countanalysis: Determines which regulatory regions are signifigantly altered...

In ewymathe/testALTREinstall: Workflow for Identifying Regulatory Elements from Chromatin Accessibility Assay Data

Description

Altered regions are those that show differences in chromatin accessibility (using DESeq2 algorithm)

Usage

countanalysis(counts, pval = 0.01, lfcvalue = 1)

Arguments

counts	counts for each region
pval	optional, pvalue considered significant (0.05, 0.01, etc.)
lfcvalue	optional, logfold change value considered significant (value reported on a log scale base 2 so log2fold change of 1.5 means difference in peaks increased by 2^1.5 or 2.8)

Value

list containing: 1) DESeq2 results table 2) some statistics 3) data.frame used for plotting

Examples

## Not run: 
dir <- system.file('extdata', package='ALTRE', mustWork=TRUE)
csvfile <- file.path(dir, 'lung.csv')
sampleinfo <- loadCSVFile(csvfile)
samplePeaks <- loadBedFiles(sampleinfo)
consPeaks <- getConsensusPeaks(samplepeaks=samplePeaks,minreps=2)
TSSannot <- getTSS()
consPeaksAnnotated <- combineAnnotatePeaks(conspeaks = consPeaks,
                                          TSS = TSSannot,
                                          merge = TRUE,
                                          regionspecific = TRUE,
                                          mergedistenh = 1500,
                                          mergedistprom = 1000)
counts_consPeaks <- getCounts(annotpeaks = consPeaksAnnotated,
                              sampleinfo=sampleinfo,
                              reference = 'SAEC')
altre_peaks <- countanalysis(counts=counts_consPeaks,
                             pval=0.01,
                             lfcvalue=1)

## End(Not run)

ewymathe/testALTREinstall documentation built on May 16, 2019, 9:42 a.m.