GSVA
Bioc currentGene Set Variation Analysis for Microarray and RNA-Seq Data
Release Lineage
Entered 2.8 · Apr 14, 2011
Current · Requires R 4.6
Description
Gene Set Variation Analysis (GSVA) is a non-parametric, unsupervised method for estimating variation of gene set enrichment through the samples of a expression data set. GSVA performs a change in coordinate systems, transforming the data from a gene by sample matrix to a gene-set by sample matrix, thereby allowing the evaluation of pathway enrichment for each sample. This new matrix of GSVA enrichment scores facilitates applying standard analytical methods like functional enrichment, survival analysis, clustering, CNV-pathway analysis or cross-tissue pathway analysis, in a pathway-centric manner.
None published for Bioconductor
Test coverage
Line coverage
–
Expression
–
Tests / Examples
–
Functions
166 11 exported
Complexity
4.5 avg / 22 max
Call network
166 nodes / 163 edges
Test coverage is not measured for Bioconductor packages; nodes fall back to a neutral fill.
Call graph
Open call graph →Lowest coverage
Per-function coverage is not measured for this package yet.
Code
Structure
Lines of code
15,304
Files
99
Compiled share
20.7%
Has compiled src
Yes
Language breakdown
API
Exported functions
23
Internal functions
110
Recent export changes
Testing
Has tests
Yes
Test-to-code ratio
0.08
testthat edition
–
Docs
Roxygen coverage
100%
Health & Security signals
Informational signals; not verdicts.
on.exit coverage
0%
Unsafe pattern score
0
Dep constraint coverage
4.2%
Secret pattern count
0
Bundled 3rd-party code
2 items
Portability & License
Min R version
4.0.0
System requirements
–
C++ standard
–
License
Artistic-2.0
License flags
SPDX valid, OSI approved
History
Versions
31
First release
2011-06-16
Latest release
2026-08-04
Avg cadence
183 days
Cold removal rate
–
Dep drift
35
LOC over versions
Per-file churn detail lives in the source pipeline: https://github.com/r-observatory/bioc-code-metrics.
Documentation
- Help pages
- 23 (2 internal)
- Examples that run
- 95%19 run in a plain check · 1 only when donttest examples are enabled (R CMD check --as-cran)
- Exported functions with examples
- 100%
- Documented parameters
- 75%
- Return-value docs
- 100%
- Help pages citing references
- 43%
Repository practices
4 development-tooling and community-health practices detected across 2 families in the upstream repository
Checks run against github.com/rcastelo/gsva on 2026-10-21.
The R CMD check workflow runs on Linux
Topics
Depended on by (25)
Show all 34 packagesHide all 34 packages
Bioconductor (15)
CRAN (10)
Suggested by (9)
People
- Robert Castelo author maintainer
- Chan Zuckerberg Initiative (CZI) funder
- Spanish Ministry of Science, Innovation and Universities (MCIU) funder
- Justin Guinney author
- Axel Klenk contributor
- Pablo Sebastian Rodriguez contributor
- Alexey Sergushichev contributor
Cite
Cite this package
Run in R for the authors' entry:
citation("GSVA")The package provides a citation file, so citation() returns the authors' own entry.
It points to 10.1186/1471-2105-14-7.
Cite the R Observatory
For a number measured here: a download total, a coverage figure, an archival date.
From data release v2026-10-24, which the citation names so these numbers can be found later. More on citing and the projects behind them.