plot_pca | R Documentation |
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.
plot_pca( expression.matrix, metadata, annotation.id, n.abundant = NULL, show.labels = FALSE, show.ellipses = TRUE, label.force = 1 )
expression.matrix |
the expression matrix; rows correspond to genes and
columns correspond to samples; usually preprocessed by
|
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 #' |
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) |
The PCA plot as a ggplot object.
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)
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