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.
1 2 3 4 | 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
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | ## 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)
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