plot_pca {bulkAnalyseR}

Create a principal component analysis (PCA) plot the samples of an experiment

Description

This function creates a PCA plot between all samples in the expression matrix using the specified number of most abundant genes as input. A metadata column is used as annotation.

Usage

plot_pca(
  expression.matrix,
  metadata,
  annotation.id,
  n.abundant = NULL,
  show.labels = FALSE,
  show.ellipses = TRUE,
  label.force = 1
)

Arguments

expression.matrix

the expression matrix; rows correspond to genes and columns correspond to samples; usually preprocessed by preprocessExpressionMatrix; a list (of the same length as modality) can be provided if #' length(modality) > 1

metadata

a data frame containing metadata for the samples contained in the expression.matrix; must contain at minimum two columns: the first column must contain the column names of the expression.matrix, while the last column is assumed to contain the experimental conditions that will be tested for differential expression; a list (of the same length as modality) can be provided if #' length(modality) > 1

annotation.id

a column index denoting which column of the metadata should be used to colour the points and draw confidence ellipses

n.abundant

number of most abundant genes to use for the JSI calculation

show.labels

whether to label the points with the sample names

show.ellipses

whether to draw confidence ellipses

label.force

passed to the force argument of ggrepel::geom_label_repel; higher values make labels overlap less (at the cost of them being further away from the points they are labelling)

Value

The PCA plot as a ggplot object.

Examples

expression.matrix.preproc <- as.matrix(read.csv(
  system.file("extdata", "expression_matrix_preprocessed.csv", package = "bulkAnalyseR"), 
  row.names = 1
))[1:500,]

metadata <- data.frame(
  srr = colnames(expression.matrix.preproc), 
  timepoint = rep(c("0h", "12h", "36h"), each = 2)
)
plot_pca(expression.matrix.preproc, metadata, 2)