plotAB: Plots A/B compartment estimates on a per chromosome basis

In biobenkj/compartmap: Higher-order chromatin domain inference in single samples from methylation arrays and single cells from scRNA-seq and scATAC-seq

Plots A/B compartment estimates on a per chromosome basis

Description

Plot A/B compartments bins

Usage

plotAB(
  x,
  chr = NULL,
  what = "score",
  main = "",
  ylim = c(-1, 1),
  unitarize = FALSE,
  reverse = FALSE,
  top.col = "deeppink4",
  bot.col = "grey50",
  with.ci = FALSE,
  filter = TRUE,
  filter.min.eigen = 0.02,
  median.conf = FALSE
)

Arguments

x	The matrix obejct returned from getCompartments
chr	Chromosome to subset to for plotting
what	Which metadata column to plot
main	Title for the plot
ylim	Y-axis limits (default is -1 to 1)
unitarize	Should the data be unitarized?
reverse	Reverse the sign of the PC values?
top.col	Top (pos. PC values) chromatin color to be plotted
bot.col	Bottom (neg. PC values) chromatin color to be plotted
with.ci	Whether to plot confidence intervals
filter	Whether to filter eigenvalues close to zero (default: TRUE)
filter.min.eigen	Minimum absolute eigenvalue to include in the plot
median.conf	Plot the median confidence estimate across the chromosome?

Value

A plot of inferred A/B compartments

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")

#Calculate correlations
bin.cor.counts <- getCorMatrix(bin.counts)

#Get A/B signal
absignal <- getABSignal(bin.cor.counts)

#Plot the A/B signal
par(mar=c(1,1,1,1))
par(mfrow=c(1,1))
plotAB(absignal, what = "pc")

biobenkj/compartmap documentation built on Oct. 18, 2023, 11:11 a.m.