plotQLDisp: Plot the quasi-likelihood dispersion
In edgeR: Empirical Analysis of Digital Gene Expression Data in R

Description Usage Arguments Details Value Author(s) References Examples

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

1 2	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", ...)

`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`.

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.

A plot is created on the current graphics device.

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

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

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)