processPathways: Enrichment analysis using GREAT package to identify putative...

In Mathelab/ALTRE: Workflow for Identifying Regulatory Elements from Chromatin Accessibility Assay Data

Description

Enrichment analysis using GREAT package to identify putative pathways of interest for further investigation

Usage

processPathways(GREATpath, pathway_category = "GO", adjustby = "bonferroni",
  test = "Binom", enrichcutoff = 2, adjpvalcutoff = 0.05)

Arguments

GREATpath	output of runGREAT()
pathway_category	character, "GO", "Pathway Data", "Regulatory Motifs", "Phenotype Data and Human Disease", "Gene Expression", "Gene Families" (default is "GO")
adjustby	character, "fdr" or "bonferroni", default is "bonferroni"
test	character, "Both" denotes hypergeometric and binomical tests are used to determine enriched pathways, "Binom" denotes binomial tests used, "Hyper" denotes hypergeometric tests are used. Default is "Binom"
enrichcutoff	numeric, fold change enrichment cutoff to determine enriched pathways, default is 2
adjpvalcutoff	numeric, Bonferroni adjusted p-value cutoff to determine enriched pathways, default is 0.05

Value

list of dataframes for enriched pathways - each dataframe in the list represents one pathway type (e.g. "GO Molecular Function")

Examples

## Not run: 
csvfile <- loadCSVFile("DNaseEncodeExample.csv")
samplePeaks <- loadBedFiles(csvfile)
consensusPeaks <- getConsensusPeaks(samplepeaks = samplePeaks, minreps = 2)
TSSannot <- getTSS()
consensusPeaksAnnotated <- combineAnnotatePeaks(conspeaks = consensusPeaks,
                                          TSS = TSSannot,
                                          merge = TRUE,
                                          regionspecific = TRUE,
                                          distancefromTSSdist = 1500,
                                          distancefromTSSprox = 1000)
consensusPeaksCounts <- getCounts(annotpeaks = consensusPeaksAnnotated,
                              reference = 'SAEC',
                              sampleinfo = csvfile,
                              chrom = 'chr21')
alteredPeaks <- countanalysis(counts=consensusPeaksCounts,
                             pval=0.01,
                             lfcvalue=1)
alteredPeaksCategorized <- categAltrePeaks(alteredPeaks,
                             lfctypespecific = 1.5,
                             lfcshared = 1.2,
                             pvaltypespecific = 0.01,
                             pvalshared = 0.05)
callPaths <- runGREAT(peaks = alteredPeaksCategorized)
pathResults <- processPathways(callPaths, pathway_category = "GO",
enrichcutoff = 2, adjpvalcutoff = 0.05)

## End(Not run)

Mathelab/ALTRE documentation built on May 7, 2019, 3:41 p.m.