plot_PCA: Plot Principal Component Analysis (PCA) of samples

In almeidasilvaf/BioNERO: Biological Network Reconstruction Omnibus

Plot Principal Component Analysis (PCA) of samples

Description

Plot Principal Component Analysis (PCA) of samples

Usage

plot_PCA(
  exp,
  metadata,
  metadata_cols = NULL,
  log_trans = FALSE,
  PCs = c(1, 2),
  size = 2
)

Arguments

exp	A gene expression data frame with genes in row names and samples in column names or a 'SummarizedExperiment' object.
metadata	A data frame of sample metadata containing sample names in row names and sample annotation in subsequent columns. Ignored if 'exp' is a 'SummarizedExperiment' object, since colData will be automatically extracted.
metadata_cols	A vector (either numeric or character) indicating which columns should be extracted from column metadata if exp is a 'SummarizedExperiment' object. The vector can contain column indices (numeric) or column names (character). By default, all columns are used.
log_trans	Logical indicating whether the gene expression matrix should be log transformed using log(exp + 1). Default: FALSE.
PCs	Numeric vector of length 2 indicating the principal components to be plotted on the x-axis and y-axis, respectively. Default: c(1, 2).
size	Numeric indicating the point size. Default is 2.

Value

A ggplot object with the PCA plot.

Author(s)

Fabricio Almeida-Silva

See Also

ggplot

Examples

data(zma.se)
plot_PCA(zma.se, log_trans = TRUE)

almeidasilvaf/BioNERO documentation built on Oct. 9, 2024, 1:49 a.m.