plot_pca: Perform a PCA for copy number profiles

In ataudt/aneufinder: Analysis of Copy Number Variation in Single-Cell-Sequencing Data

Perform a PCA for copy number profiles

Description

Perform a PCA for copy number profiles in aneuHMM objects.

Usage

plot_pca(hmms, PC1 = 1, PC2 = 2, colorBy = NULL, plot = TRUE,
  exclude.regions = NULL)

Arguments

hmms	A list of aneuHMM objects or a character vector with files that contain such objects.
PC1	Integer specifying the first of the principal components to plot.
PC2	Integer specifying the second of the principal components to plot.
colorBy	A character vector of the same length as hmms which is used to color the points in the plot.
plot	Set to FALSE if you want to return the data.frame that is used for plotting instead of the plot.
exclude.regions	A GRanges-class with regions that will be excluded from the computation of the PCA. This can be useful to exclude regions with artifacts.

Value

A ggplot object or a data.frame if plot=FALSE.

Examples

## Get results from a small-cell-lung-cancer
lung.folder <- system.file("extdata", "primary-lung", "hmms", package="AneuFinderData")
lung.files <- list.files(lung.folder, full.names=TRUE)
## Get results from the liver metastasis of the same patient
liver.folder <- system.file("extdata", "metastasis-liver", "hmms", package="AneuFinderData")
liver.files <- list.files(liver.folder, full.names=TRUE)
## Plot the PCA
classes <- c(rep('lung', length(lung.files)), rep('liver', length(liver.files)))
labels <- c(paste('lung',1:length(lung.files)), paste('liver',1:length(liver.files)))
plot_pca(c(lung.files, liver.files), colorBy=classes, PC1=2, PC2=4)

ataudt/aneufinder documentation built on April 18, 2023, 4:20 a.m.