Calculate Signature Score Using PCA, Mean, or Z-score Methods

Computes signature scores from gene expression data using Principal Component Analysis (PCA), mean-based, or z-score approaches.

Usage

sigScore(eset, methods = c("PCA", "mean", "zscore"))

Arguments

eset

Normalized expression matrix with genes (signature) as rows and samples as columns.

methods

Scoring method: `"PCA"` (default) for principal component 1, `"mean"` for mean expression, or `"zscore"` for z-score normalized mean.

Value

Numeric vector of length `ncol(eset)`; a score summarizing the rows of `eset`.

Author

Dorothee Nickles, Dongqiang Zeng

Examples

# Load example data
eset_stad <- load_data("eset_stad")
eset <- count2tpm(eset_stad, idType = "ensembl")
#> ℹ Using local annotation (anno_grch38) for TPM conversion
#> ! Omitting 3985 genes without length information
#> Warning: longer object length is not a multiple of shorter object length
#> ℹ No duplicate gene symbols found.

# Get signature genes
signature_tme <- load_data("signature_tme")
genes <- signature_tme[["CD_8_T_effector"]]
genes <- genes[genes %in% rownames(eset)]

# Calculate scores (only if enough genes are available)
if (length(genes) >= 2) {
  score_pca <- sigScore(eset = eset[genes, ], methods = "PCA")
  score_mean <- sigScore(eset = eset[genes, ], methods = "mean")
  score_zscore <- sigScore(eset = eset[genes, ], methods = "zscore")
}