems Biology 2007, 1:54.

The material is posted at http://horvath.genetics.ucla.edu/html/CoexpressionNetwork/EigengeneNetwork/SupplementSimulations.pdf.