Simulation of a single gene co-expression module.
simulateModule(
ME,
nGenes,
nNearGenes = 0,
minCor = 0.3, maxCor = 1, corPower = 1,
signed = FALSE, propNegativeCor = 0.3,
geneMeans = NULL,
verbose = 0, indent = 0)seed module eigengene.
number of genes in the module to be simulated. Must be non-zero.
number of genes to be simulated with low correlation with the seed eigengene.
minimum correlation of module genes with the eigengene. See details.
maximum correlation of module genes with the eigengene. See details.
controls the dropoff of gene-eigengene correlation. See details.
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.
proportion of genes to be simulated with negative gene-eigengene correlations.
Only effective if signed is FALSE.
optional vector of length nGenes giving desired mean expression for each gene. If
not given, the returned expression profiles will have mean zero.
integer level of verbosity. Zero means silent, higher values make the output progressively more and more verbose.
indentation for diagnostic messages. Zero means no indentation, each unit adds two spaces.
A matrix containing the expression data with rows corresponding to samples and columns to genes.
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.
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://www.genetics.ucla.edu/labs/horvath/CoexpressionNetwork/EigengeneNetwork/SupplementSimulations.pdf.
simulateEigengeneNetwork for a simulation of eigengenes with a given causal structure;
simulateDatExpr for simulations of whole datasets consisting of multiple modules;
simulateDatExpr5Modules for a simplified interface to expression simulations;
simulateMultiExpr for a simulation of several related data sets.