calculate_sig_score_pca: Calculate Signature Score Using PCA Method

In IOBR: Immune Oncology Biological Research

Calculate Signature Score Using PCA Method

Description

Computes signature scores using Principal Component Analysis. The first principal component is used as the signature score.

Usage

calculate_sig_score_pca(
  pdata = NULL,
  eset,
  signature,
  mini_gene_count = 3,
  column_of_sample = "ID",
  adjust_eset = FALSE
)

Arguments

pdata	Data frame with phenotype data. If 'NULL', created from 'eset' column names.
eset	Expression matrix (genes as rows, samples as columns).
signature	List of gene signatures.
mini_gene_count	Minimum genes required per signature. Default is 3.
column_of_sample	Column in 'pdata' with sample IDs. Default is '"ID"'.
adjust_eset	Logical: remove problematic features. Default is 'FALSE'.

Value

Tibble with signature scores.

Author(s)

Dongqiang Zeng

Examples

set.seed(123)
eset <- matrix(rnorm(1000), nrow = 100, ncol = 10)
rownames(eset) <- paste0("Gene", 1:100)
colnames(eset) <- paste0("Sample", 1:10)
signature <- list(
  Signature1 = paste0("Gene", 1:10),
  Signature2 = paste0("Gene", 11:20)
)

result <- calculate_sig_score_pca(eset = eset, signature = signature)

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