BinQuasi (version 0.1-6)

count.table: Create a matrix of ChIP-seq count data

Description

Create a matrix of ChIP-seq count data from sorted bam files using a non-overlapping genomic partition. Used within the main peak calling function, BQ.

Usage

count.table(dir, ChIP.files, control.files, bin.size = NULL,
  frag.length = NULL, minimum.count = 20)

Arguments

dir

Directory where the sorted bam files (and their corresponding bam indices) are saved.

ChIP.files

File names (with file extensions) of the ChIP sample files in sorted bam format.

control.files

File names (with file extensions) of the input/control sample files in sorted bam format.

bin.size

Window size, constant across all samples, used to generate a non-overlapping partition for counts. If NULL, an estimate will be used (see details).

frag.length

Average length of the ChIP fragments in each sample provided. Reads are extended to this length from their 3' ends. If NULL, cross correlation will be used to estimate the fragment length of each sample (see details).

minimum.count

The count threshold used for filtering out windows with sparse counts. Any genomic window with counts less than this value across all samples will be removed.

Value

A list containing:

counts

Data frame with rows corresponding to genomic windows and columns for the chromosomes, start and end locations, as well as a column for the counts of each sample.

bin.size

The bin size used to create the genomic partition.

fragment.length

Vector of the fragment lengths used to extend the reads in each sample.

filter

Count threshold used to create the counts data frame. Windows with counts summed across all samples that fall below this value were removed.

Details

This function creates a count table of ChIP sequencing data (supplied as sorted bam files) using a non-overlapping partition across the genome.

The fragment length (if not provided) is estimated using the cross-correlation method of Ramachandran et al (2013). A fragment length is estimated for each sample, after removing duplicate reads, by taking the average over all chromosomes in the sample. Estimation is performed at 5 bp resolution and restricted to a minimum fragment length of 50 bp and maximum of 600 bp.

The bin size (if not provided) is selected using a procedure by Shimazaki and Shinomoto (2007) based on minimizing the mean-integrated squared error for a time-dependent Poisson point process. This procedure is applied to each ChIP sample (at 5 bp resolution, restricted to a minimum of 50 bp and maximum of 1000 bp), and the minimum across all ChIP samples is returned as the bin size.

For a given sample and window, the count is determined as the number of fragments overlapping the window.

References

Shimazaki and Shinomoto (2007) "A method for selecting the bin size of a time histogram" Neural computation, 19(6), 1503-27.

Ramachandran, Palidwor, Porter, and Perkins (2013) "MaSC: mappability-sensitive cross-correlation for estimating mean fragment length of single-end short-read sequencing data" Bioinformatics 29(4), 444-50.

Examples

# NOT RUN {
fpath <- paste0(system.file(package = 'BinQuasi'), '/extdata/')
d <- count.table(dir = fpath,
                 ChIP.files = c('C1.bam', 'C2.bam'),
                 control.files = c('I1.bam', 'I2.bam'),
                 bin.size = 60, frag.length = c(101, 300, 150, 10),
                 minimum.count = 20)
                 head(d$counts)
# }
# NOT RUN {

# }

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