R/PlotCNVs.R

Defines functions PlotCNVs

Documented in PlotCNVs

##' PlotCNVs: Function to plot Log R Ratio (LRR) and B Allele Frequency (BAF) of CNVs from a data frame (DF).
##'
##' Specifically designed to handle noisy data from amplified DNA on phenylketonuria (PKU) cards. The function is a pipeline using many subfunctions.
##' @title PlotCNVs
##' @param DF: Data frame with predicted CNVs for each sample, default = Unknown.
##' @param PathRawData: The path to the raw data files containing LRR and BAF values.
##' @param Cores: Number of cores used, default = 1.
##' @param Skip: Integer, the number of lines of the data file to be skipped before beginning to read the data, default = 0.
##' @param PlotPosition: Unknown, default = 1.
##' @param Pattern: File pattern in the raw data, default = "*".
##' @param Recursive: Logical, Unknown, default = TRUE.
##' @param Dpi: Dots per inch, default = 300.
##' @param Files: Unknown, default = NA.
##' @param SNPList: Getting chromosome (Chr) and position from another source than the RawFile - input should be the full path of the SNPList with columns: Name, Chr, and Position. Any positions from the RawFile will be erased. A PFB-column is also allowed but will be overwritten by the PFB-parameter or exchanged with 0.5, default = NULL.
##' @param Key: Exchange the ID printed on the plot and in the name of file with a deidentified ID - requires that the DF contains a column called ID_deidentified, default = NA.
##' @param Start: Start position of plot
##' @param Stop: Stop position of plot
##' @param OutFolder: Path for saving outputfiles, default is the current folder.
##' @return One BAF- and LRR-plot for each CNV.
##' @author Marcelo Bertalan, Ida Elken Sønderby, Louise K. Hoeffding.
##' @source \url{http://biopsych.dk/iPsychCNV}
##' @export
##' @examples
##' # Creating CNVs from MockData & plotting
##' MockCNVs <- MockData(N=2, Type="Blood", Cores=10)
##' CNVs <- iPsychCNV(PathRawData=".", Pattern="^MockSample*", Skip=0)
##' CNVs.Good <- subset(CNVs, CN != 2) # keep only CNVs with CN = 0, 1, 3, 4.
##' PlotCNVs(DF=CNVs.Good[1,], PathRawData=".", Cores=1, Skip=0, Pattern="^MockSamples*", key=NA, OutFolder="../", XAxisDefine = NULL)

PlotCNVs <- function(DF, PathRawData=".", Cores=1, Skip=10, PlotPosition=1, Pattern="",recursive=TRUE, dpi=300, Files=NA, Start=NA, Stop=NA, SNPList=NULL, OutFolder=".", Window=35, key = NA)
{
  library(ggplot2)
  library(ggbio) # For some reason ggplot2 2.0.2 is not working, probably conflict with other packages. Version 1.0.1 works.
  library(parallel)
  library(biovizBase)
  library(RColorBrewer)
  library(GenomicRanges)

  LocalFolder <- PathRawData
  if(is.na(Files))
  {
    Files <- list.files(path=PathRawData, pattern=Pattern, full.names=TRUE, recursive=recursive)
  }

  DF$UniqueID <- 1:nrow(DF)

  mclapply(unique(DF$ID), mc.cores=Cores, function(UID)
  {
    X <- subset(DF, ID %in% UID)
    chr <- gsub(" ", "",X$Chr) # X$Chr
    ID <- X$ID
    UniqueID <- X$UniqueID
    cat(ID, "\n")

    CNVstart <- as.numeric(X$Start)
    CNVstop <- as.numeric(X$Stop)
    Size <- as.numeric(X$Length)
    CN <- X$CN
    SDCNV <- round(as.numeric(X$SDCNV), digits=2)
    NumSNP <- as.numeric(X$NumSNPs)
    if(length(X$status1) >0) {pheno <-unique(X$status1)[1] }

    if(is.na(Start) & is.na(Stop))
    {
      TotalLength <- max(CNVstop) - min(CNVstart)
      # Start & Stop-positions of plot
      Start <- min(CNVstart) - (TotalLength*2)
      Stop <- max(CNVstop) + (TotalLength*2)
    }

    ## Naming output-file
         # based on key or not
         if (!is.na(key))  # if want a different ID from the genetic ID in the plot
         {
           ID_deidentified <- X$ID_deidentified  # Added this to get ID_deidentified
           NewName <- paste(ID_deidentified, "_chr", unique(chr), "_", Start, "-", Stop, sep="", collapse="")
         }
         else
         {
          NewName <- paste(unique(ID),"_chr", unique(chr), "_", Start, "-", Stop, sep="", collapse="")
         }

    # based on OutFolder or not
    if(OutFolder!=".")
    {
      OutPlotfile <- paste(OutFolder, NewName, "_plot.png", sep="", collapse="")
    }
    else
    {
      OutPlotfile <- paste(NewName, "plot.png", sep="_", collapse="")
      OutPlotfile <- gsub(" ","",OutPlotfile)
    }
    print(OutPlotfile)


    # Reading sample file
    #     RawFile <- paste(PathRawData, ID, sep="", collapse="")
#    RawFile <- Files[grep(ID, Files)]
#    RawFile <- Files[which(Files == paste(PathRawData, ID, Pattern, sep=""))]
        RawFile <- Files[grep(paste("\\b", ID, Pattern, "$", sep = ""), Files)] # this should deal with similar filesnames, i.e 10 & 110

        cat("File: ", RawFile,"\n")

    sample <- ReadSample(RawFile, skip=Skip, SNPList=SNPList)

    ## Set all CN-markers to NA (as they all have the non-usable value of 2) ONLY REALLY FOR AFFY DATA
    if(any(sample[,"B.Allele.Freq"] == 2, na.rm=T)) { sample[which(sample[,"B.Allele.Freq"] == 2), "B.Allele.Freq"] <- NA }

    red <- subset(sample,Chr==unique(chr)) # select SNPs from rawfile in Chr of interest
    red <- subset(red, Position > min(Start) & Position < max(Stop)) # select SNPs from rawfile that is within the plotted area
    red2 <- red[with(red, order(Position)),] # order selected SNPs by Position

    Mean <- SlideWindowMean(red2$Log.R.Ratio, Window)
    red2$Mean <- Mean

    # Ideogram
    data(hg19IdeogramCyto,package="biovizBase")
    CHR <- paste("chr", unique(chr), collapse="", sep="")
    p3 <- plotIdeogram(hg19IdeogramCyto,CHR,cytoband=TRUE,xlabel=TRUE, aspect.ratio = 1/85, alpha = 0.3) +
      xlim(GRanges(CHR,IRanges(min(Start),max(Stop))))

    # Colors
    Colors = brewer.pal(9,"Set1")

    # B.Allele
    rect2 <- data.frame (xmin=CNVstart, xmax=CNVstop, ymin=0, ymax=1, CN=as.character(CN)) # CNV position

    # Info for breaks, to always have 10 breaks.
    ##     BY <- round((max(red2$Position) - min(red2$Position))/10) # superfluous?

    p1 <- ggplot() + geom_point(data=red2, aes(x=Position, y = B.Allele.Freq, col="B.Allele.Freq"), size=0.5) +
      geom_rect(data=rect2, aes(xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, fill=as.factor(CN)), color="darkgrey", alpha=0.2) +
      theme(legend.title=element_blank()) + scale_colour_manual(values = c("1" = Colors[1], "2" = Colors[9], "3"= Colors[2], "4" = Colors[3], "0"= Colors[4], "5" = Colors[5], "B.Allele.Freq" = Colors[2], "CNV region" = Colors[3], "CNV predicted" = Colors[4], "Mean" = "black"))
    ##     + scale_color_manual(values = c(Colors[2:4]))
    ##     + scale_x_continuous(breaks = round(seq(min(red2$Position), max(red2$Position), by = BY),1))

    # LogRRatio
    p2 <- ggplot() + geom_point(data=red2, aes(x=Position, y=Log.R.Ratio, col="Log.R.Ratio"), alpha = 0.6, size=0.5)  +
      geom_line(data=red2, aes(x=Position, y = Mean, col="Mean"), size = 0.5) + # Mean of signal line
      ylim(-1, 1) + # set y-axis
      theme(legend.title=element_blank()) +
      scale_color_manual(values = c(Colors[1], "black"))   # black color
    ##       + scale_x_continuous(labels=format_si(), # this sets the axis label to K, M for 1000, 1000000 etc...
    ##                          breaks = round(seq(min(red2$Position), max(red2$Position), by = BY),1)) # this gives a distance of 500,000 between ticks on x-axis

    # Title printed for plot
    if (!is.na(key))  # if want a different ID from the genetic ID in the plot
    {
      IDfortitle <- unique(ID_deidentified)
    }
    else
    {
      IDfortitle <- unique(ID)
    }

    ##     if (!is.na(key))  # if want a different ID from the genetic ID in the plot
    if (length(X$status1) > 0)
        {
        Title <- paste(pheno,", CN: ", unique(CN), ", NumSNPs: " , max(NumSNP), ", Sample: ", IDfortitle, sep="", collapse="")
        ##       Title <- paste("CN: ", CN, "   Size: ", prettyNum(Size, big.mark=",",scientific=FALSE), "   SNPs: ", NumSNP, "   Sample: ", ID_deidentified, sep="", collapse="")
        }
        else
        {
          Title <- paste("CN: ", unique(CN), ", NumSNPs: ", max(NumSNP), ", Sample: ", IDfortitle, sep="", collapse="")
          ##       Title <- paste("CN: ", CN, "   Size: ", prettyNum(Size, big.mark=",",scientific=FALSE), "   SNPs: ", NumSNP, "   Sample: ", ID, sep="", collapse="")
        }
        Plot <- tracks(p3, p1,p2, main=Title, heights=c(3,5,5))
        ggsave(OutPlotfile, plot=Plot, height=5, width=10, dpi=dpi)

  })
}
mbertalan/iPsychCNV documentation built on June 30, 2017, 2:02 a.m.