plotGREATenrich: Given the output from processPathways(), creates a heatmap...

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

Description

Given the output from processPathways(), creates a heatmap from the ouput of the GREAT enrichment analysis. Presence or absence of the pathway in enrichment of both type-specific (increased or decreased log2fold change, low p-value) and shared (no change, higher p-value) regulatory regions is plotted.

Usage

plotGREATenrich(input, maintitle = "GREAT Enrichment Analysis",
  pathwaycateg = NULL, test = "Binom", numshow = 10,
  maintitlesize = "20px", ylabelsize = "10px", xlabelsize = "10px",
  xlabel = NULL, subtitle = "(color corresponds to p-value)",
  subtitlesize = "13px")

Arguments

input	results from GREAT enrichment analysis
maintitle	main title (default, "GREAT Enrichment Analysis")
pathwaycateg	ontology, to see available ontologies in your input results (e.g. named GREATpathways, type getOntologies(GREATpathways)
test	character, "Binom" uses binomial test restuls, "Hyper" uses hypergeometric test results. Default is "Binom"
numshow	number of top pathways (ranked according to p-value) of each type (expt, reference, shared) to show in the plot (default=10)
maintitlesize	main title size (default, 20px)
ylabelsize	size of ylabel (default, 10px)
xlabelsize	size of xlabel (default, 10px)
xlabel	label for x-axis (default, Experiment-specific, shared, Reference-specific )
subtitle	subtitle (default, "color corresponds to p-value")
subtitlesize	subitle size (default 15px)

Value

heatmap

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(analysisresults = 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)
plotGREATenrich(pathResults, maintitle = "GREAT Enrichment Analysis",
pathwaycateg = "GO_Molecular_Function")

## End(Not run)

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