Perform PCA on Gene Expression Data — pca

This function performs principal component analysis (PCA) on gene expression data in a `SummarizedExperiment` object.

Usage

pca_gexp(
  exp_data,
  assay = "norm",
  filter = TRUE,
  n_hvg = 2000,
  center = TRUE,
  scale = TRUE
)

Arguments

exp_data: A `SummarizedExperiment` object containing the gene expression data.
assay: A character string specifying the assay to use for PCA. Default is `"norm"`.
filter: Logical. If `TRUE`, PCA is performed on the top `n_hvg` highly variable genes. If `FALSE`, PCA is performed on all genes. Default is `TRUE`.
n_hvg: An integer specifying the number of highly variable genes to use if `filter = TRUE`. Default is 2000.
center: Logical. If `TRUE`, the variables are centered before PCA. Default is `TRUE`.
scale: Logical. If `TRUE`, the variables are scaled to unit variance before PCA. Default is `TRUE`.

Value

A `prcomp` object containing the PCA results.

Details

If `filter = TRUE`, the function identifies the top `n_hvg` highly variable genes using a robust coefficient of variation and performs PCA on these genes. Otherwise, PCA is performed on all genes in the selected assay.

The `prcomp` function is used for PCA, with options to center and scale the data before analysis.