# Bioconductor Gsva

> 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 fr

- Skill: `biomate-ai/bioconductor-gsva` (Agent Skill)
- Install (CLI): `npx skillmds@latest add biomate-ai/bioconductor-gsva`
- Raw SKILL.md: https://api.skillmd.com/api/skills/biomate-ai/bioconductor-gsva/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: biomate-ai (https://skillmd.com/u/biomate-ai)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/biomate-ai/bioconductor-gsva

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# GSVA

## Dependencies & Environment

> Package-intrinsic requirements from the Bioconductor landing page — reproduce in any R environment.

- **Version:** 2.6.2 · **Bioconductor:** 3.23 · **R:** ≥ 4.6
- **Imports:** BiocGenerics, MatrixGenerics, S4Vectors, S4Arrays, HDF5Array, SparseArray, DelayedArray, IRanges, Biobase, SummarizedExperiment, GSEABase, Matrix, DelayedMatrixStats, BiocParallel, SingleCellExperiment, BiocSingular, SpatialExperiment, sparseMatrixStats, cli, memuse
- **Install:** `BiocManager::install("GSVA")`

## When to Use
- **Sample-Wise Pathway Scoring**: Calculating sample-specific pathway enrichment scores from a gene expression matrix using `gsva`.
- **Dimensionality Reduction**: Transforming a high-dimensional gene-by-sample matrix into a lower-dimensional pathway-by-sample matrix for downstream analysis.
- **Method-Specific Enrichment**: Applying specific single-sample enrichment methods like PLAGE, z-score, or ssGSEA using `plageParam`, `zscoreParam`, or `ssgseaParam`.

## When NOT to Use
- **For Simple Differential Expression**: For simple differential expression of individual genes, use `edgeR` because it is designed for gene-level count data modeling rather than gene set enrichment.
- **For Purely Managing Gene Sets**: For purely importing and managing gene set collections without analysis, use `GSEABase` because it provides dedicated classes like `GeneSetCollection` and functions like `getGmt`.

## Data Requirements
- **Expression Matrix**: A normalized gene expression matrix, an `ExpressionSet`, or a `SummarizedExperiment` object.
- **Gene Sets**: A `list` object of gene identifiers or a `GeneSetCollection` object.

## Key Parameters
- **kcdf** ("auto"): Kernel consensus density estimation; use `"Gaussian"` for continuous data or `"Poisson"` for integer counts.
- **kcdfNoneMinSampleSize** (200): Minimum sample size at which the empirical cumulative distribution function is estimated directly without a kernel.
- **tau** (1): Exponent defining the weight of the tail in the random walk.
- **maxDiff** (TRUE): Logical flag specifying whether to calculate the enrichment score as the magnitude difference between the largest positive and negative random walk deviations.
- **absRanking** (FALSE): Logical flag implying whether a modified Kuiper statistic is used.
- **sparse** (FALSE): Logical flag used when the input expression data is stored in a sparse matrix.
- **minSize** (1): Minimum size of gene sets to be considered.
- **maxSize** (Inf): Maximum size of gene sets to be considered.

## Best Practices
- **Match Identifiers**: Ensure gene identifiers between the expression data matrix and the gene sets belong to the same standard nomenclature to allow `mapIdentifiers` to work.
- **Set the Correct Kernel**: Use `kcdf="Poisson"` when providing RNA-seq integer count data directly to `gsvaParam`.
- **Import GMTs Safely**: Use `readGMT` to import gene sets from GMT files, as it handles duplicated gene set names better than base R functions.

## Common Pitfalls
- **Providing Raw Counts with Gaussian Kernel**: Running GSVA on raw RNA-seq counts without changing the kernel. Fix: Set `kcdf="Poisson"` in `gsvaParam` when using integer counts.
- **Identifier Mismatch**: Gene identifiers do not match between the expression matrix and gene sets. Fix: Provide data using specialized containers (like `ExpressionSet` or `SummarizedExperiment`) so GSVA can automatically map identifiers.
- **Duplicated Gene Set Names**: Duplicated gene set names in GMT files causing errors with standard import functions. Fix: Use the `readGMT` function which deduplicates gene set names by default.

## Alternatives
- `GSEABase`: For foundational gene set data structures and basic GMT file importing.
- `qusage`: Provides alternative functions like `read.gmt` for reading gene sets.
- `edgeR`: For gene-level differential expression analysis using log-CPM units.

## Citations
- Hänzelmann S, Castelo R, Guinney J. "GSVA: gene set variation analysis for microarray and RNA-seq data." BMC Bioinformatics, 2013.
- Barbie et al. "Systematic RNA interference reveals that oncogenic KRAS-driven cancers require TBK1." Nature, 2009.

## References
- Homepage: https://bioconductor.org/packages/GSVA
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/GSVA/inst/doc/GSVA.html

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## Run this on BioMate

This skill is the **knowledge layer** — when, why, and how to use `gsva`. To **run this analysis on your own data** with managed compute, automated QC, and reproducible outputs, use **[BioMate](https://www.biomate.ai?ref=kb&pkg=gsva)** — free to start.

▶ **[Open `gsva` on BioMate →](https://www.biomate.ai?ref=kb&pkg=gsva)**