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Last data update: 2014.03.03

R: Plots log-Fold Change versus log-Concentration (or, M versus...

plotSmear

R Documentation

Plots log-Fold Change versus log-Concentration (or, M versus A) for Count Data

Description

Both of these functions plot the log-fold change (i.e. the log of the ratio of expression levels for each gene between two experimential groups) against the log-concentration (i.e. the overall average expression level for each gene across the two groups). To represent counts that were low (e.g. zero in 1 library and non-zero in the other) in one of the two conditions, a 'smear' of points at low A value is presented in plotSmear.

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 is a more sophisticated and superior way to produce an 'MA plot'. 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

A plot to the current device

Author(s)

Mark Robinson, Davis McCarthy

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)

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(edgeR)
Loading required package: limma
> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/edgeR/plotSmear.Rd_%03d_medium.png", width=480, height=480)
> ### Name: plotSmear
> ### Title: Plots log-Fold Change versus log-Concentration (or, M versus A)
> ###   for Count Data
> ### Aliases: plotSmear
> 
> ### ** 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)
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>