\name{GCbiasPlots}
\alias{GCbiasPlots}
\alias{GCbiasPlots,AdjustedCopyEstimate-method}
\title{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{
  \S4method{GCbiasPlots}{AdjustedCopyEstimate}(copy, y.max = NULL, pch = 19,
            cex = 0.2, pch.col = "black", line.col = "red", lty = 1, lwd = 2, verbose = TRUE)
}
\arguments{
  \item{copy}{A \code{\linkS4class{CopyEstimate}} object.}
  \item{y.max}{The maximum value of the y-axis of the scatter plots.}
  \item{pch}{Style of points in the scatter plots.}
  \item{cex}{Size of the points in the scatter plots.}
  \item{pch.col}{Colour of points in the scatter plots.}
  \item{line.col}{Colour of regression line in each scatter plot.}
  \item{lty}{Line type of plotted regression line.}
  \item{lwd}{Line width of plotted regression line.}
  \item{verbose}{Whether to print the progess of processing.}
}
\details{
  See \code{\link{absoluteCN}} or \code{\link{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 \code{copy}.
  The output should, therefore, be sent to a PDF device.
}
\author{Dario Strbenac}
\examples{
  \dontrun{
    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()
  }
}