crossCorrByShift: Calculate cross strand correlation using the read shift...

In jrboyd/peakrefine: Peak Refinement Metric Gathering and Evalutation by Motif Enrichment

Description

Calculate cross strand correlation using the read shift method.

Usage

crossCorrByShift(bam_file, query_gr, n_regions = 20, max_dupes = 1,
  frag_min = 0, frag_max = 250, step = 10, small_step = 1,
  include_plots = TRUE)

Arguments

bam_file	character. Path to .bam file, must have index at .bam.bai.
query_gr	GRanges. Regions to calculate cross correlation for.
n_regions	integer. query_gr will be downsampled to this many regions for speed. Use NA to skip downsampling.
max_dupes	integer. Duplicate reads above this value will be removed.
frag_min	integer. extension value to start at.
frag_max	integer. extension value to end at.
step	integer. proceed from frag_min measuring correlation every step.
small_step	integer. after measuring correlation every step, a second round of fragment size refinement is done using small_step within +/- step of maximum.
include_plots	logical. Should plots be included in output?

Value

data.table of metrics

Examples

bam_file = system.file("extdata", "MCF10A_CTCF.random5.bam", package = "peakrefine")
np = system.file("extdata", "MCF10A_CTCF.random5.narrowPeak", package = "peakrefine")
qgr = rtracklayer::import(np, format = "narrowPeak")
crossCorrByShift(bam_file, qgr[1:2], frag_min = 50,
frag_max = 250, step = 50, small_step = 10)

jrboyd/peakrefine documentation built on July 30, 2020, 7:13 p.m.