Plot the quasi-likelihood dispersion
Plot the genewise quasi-likelihood dispersion against the gene abundance (in log2 counts per million).
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 |
xlab |
label for the x-axis. |
ylab |
label for the y-axis. |
pch |
the plotting symbol. See |
cex |
plot symbol expansion factor. See |
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 |
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)
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