iobr_pca: Principal Component Analysis (PCA) Visualization

In IOBR: Immune Oncology Biological Research

Principal Component Analysis (PCA) Visualization

Description

This function performs Principal Component Analysis (PCA) on gene expression data, reduces dimensionality while preserving variance, and generates a scatter plot visualization.

Usage

iobr_pca(
  data,
  is.matrix = TRUE,
  scale = TRUE,
  is.log = FALSE,
  pdata,
  id_pdata = "ID",
  group = NULL,
  geom.ind = "point",
  cols = "normal",
  palette = "jama",
  repel = FALSE,
  ncp = 5,
  axes = c(1, 2),
  addEllipses = TRUE
)

Arguments

data	Input data for PCA: matrix or data frame.
is.matrix	Logical indicating if input is a matrix. Default is TRUE.
scale	Logical indicating whether to scale the data. Default is TRUE.
is.log	Logical indicating whether to log-transform the data. Default is FALSE.
pdata	Data frame with sample IDs and grouping information.
id_pdata	Column name in 'pdata' for sample IDs. Default is "ID".
group	Column name in 'pdata' for grouping variable. Default is NULL.
geom.ind	Type of geometric representation for points. Default is "point".
cols	Color scheme for groups. Default is "normal".
palette	Color palette for groups. Default is "jama".
repel	Logical indicating whether to repel overlapping points. Default is FALSE.
ncp	Number of principal components to retain. Default is 5.
axes	Principal components to plot (e.g., c(1, 2)). Default is c(1, 2).
addEllipses	Logical indicating whether to add concentration ellipses. Default is TRUE.

Value

A ggplot object of the PCA plot.

Author(s)

Dongqiang Zeng

Examples

if (requireNamespace("FactoMineR", quietly = TRUE) &&
  requireNamespace("factoextra", quietly = TRUE)) {
  set.seed(123)
  eset <- matrix(rnorm(1000), nrow = 100, ncol = 10)
  rownames(eset) <- paste0("Gene", 1:100)
  colnames(eset) <- paste0("Sample", 1:10)
  pdata <- data.frame(
    ID = colnames(eset),
    group = rep(c("A", "B"), each = 5)
  )
  iobr_pca(eset, pdata = pdata, id_pdata = "ID", group = "group", addEllipses = FALSE)
}

IOBR documentation built on May 30, 2026, 5:07 p.m.