Computes TF-modules pathway activities scores — compute.pathway.activity • CellTFusion

This function computes pathway activity scores from normalized gene expression data using a multivariate linear model (MLM) based on the PROGENy resource (Schubert et al., 2018). Optionally, it also performs Gene Set Variation Analysis (GSVA) using hallmark signatures or any user-provided gene sets.

Usage

# S3 method for class 'pathway.activity'
compute(
  RNA.tpm,
  gene_sets = NULL,
  paths = NULL,
  return = TRUE,
  file.name = NULL
)

Arguments

RNA.tpm: A numeric matrix of normalized gene expression values with genes as rows and samples as columns.
gene_sets: A list of gene sets (e.g., hallmark signatures or user-defined sets). If provided, GSVA scores will be computed for these sets. Default is NULL.
paths: A data frame describing the pathway-gene interactions for use with PROGENy. If NULL, the human PROGENy resource (top 500 genes) will be used by default.
return: Logical; if TRUE, saves matrices in Results/ folder. Default is TRUE.
file.name: Optional character suffix used when writing output CSV files.

Value

If gene_sets is NULL, a scaled matrix of PROGENy pathway activity scores (samples as rows, pathways as columns). If gene_sets is provided, a list with two elements:

sample_acts_progeny: A scaled matrix of PROGENy pathway activity scores.
sample_acts_gsva: A scaled matrix of GSVA scores based on the provided gene sets.

References

Schubert M, Klinger B, Kluenemann M, Sieber A, Uhlitz F, Sauer S, Garnett MJ, Bluethgen N, Saez-Rodriguez J. Perturbation-response genes reveal signaling footprints in cancer gene expression. Nature Communications. 2018. doi:10.1038/s41467-017-02391-6

Examples

# Compute only PROGENy activities
data("counts.norm.tuto")
pathways <- compute.pathway.activity(counts.norm.tuto)
#> Using cached PROGENy pathways collection from  Results/Pathways_collection_PROGENy.csv