graphTheory: Graph theory to test associations between two or more...

In PCpheno: Phenotypes and cellular organizational units

Description

Graph theory approach associated with a permutation test to evaluate whether the number of associations is unexpectedly large.

Usage

graphTheory(genename, interactome, perm)

Arguments

genename	A vector a gene names that are associated with a particular phenotype
interactome	A binary matrix composed of genes (rows) and biological complexes (columns) (see package ScISI)
perm	Numeric, number of permutation run

Details

We form two distinct graphs where the set of nodes are the proteins (genes) in the organism. In one graph we create edges between genes if the two genes are members of one, or more, protein complexes. In the second graph we create an edge between all genes that are associated to a particular phenotype. We then construct a third graph on the same node set, but where there is an edge in this graph only if there is an edge in both of the first to graphs. We count the number of edges in the third and test by permutation whether the number of edges is unexpectedly large.

Value

The returned value is a list with components:

edgeCount	Number of associations observed between the genes that are linked to a particular phenotype and the given interactome.
edgeSimul	Number of associations if the genes that are linked to a particular phenotype are randomly distributed across the given interactome
p.value	Returned p.value

Author(s)

R. Gentleman and N. LeMeur

References

Balasubramanian, R., LaFramboise, T., Scholtens, D., Gentleman, R. (2004) A graph-theoretic approach to testing associations between disparate sources of functional genomics data.Bioinformatics,20(18),3353-3362.

Examples

data(ScISI)
data(essglist)
ans <- graphTheory(names(essglist), ScISI, perm=3)

PCpheno documentation built on Nov. 8, 2020, 5:10 p.m.