signature-plot-functions: Plot Mutational Signatures
In julian-gehring/SomaticSignatures: Somatic Signatures

Description Usage Arguments Details Value See Also Examples

Visualize estimated signatures, sample contribution, and mutational spectra.

plotObservedSpectrum(s, colorby = c("sample", "alteration"))
plotFittedSpectrum(s, colorby = c("sample", "alteration"))

plotMutationSpectrum(vr, group, colorby = c("sample", "alteration"), normalize = TRUE)

plotSignatureMap(s)
plotSignatures(s, normalize = FALSE, percent = FALSE)

plotSampleMap(s)
plotSamples(s, normalize = FALSE, percent = FALSE)

`s`	MutationalSignatures object [required]
`vr`	VRanges object
`colorby`	Which variable to use for the coloring in the spectra representation.
`normalize`	Plot relative constributions (TRUE) instead of absolute (FALSE) ones.
`percent`	Display the results as fraction (FALSE) or percent (TRUE)

group

Charactering string that represents the variable name used for grouping.

With the plotting function, the obtained signatures and their occurrance in the samples can be visualized either as a heatmap ('plotSignatureMap', 'plotSampleMap') or a barchart ('plotSignature', 'plotSamples').

Since the plotting is based on the 'ggplot2' framework, all properties of the plots can be fully controlled by the user after generating the plots. Please see the examples for some customizations and the 'ggplot2' documentation for the entire set of options.

A 'ggplot' object, whose properties can further be changed

See the ggplot2 package for customizing the plots.

data("sca_sigs", package = "SomaticSignatures")

plotSamples(sigs_nmf)

plotSignatures(sigs_nmf, normalize = TRUE)

## customize the plots ##
p = plotSamples(sigs_nmf)

library(ggplot2)
## (re)move the legend
p = p + theme(legend.position = "none")
## change the axis labels
p = p + xlab("Studies")
## add a title
p = p + ggtitle("Somatic Signatures in TGCA WES Data")
## change the color scale
p = p + scale_fill_brewer(palette = "Blues")
## decrease the size of x-axis labels
p = p + theme(axis.text.x = element_text(size = 9))

p