plotVolcano: Plots the LFC and p-value of a fit object.
In ChristofferFlensburg/XRank:

Description Usage Arguments Details Examples

Plots the LFC and p-value of a fit object.

plotVolcano(fit, coef = 1, print = 20, main = NA, shrink = T,
  line = "fdr", xlim = "default", ylim = "default",
  xlab = "corrected LFC", ylab = "-log10(p-value)", specialGenes = c(),
  col = "red", ...)

`fit`	A fit object that has gone through XRank.
`coef`	The columns of the fit object to be plotted. Default 1.
`print`	Non-negative integer. The number of top ranked genes to have their names printed on the plot.
`shrink`	Logical. Whether to print the higher ranked genes in a smaller font.
`line`	character. Where to plot a horisontal support line. 'nGenes' plots a line at p = 1/nrow(fit), above which there will be one false positive on average. 'fdr' draws the line at the p corresponding to fdr = 5%, above which 5% of the genes are false positives on average. 'fdr' reverts to 'nGenes' if no gene has fdr <= 5%. Default 'fdr'.
`specialGenes`	vector of rownames of fit. These genes will be highlighted no matter where on the plot they are.
`col`	The colour of the best.guess dots. Default 'red'.
`...`	Remaining arguments are passed on to plot(...).

This function plots a standard volcano plot: The -log10(p-value) as function of the LFC. It also superimpose the posterior estimates of the LFC by XRank: best.guess in red. The best.guess will in general be strongly shrunk towards LFC = 0 for large p-values, and leave small p-values essentially unchanged. See XRank for more information about these statistics.

#Set up a (highly artificial) count matrix.
counts = matrix(rpois(1000*6, 100), ncol=6,
         dimnames=list(paste0('gene', 1:1000), c('a', 'b', 'c', 'D', 'E', 'F')))

#set up experimental design
group = c(rep('lower', 3), rep('upper', 3))
design = model.matrix(~0+group)
colnames(design) = gsub('^group', '', colnames(design))
contrasts = limma::makeContrasts('upper-lower', levels=colnames(design))

#run voom and limma
fit = limma::voom(counts, design, plot=T)
fit = limma::lmFit(fit, design=design)
fit = limma::contrasts.fit(fit, contrasts)
fit = limma::eBayes(fit)
fit = XRank(fit, plot=F)
plotVolcano(fit)