GOGANPA (version 1.0)

selectRho: Choosing a threshold based on the Scale-Free-Topology-Criterion

Description

Determine the threshold parameter which will result in a network with optimal scale-free fitness.

Usage

selectRho(simMat, rhovec = NULL)

Arguments

simMat

The GO-similairty matrix. Missing and negative entries are not allowed. The gene names should be assigned to the row and column names.

rhovec

a vector of candidate thresholds, or if NULL, a set of thresholds chosen according to the range of the similarity matrix.

Value

A list, with elements:

criterion

a summary table of the candidate thresholds' resulting fits.

bestrho

The candidate threshold with the highest R-squared.

Details

The scale-free fitness measure is based on linear-regression-based R-squared goodness-of-fit measure.

References

Chang, B., Kustra, R. and Tian, WD (2012) Functional-Network-based Gene Set Analysis using Gene Ontology. Submitted.

Zhang, B. and Horvath, S. (2005) A General Framework for Weighted Gene Co-Expression Network Analysis. Statistical Applications in Genetics and Molecular Biology. 4:1:A17.

See Also

getGNET

Examples

#Not to Run
data("simMatSmall",package="GOGANPA")
fit <- selectRho(simMatSmall)
plot(fit$criterion[,1],fit$criterion[,2])
abline(v=fit$bestrho,col=2)