plotAB {compartmap} | R Documentation

## 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")

[Package compartmap version 1.10.0 Index]