prot.pca: PCA Analysis

In NicWir/VisomX: User-Friendly and Comprehensive Analysis and Visualization of Omics Data

PCA Analysis

Description

This function performs principal component analysis (PCA) on a given matrix.

Usage

prot.pca(
  mat,
  metadata = NULL,
  center = TRUE,
  scale = FALSE,
  rank = NULL,
  removeVar = NULL,
  transposed = FALSE,
  BSPARAM = BiocSingular::ExactParam(),
  in_workflow = FALSE
)

Arguments

mat	A numeric matrix.
metadata	An optional data frame with rownames matching 'colnames(mat)'.
center	A logical indicating whether to center the data before PCA.
scale	A logical indicating whether to scale the data before PCA.
rank	An integer indicating the number of principal components to calculate.
removeVar	A numeric value between 0 and 1 indicating the fraction of variables to remove based on variance.
transposed	A logical indicating whether the matrix is transposed.
BSPARAM	An object of class ExactParam or ApproxParam.
in_workflow	Indicated whether this function is called from within prot.workflow.

Value

A list with components rotated, loadings, variance, sdev, metadata, xvars, yvars, and components.

References

BiocSingular (https://bioconductor.org/packages/release/bioc/html/BiocSingular.html)

Examples

mat <- matrix(rnorm(1000), nrow = 100, ncol = 10)
prot.pca(mat)

NicWir/VisomX documentation built on Dec. 8, 2024, 1:27 a.m.