neighborCounts: Load Hi-C interaction counts

In LTLA/diffHic: Differential Analyis of Hi-C Data

Load Hi-C interaction counts

Description

Collate count combinations for interactions between pairs of bins across multiple Hi-C libraries.

Usage

neighborCounts(files, param, width=50000, filter=1L, flank=NULL, exclude=NULL)

Arguments

files	a character vector containing paths to the index files generated from each Hi-C library
param	a pairParam object containing read extraction parameters
width	an integer scalar specifying the width of each square in base pairs
filter	an integer scalar specifying the minimum count for each square
flank	an integer scalar, specifying the number of bins to consider as the local neighborhood
exclude	an integer scalar, specifying the number of bins to exclude from the neighborhood

Details

This function combines the functionality of squareCounts and enrichedPairs. The idea is to allow counting of neighborhoods when there is insufficient memory to load all bin pairs with filter=1L in squareCounts. Here, the interaction space around each bin pair is examined as the counts are loaded for that bin pair, avoiding the need to hold the entire interaction space at once. Only the counts and local enrichment values for those bin pairs with row sums above filter are reported to save memory. The returned assay matrices are equivalent to that computed with enrichedPairs with the default settings.

Value

An InteractionSet object is returned with the number of read pairs for each bin pair across all libraries. For each bin pair, the number of read pairs in each neighborhood region is also returned in separate assay fields. mcols contains the size of each neighborhood in terms of the number of bin pairs.

Author(s)

Aaron Lun

References

Rao S et al. (2014). A 3D map of the human genome at kilobase resolution reveals principles of chromatin looping. Cell. 159, 1665-1690.

See Also

squareCounts, enrichedPairs

Examples

hic.file <- system.file("exdata", "hic_sort.bam", package="diffHic")
cuts <- readRDS(system.file("exdata", "cuts.rds", package="diffHic"))
param <- pairParam(fragments=cuts)

# Setting up the parameters
fout <- tempfile(fileext=".h5")
invisible(preparePairs(hic.file, param, file=fout))

# Collating to count combinations.
y <- neighborCounts(fout, param, width=50, filter=2, flank=5)
y

LTLA/diffHic documentation built on April 1, 2024, 7:21 a.m.