alleleCounts: Method alleleCounts
In InesdeSantiago/align_parser: BaalChIP: Bayesian analysis of allele-specific transcription factor binding in cancer genomes

Description Usage Arguments Details Value Note Author(s) See Also Examples

Method alleleCounts

Generates allele-specific read count data from each BAM ChIP-seq dataset for each variant.

alleleCounts(.Object, min_base_quality = 10, min_mapq = 15,
  verbose = TRUE)

## S4 method for signature 'BaalChIP'
alleleCounts(.Object, min_base_quality = 10,
  min_mapq = 15, verbose = TRUE)

`.Object`	An object of the `BaalChIP` class.
`min_base_quality`	A numeric value indicating the minimum read base quality below which the base is ignored when summarizing pileup information (default 10).
`min_mapq`	A numeric value indicating the minimum mapping quality (MAPQ) below which the entire read is ignored (default 15).
`verbose`	logical. If TRUE reports extra information on the process

Utilizes the information within the samples slot of a BaalChIP object. Will primarily find all variants overlapping peaks. Then, for each variant, computes the number of reads carrying the reference (REF) and alternative (ALT) alleles.

An updated BaalChIP object with the slot alleleCounts containing a list of GRanges objects.

BaalChIP computes allelic counts at each variant position with Rsamtools pileup function. The algorithm follows pileup::Rsamtools by automatically excluding reads flagged as unmapped, secondary, duplicate, or not passing quality controls.

Ines de Santiago

BaalChIP.get

setwd(system.file('test',package='BaalChIP'))
samplesheet <- 'exampleChIP.tsv'
hets <- c('MCF7'='MCF7_hetSNP.txt', 'GM12891'='GM12891_hetSNP.txt')
res <- BaalChIP(samplesheet=samplesheet, hets=hets)
res <- alleleCounts(res, min_base_quality=10, min_mapq=15)

#retrieve alleleCounts:
counts <- BaalChIP.get(res, 'alleleCountsPerBam')

#alleleCounts are grouped by bam_name and group_name:
names(counts)
names(counts[['MCF7']])

#check out the result for one of the bam files:
counts[['MCF7']][[1]]