plotQLDisp: Plot the quasi-likelihood dispersion

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

Plot the quasi-likelihood dispersion

Description

Plot the genewise quasi-likelihood dispersion against the gene abundance (in log2 counts per million).

Usage

plotQLDisp(glmfit, xlab="Average Log2 CPM", ylab="Quarter-Root Mean Deviance", pch=16, 
       cex=0.2, col.shrunk="red", col.trend="blue", col.raw="black", ...)

Arguments

glmfit	a DGEGLM object produced by glmQLFit.
xlab	label for the x-axis.
ylab	label for the y-axis.
pch	the plotting symbol. See points for more details.
cex	plot symbol expansion factor. See points for more details.
col.shrunk	color of the points representing the squeezed quasi-likelihood dispersions.
col.trend	color of line showing dispersion trend.
col.raw	color of points showing the unshrunk dispersions.
...	any other arguments are passed to plot.

Details

This function displays the quarter-root of the quasi-likelihood dispersions for all genes, before and after shrinkage towards a trend. If glmfit was constructed without an abundance trend, the function instead plots a horizontal line (of colour col.trend) at the common value towards which dispersions are shrunk. The quarter-root transformation is applied to improve visibility for dispersions around unity.

Value

A plot is created on the current graphics device.

Author(s)

Aaron Lun, Davis McCarthy, Gordon Smyth, Yunshun Chen.

References

Chen Y, Lun ATL, and Smyth, GK (2016). From reads to genes to pathways: differential expression analysis of RNA-Seq experiments using Rsubread and the edgeR quasi-likelihood pipeline. F1000Research 5, 1438. http://f1000research.com/articles/5-1438

Examples

nbdisp <- 1/rchisq(1000, df=10)
y <- DGEList(matrix(rnbinom(6000, size = 1/nbdisp, mu = 10),1000,6))
design <- model.matrix(~factor(c(1,1,1,2,2,2)))
y <- estimateDisp(y, design)

fit <- glmQLFit(y, design)
plotQLDisp(fit)

fit <- glmQLFit(y, design, abundance.trend=FALSE)
plotQLDisp(fit)

OliverVoogd/edgeR documentation built on July 28, 2022, 10:13 p.m.