facopyEnrichment: Gene-set Enrichment Analysis

In facopy: Feature-based association and gene-set enrichment for copy number alteration analysis in cancer

Description

Performs gene-set enrichment analysis of cancer copy number data, based on a gene table, a facopyInfo object and the correlations between gene copy number and expression data.

Usage

facopyEnrichment(fad, geneTable, cor, outFolder, 
                 pvalThr = 0.05, corThr = 0.1, plotThr=0.001)

Arguments

fad	facopyInfo object with a certain study's facopy data.
geneTable	A table with the format of facopy function's output, containing at least the columns regarding feature name, p-value and chromosome arm.
cor	A list generated by a call to calculateCor, containing information on correlation between gene copy number and expression data.
outFolder	The folder to which to write the enrichment results.
pvalThr	Maximum p-value required to consider a gene to be significantly associated.
corThr	Minimum R2 required to consider a gene's copy number and expression to be sufficiently correlated.
plotThr	Pathways from Biocarta, KEGG and Reactome with lower p-values than this integer will have their graphs plotted in the output folder.

Details

Only use this function and calculateCor if you selected some kind of gene collection as genomic features.

Value

The input gene table is returned with correlation values attached, ordered by p-value and then by R2.

Author(s)

David Mosen-Ansorena

Examples

data(myStudy) # load example study

# then perform an association analysis
# genes = facopy(myStudy, "amp", "stage")

# calculate expression-CN correlations
# eCor = calculateCor(myStudy, "mrna_merged_median_Zscores", "coadread_tcga_pub")

# and run facopy enrichment
# facopyEnrichment(myStudy, genes, eCor, "~/myFolder/stageAmpEnrichment")

facopy documentation built on May 2, 2018, 2:30 a.m.