GOstats (version 2.38.1)

compCorrGraph: A function to compute a correlation based graph from Gene Expression Data

Description

Given a set of gene expression data (an instance of the ExpressionSet class) this function computes a graph based on correlations between the probes.

Usage

compCorrGraph(eSet, k = 1, tau = 0.6)

Arguments

eSet

An instance of the ExpressionSet class.

k

The power to raise the correlations to.

tau

The lower cutoff for absolute correlations.

Value

An instance of the graph class. With edges and edge weights determined by applying the algorithm described previously.

Details

Zhou et al. describe a method of computing a graph between probes (genes) based on estimated correlations between probes. This function implements some of their methods.

Pearson correlations between probes are computed and then these are raised to the power k. Any of the resulting estimates that are less than tau in absolute value are set to zero.

References

Zhou et al., Transitive functional annotation by shortest-path analysis of gene expression data.

See Also

compGdist

Examples

 ## Create an ExpressionSet to work with
 set.seed(123)
 exprMat <- matrix(runif(50 * 5), nrow=50)
 genData <- new("ExpressionSet", exprs=exprMat)

 corrG = compCorrGraph(genData)