GCbiasPlots {Repitools} | R Documentation

## Plot GC content vs. Read Counts Before Normalising, and GC content vs. Copy Estimates After Normalising.

### Description

Two plots on the same plotting page are made for each sample. The top plot has estimates of copy number separated by GC content before any GC correction was applied. The bottom plot shows the copy number estimates after GC correction was applied.

### Usage

      ## S4 method for signature 'AdjustedCopyEstimate'
    GCbiasPlots(copy, y.max = NULL, pch = 19,
                cex = 0.2, pch.col = "black", line.col = "red", lty = 1, lwd = 2, verbose = TRUE)

### Arguments

`copy` | A `CopyEstimate` object.
`y.max` | The maximum value of the y-axis of the scatter plots.
`pch` | Style of points in the scatter plots.
`cex` | Size of the points in the scatter plots.
`pch.col` | Colour of points in the scatter plots.
`line.col` | Colour of regression line in each scatter plot.
`lty` | Line type of plotted regression line.
`lwd` | Line width of plotted regression line.
`verbose` | Whether to print the progess of processing.

### Details

See `absoluteCN` or `relativeCN` for how to do the GC adjusted copy number estimates. The line plotted through the scatterplots is a lowess line fit to the data points.

### Value

A number of pages of scatterplots equal to the number of samples described by `copy`. The output should, therefore, be sent to a PDF device.

### Author(s)

Dario Strbenac

### Examples

      ## Not run:
        library(BSgenome.Hsapiens.UCSC.hg18)
        library(BSgenome.Hsapiens36bp.UCSC.hg18mappability)
        load("inputsReads.RData")
        windows <- genomeBlocks(Hsapiens, chrs = paste("chr", c(1:22, 'X', 'Y'), sep = ''),
                                width = 20000)
        counts <- annotationBlocksCounts(inputsReads, anno = windows, seq.len = 300)

        gc.par <- GCAdjustParams(genome = Hsapiens, mappability = Hsapiens36bp,
                                 min.mappability = 50, n.bins = 10, min.bin.size = 10,
                                 poly.degree = 4, ploidy = c(2, 4))
        abs.cn <- absoluteCN(input.windows = windows, input.counts = counts, gc.params = gc.par)

        pdf("bias.pdf")
        GCbiasPlots(abs.cn, y.max = 8)
        dev.off()

    ## End(Not run)

[Package Repitools version 1.26.0 Index]