Gsva Tools

Configuration File: gsva_tools.json Tool Type: Local Tools Count: 1

This page contains all tools defined in the gsva_tools.json configuration file.

Available Tools

GSVA_score (Type: GSVATool)

Gene Set Variation Analysis (Hänzelmann 2013): turn an expression matrix (genes x samples) into a…

GSVA_score tool specification

Tool Information:

  • Name: GSVA_score

  • Type: GSVATool

  • Description: Gene Set Variation Analysis (Hänzelmann 2013): turn an expression matrix (genes x samples) into a label-free gene_set x sample pathway-activity matrix. Unlike ssGSEA, GSVA first converts each gene to a kernel-CDF quantile across samples, then runs a symmetric KS walk with a max-deviation difference score (mx_diff), giving signed scores centered near zero (positive = set up-regulated in that sample vs the cohort). Pure-compute (no R); supply log-scale expression + gene sets from MSigDB/Enrichr.

Parameters:

  • expression (object) (required) {gene: [value_per_sample, …]} — a genes x samples expression matrix on a continuous (e.g. log-normalized) scale.

  • samples ([‘array’, ‘null’]) (optional) Sample names (same order as the per-gene value lists); defaults to sample_1..

  • gene_sets ([‘object’, ‘null’]) (optional) {set_name: [gene, …]} collection to score. Alternative to gene_set.

  • gene_set ([‘array’, ‘null’]) (optional) A single gene set (list of gene symbols).

  • tau ([‘number’, ‘null’]) (optional) Rank-weighting exponent in the random walk (default 1.0, the GSVA standard).

  • mx_diff ([‘boolean’, ‘null’]) (optional) True (default) = ES_pos+ES_neg difference score (signed, bimodal, GSVA default); False = classic single max-deviation KS statistic.

Example Usage:

query = {
    "name": "GSVA_score",
    "arguments": {
        "expression": "example_value"
    }
}
result = tu.run(query)