getBinMatrix: Generate bins for A/B compartment estimation

In JordanVeldboom/compartmap: A/B compartment inference from ATAC-seq and methylation array data

Description

Generate bins across a user defined chromosome for A/B compartment estimation. A/B compartment estimation can be used for non-supported genomes if chr.end is set.

Usage

getBinMatrix(
  x,
  genloc,
  chr = "chr1",
  chr.start = 0,
  chr.end = NULL,
  res = 100000,
  FUN = sum,
  genome = c("hg19", "hg38", "mm9", "mm10")
)

Arguments

x	A p x n matrix where p (rows) = loci and n (columns) = samples/cells
genloc	GRanges object that contains corresponding genomic locations of the loci
chr	Chromosome to be analyzed
chr.start	Starting position (in bp) to be analyzed
chr.end	End position (in bp) to be analyzed
res	Binning resolution (in bp)
FUN	Function to be used to summarize information within a bin
genome	Genome corresponding to the input data ("hg19", "hg38", "mm9", "mm10")

Details

This function is used to generate a list object to be passed to getCorMatrix

Value

A list object to pass to getCorMatrix

Examples

library(GenomicRanges)

#Generate random genomic intervals of 1-1000 bp on chr1-22
#Modified from https://www.biostars.org/p/225520/
random_genomic_int <- data.frame(chr = rep("chr14", 100))
random_genomic_int$start <- apply(random_genomic_int, 1, function(x) { round(runif(1, 0, getSeqLengths(chr = x)[[1]]), 0) })
random_genomic_int$end <- random_genomic_int$start + runif(1, 1, 1000)
random_genomic_int$strand <- "*"

#Generate random counts
counts <- rnbinom(1000, 1.2, 0.4)

#Build random counts for 10 samples
count.mat <- matrix(sample(counts, nrow(random_genomic_int) * 10, replace = FALSE), ncol = 10)
colnames(count.mat) <- paste0("sample_", seq(1:10))

#Bin counts
bin.counts <- getBinMatrix(count.mat, makeGRangesFromDataFrame(random_genomic_int), chr = "chr14", genome = "hg19")

JordanVeldboom/compartmap documentation built on July 3, 2020, 6:32 p.m.