plotSmear: Smear plot

In edgeR: Empirical Analysis of Digital Gene Expression Data in R

Description

Make a mean-difference plot of two libraries of count data with smearing of points with very low counts, especially those that are zero for one of the columns.

Usage

plotSmear(object, pair=NULL, de.tags=NULL, xlab="Average logCPM", ylab="logFC", pch=19,
     cex=0.2, smearWidth=0.5, panel.first=grid(), smooth.scatter=FALSE, lowess=FALSE, ...)

Arguments

object	DGEList, DGEExact or DGELRT object containing data to produce an MA-plot.
pair	pair of experimental conditions to plot (if NULL, the first two conditions are used). Ignored if object is a DGELRT object.
de.tags	rownames for genes identified as being differentially expressed; use exactTest or glmLRT to identify DE genes. Note that ‘tag’ and ‘gene’ are synonymous here.
xlab	x-label of plot
ylab	y-label of plot
pch	scalar or vector giving the character(s) to be used in the plot; default value of 19 gives a round point.
cex	character expansion factor, numerical value giving the amount by which plotting text and symbols should be magnified relative to the default; default cex=0.2 to make the plotted points smaller
smearWidth	width of the smear
panel.first	an expression to be evaluated after the plot axes are set up but before any plotting takes place; the default grid() draws a background grid to aid interpretation of the plot
smooth.scatter	logical, whether to produce a 'smooth scatter' plot using the KernSmooth::smoothScatter function or just a regular scatter plot; default is FALSE, i.e. produce a regular scatter plot
lowess	logical, indicating whether or not to add a lowess curve to the MA-plot to give an indication of any trend in the log-fold change with log-concentration
...	further arguments passed on to plot

Details

plotSmear produces a type of mean-difference plot (or MA plot) with a special representation (smearing) of log-ratios that are infinite. plotSmear resolves the problem of plotting genes that have a total count of zero for one of the groups by adding the 'smear' of points at low A value. The points to be smeared are identified as being equal to the minimum estimated concentration in one of the two groups. The smear is created by using random uniform numbers of width smearWidth to the left of the minimum A. plotSmear also allows easy highlighting of differentially expressed (DE) genes.

Value

Invisibly returns the x and y coordinates of the plotted points, and a plot is created on the current device.

Author(s)

Mark Robinson created the original concept of smearing the infinite log-fold-changes.

See Also

maPlot, plotMD.DGEList

Examples

y <- matrix(rnbinom(10000,mu=5,size=2),ncol=4)
d <- DGEList(counts=y, group=rep(1:2,each=2), lib.size=colSums(y))
rownames(d$counts) <- paste("gene",1:nrow(d$counts),sep=".")
d <- estimateCommonDisp(d)
plotSmear(d)

# find differential expression
de <- exactTest(d)

# highlighting the top 500 most DE genes
de.genes <- rownames(topTags(de, n=500)$table)
plotSmear(d, de.tags=de.genes)

Example output

Loading required package: limma

edgeR documentation built on Jan. 16, 2021, 2:03 a.m.