runGREAT: 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

runGREAT(peaks, species = "hg19", rule = "basalPlusExt", adv_upstream = 5,
  adv_downstream = 1, adv_span = 1000, adv_twoDistance = 1000,
  adv_oneDistance = 1000, pathway_category = "GO")

Arguments

peaks	list, output of categAltrePeaks() function #@param peaktype character, "Experiment Specific", "Reference Specific", # "Ambiguous", "Shared", or "All" (All is default)
species	default hg19
rule	character, "basalPlusExt", "twoClosest", "oneClosest" rule that associates genomic regions to genes (default is "basalPlusExt"). See https://bioconductor.org/packages/release/bioc/html/chipenrich.html for more detail.
adv_upstream	kb, extension to upstream (if rule is basalPlusExt), default 5
adv_downstream	kb, extension to downstream (if rule is basalPlusExt), default 1.0
adv_span	kb, max extension (if rule is basalPlusExt), default 1000.0
adv_twoDistance	kb, max extension (if rule is twoClosest), default 1000.0
adv_oneDistance	kb, max extension (if rule is oneClosest), default 1000.0
pathway_category	character, "GO", "Pathway Data", "Regulatory Motifs", "Phenotype Data and Human Disease", "Gene Expression", "Gene Families" (default is "GO")

Value

ways – pathways also annotated with additional information

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)

## End(Not run)

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