getABSignal {compartmap} | R Documentation

## Calculate Pearson correlations of smoothed eigenvectors

### Description

This function is used to generate a list x to be passed to getABSignal

### Usage

    getABSignal(x, k = 5, iter = 2, squeeze = FALSE)

### Arguments

`x` | A list object from getCorMatrix
`k` | Value of k for smoothing (default = 2)
`iter` | Number of iterations for moving average smoothing (default = 2)
`squeeze` | Whether squeezing was used (implies Fisher's Z transformation)

### Value

A list x to pass to getABSignal

### Examples

    library(GenomicRanges)
    library(Homo.sapiens)
    library(mixOmics)

    #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$start <- apply(random_genomic_int, 1, function(x) { round(runif(1, 0, seqlengths(Homo.sapiens)[x][[1]]), 0) })
    random_genomic_int$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)

[Package compartmap version 1.4.0 Index]