plotAB {compartmap} | R Documentation |
Plot A/B compartments bins
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 )
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? |
A plot of inferred A/B compartments
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")