functional.enrichment: Functional enrichment analyses for genes partitioned by...
In vdumeaux/mixtR: Matched Interaction across Tissues (MIxT) data analysis

Description Usage Arguments Value

Enrichemnt is estimated by the hypergeometric minimum-likelihood p-values, computed with the function <e2><80><98>dhyper<e2><80><99> (equivalent to one-sided Fisher exact test). P-values are then adjusted for multiple testing.

1 2	functional.enrichment(mixt.ranksum, tissue, cohort.name = "all", functional.groups, p.adjust.method = "BH", mc.cores = 2)

`mixt.ranksum`	Output of sig.ranksum()
`tissue`	character string that provides the name of tissue of interest
`cohort.name`	character string that provides the name of the patient cohort to analyze, default is set to 'all'
`functional.groups`	a list of length 2: each object is a named vector of gene set appartenance for each tissue.
`p.adjust.method`	correction method. See '?stats::p.adjust'
`mc.cores`	number of cores to use

The output is a list containing the following objects

`results`	data frame of enrichment results for each gene set/module. Enrichment is estimated for all genes in the gene set/module and for genes that are positively (up genes) or negatively (dn genes) correlated with the patient ranksum. Number of genes in common with signature are provided for all, up, and down gene groups.
`updn.common`	names of genes in common between gene set/module and functional signature.
`up.common`	names of genes in common between up genes in gene set/module and functional signature.
`up.common`	names of genes in common between dn genes in gene set/module and functional signature.