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

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

Description

Plot A/B compartments bins

Usage

plotAB(
  x,
  chr = NULL,
  what = c("score", "flip.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
main	Title for the plot
ylim	Y-axis limits (default is -1 to 1)
unitarize	Should the data be unitarized? (not explicitly necessary for arrays)
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)
median.conf	Whether to plot the median confidence estimate across the chromosome
filter.lower.bound	The minimum absolute eigenvalue to include in the plot

Value

invisibly, the compartment estimates from the plot

Examples

library(GenomicRanges)
library(Homo.sapiens)

#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$pc, ylim = c(-0.2, 0.2), unitarize = TRUE)

## Not run: 
#If plotting individual A/B signals using output from getCompartments
#Note: this function currently only supports plotting individual chromosomes from single samples
bin.chr1.ab <- getCompartments(data, "array", chrs = "chr1", genome = "hg19")

#For 7 samples
#Adjust ylim as necessary
par(mar=c(1,1,1,1))
par(mfrow=c(7,1))
plotAB(bin.chr1.ab[,1], ylim = c(-0.2, 0.2), unitarize = TRUE)
plotAB(bin.chr1.ab[,2], ylim = c(-0.2, 0.2), unitarize = TRUE, top.col = "goldenrod")
plotAB(bin.chr1.ab[,3], ylim = c(-0.2, 0.2), unitarize = TRUE, top.col = "darkblue")
plotAB(bin.chr1.ab[,4], ylim = c(-0.2, 0.2), unitarize = TRUE, top.col = "red")
plotAB(bin.chr1.ab[,5], ylim = c(-0.2, 0.2), unitarize = TRUE, top.col = "black")
plotAB(bin.chr1.ab[,6], ylim = c(-0.2, 0.2), unitarize = TRUE, top.col = "cyan")
plotAB(bin.chr1.ab[,7], ylim = c(-0.2, 0.2), unitarize = TRUE, top.col = "seagreen")

## End(Not run)

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