general.volcano_plot: Plots a volcano plot.
In jhooge/BioViz: BiomarkerVizard

Description Usage Arguments Value Author(s) Examples

The plot does not aim to show any details about which genes or pathways are over/under represented, but is meant to show only the overall directionality of the genes between groups. If there is a certain perturbation in a system, the volcano plot may reveal a response in which there is a dramatic increase or decreased in transcription. In either case, we can get a better idea of the overall system by showing a volcano plot. Each gene is represented with its p-value as the x-axis and its (log2) fold change as the y-axis.

general.volcano_plot(data, title = "Volcano Plot", groups = c("Untreated",
  "Treated"), top_labeled = 0, xlabel = expression(log[2]("Foldchange")),
  ylabel = expression(-log[10]("p-value")), xcutoff = c(-log2(4), log2(4)),
  ycutoff = -log10(0.05), log1p = FALSE)

`data`	A data.frame which includes columns for -log10(p-values) and log2(foldchange)
`title`	The figure title
`groups`	A character vector with the treatment groups
`top_labeled`	A numeric value that controls how many samples should be highlighted based on their pvalue
`xlabel`	X-Axis Label
`ylabel`	Y-Axis Label
`xcutoff`	A numeric value for the -log10(p-value) cutoff
`ycutoff`	A numeric value for the log2(foldchange) cutoff
`log1p`	A flag to apply a log(1+x) transformation on the y axis to account for large values

A ggplot2 figure

Jens Hooge

## Not run: 
data(iPSC)
## Plot Simple Volcano Plot
general.volcano_plot(iPSC)

## Plot Simple Volcano Plot with Group Labels
general.volcano_plot(iPSC, groups=c("A", "B"))

## Plot Volcano Plot and add top10 most significant samples
general.volcano_plot(iPSC, top_labeled=10, xcutoff=c(-log2(2), log2(4)))

## Plot Volcano Plot and add Top10 most Significant Samples and Transform y-axis by log(1+x)
general.volcano_plot(iPSC, top_labeled=10, log1p=TRUE)

## End(Not run)