This function first adds ChIP-seq signals along all regions of motif location
using the function addCovToGR. Than it calculates the
correlation of coverage for each input pair using the function
addCovCor. The Pearson correlation coefficient is added as new
metadata column to the input interactions. Note, this function does not work
on windows because reading of bigWig files is currently not supported on
windows.
addCor(gi, bwFile, name = "chip", window = 1000, binSize = 1)
| gi |
|
|---|---|
| bwFile | File path or connection to BigWig file with ChIP-seq signals. |
| name | Character indicating the sample name. |
| window | Numeric scalar for window size around the center of ranges in
|
| binSize | Integer scalar as size of bins to which the coverage values are combined. |
An GInteractions object like gi
with a new metadata column colname holding Pearson correlation
coefficient of ChIP-seq signals for each anchor pair.
if (.Platform$OS.type != "windows") { # use example bigWig file of ChIP-seq signals on human chromosome 22 exampleBigWig <- system.file("extdata", "GM12878_Stat1.chr22_1-30000000.bigWig", package = "sevenC") # use example CTCF moitf location on human chromosome 22 motifGR <- sevenC::motif.hg19.CTCF.chr22 # build candidate interactions gi <- prepareCisPairs(motifGR) # add ChIP-seq signals correlation gi <- addCor(gi, exampleBigWig) # use an alternative metadata column name for ChIP-seq correlation gi <- addCor(gi, exampleBigWig, name = "Stat1") # add ChIP-seq correlation for signals signals in windows of 500bp around # motif centers gi <- addCor(gi, exampleBigWig, window = 500) # add ChIP-seq correlation for signals in bins of 10 bp gi <- addCor(gi, exampleBigWig, binSize = 10) }