113 lines
3.7 KiB
R
113 lines
3.7 KiB
R
#'@include ROBIBarcodes.R
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#'@include logo.R
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NULL
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#' Draw a scatter plot of the reverse mismatches as a function of forward mismatches.
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#'
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#' The \code{mismatchplot} function draws a scatter plot of the number of mismatches
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#' observed in an ecoPCR result for the reverse primer as a function of the mismatches
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#' for the reverse primer. Each point for a pair (forward_mismatch,reverse_mismatch) is
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#' drawn as a circle having a surface proportional to the aboundance of this pair in the
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#' results. If a grouping factor is specified, then the circle is replaced by a pie chart.
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#'
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#' @param ecopcr an ecoPCR result data.frame as returned by the \code{\link{read.ecopcr.result}}
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#' function.
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#'
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#' @param group a factor decribing classes amongst the amplicon described in the ecoPCR
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#' result
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#'
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#' @param col a vector describing the colored used for the bubble or the pie charts
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#'
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#' @param legend a character vector describing the legend for each modality of the
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#' grouping factor. By default the factor levels are used for the legend
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#'
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#' @param legend.cex the expension factor for the legend text
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#'
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#' @param inset the distance to the margin of the legend box (see the \code{\link{legend}}
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#' documentation)
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#'
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#' @param view.legend if set to \code{FALSE} the legend corresponding to the groups is not dispayed.
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#'
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#' @param maxerror allows for specifying the maximum of errors to display on the graph.
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#'
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#' @examples
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#'
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#' # Load the ROBITools library
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#' library(ROBITools)
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#'
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#' # Load the default taxonomy
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#' taxo = default.taxonomy()
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#'
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#' # Load the sample ecoPCR data file
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#' data(GH.ecopcr)
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#'
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#' # Computes classes associated to each taxid
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#' orders = as.factor(taxonatrank(taxo,GH.ecopcr$taxid,'order',name=T))
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#'
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#' # Plot the graph
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#' mismatchplot(GH.ecopcr,group=orders)
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#'
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#' @seealso \code{\link{read.ecopcr.result}}
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#' @author Eric Coissac
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#' @export
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mismatchplot = function(ecopcr,group=NULL,
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col=NULL,legend=NULL,
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legend.cex=0.7,inset=c(0.02,0.02),
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view.legend=TRUE,
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maxerror=NA) {
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maxforward_error = max(ecopcr$forward_mismatch)
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maxreverse_error = max(ecopcr$reverse_mismatch)
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if (is.na(maxerror))
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maxerror=max(maxforward_error,maxreverse_error)
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if (is.null(group))
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group=factor(rep("all",dim(ecopcr)[1]))
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else
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group=as.factor(group)
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if (is.null(legend))
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legend = levels(group)
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actualheight= maxerror + 1
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actualwidth = maxerror + 1
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if (length(levels(group)) > 1 & view.legend)
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actualwidth = actualwidth + 2
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whitepaper(actualwidth,actualheight,xmin=-0.5,ymin=-0.5,asp=1)
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axis(1,at=0:maxerror,
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labels=0:maxerror)
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axis(2,at=0:maxerror,
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labels=0:maxerror)
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data = aggregate(group,by=list(forward=factor(ecopcr$forward_mismatch,levels=0:maxerror),
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reverse=factor(ecopcr$reverse_mismatch,levels=0:maxerror)),
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table)
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data <- data[rowSums(data[,c(-1,-2),drop=FALSE])>0, , drop=FALSE]
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if (is.null(col))
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col <- c("white", "lightblue", "mistyrose", "lightcyan",
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"lavender", "cornsilk")
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value=data[,c(-1,-2),drop=FALSE]
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x = as.integer(data[,1]) - 1
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y = as.integer(data[,2]) - 1
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diam = sqrt(rowSums(value))
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radius = diam / max(diam) / 2
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hide = mapply(pie.xy,x,y,
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data=lapply(1:(dim(value)[1]),function(y) value[y,]),
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radius=radius,
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label="",MoreArgs=list(col=col))
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if ((length(levels(group))) > 1 & view.legend)
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legend('topright',legend=legend,fill=col, cex=legend.cex, inset=inset)
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} |