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 |

`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

1 2 3 4 5 6 7 8 9 10 | ```
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)
``` |

```
Loading required package: limma
```

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