GSVA: gene set variation analysis for microarray and RNA-Seq data

By Sonja Hänzelmann, Robert Castelo, Justin Guinney. BMC Bioinformatics, 2013

12,458 citations recorded by the source corpus.

Gene set enrichment (GSE) analysis is a popular framework for condensing information from gene expression profiles into a pathway or signature summary. The strengths of this approach over single gene analysis include noise and dimension reduction, as well as greater biological interpretability. As molecular profiling experiments move beyond simple case-control studies, robust and flexible GSE methodologies are needed that can model pathway activity within highly heterogeneous data sets. To address this challenge, we introduce Gene Set Variation Analysis (GSVA), a GSE method that estimates variation of pathway activity over a sample population in an unsupervised manner. We demonstrate the robustness of GSVA in a comparison with current state of the art sample-wise enrichment methods. Further, we provide examples of its utility in differential pathway activity and survival analysis. Lastly, we show how GSVA works analogously with data from both microarray and RNA-seq experiments. GSVA provides increased power to detect subtle pathway activity changes over a sample population in comparison to corresponding methods. While GSE methods are generally regarded as end points of a bioinformatic analysis, GSVA constitutes a starting point to build pathway-centric models of biology. Moreover, GSVA contributes to the current need of GSE methods for RNA-seq data. GSVA is an open source software package for R which forms part of the Bioconductor project and can be downloaded at http://www.bioconductor.org.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/3af045503abf81705fc1833aadf5db93a9be854c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

Citations in the corpus

ReactomeGSA - Efficient Multi-Omics Comparative Pathway Analysis (2020, methodology): “For the ssGSEA method 12 the analysis is performed using the GSVA Bioconductor R package 13 .”
Best practices for single-cell analysis across modalities (2023, neutral): “3).”
The tumor microenvironment underlies acquired resistance to CSF1R inhibition in gliomas (2016, methodology): “S. Hänzelmann, R. Castelo, J. Guinney, GSVA: Gene set variation analysis for microarray and RNA-seq data.”
Integrative Clinical Genomics of Metastatic Cancer (2017, positive): “Inference of patient-specific activities 27,28 revealed coordinated changes across curated pathways that coalesce into a small number of principal cancer hallmarks (Extended Data Fig.”
Tumor immune microenvironment characterization in clear cell renal cell carcinoma identifies prognostic and immunotherapeutically relevant messenger RNA signatures (2016, methodology): “levels for angiogenesis and antigen presentation were quantified using the ssGSEA [30] implementation in R package gsva [71].”
CancerSEA: a cancer single-cell state atlas (2018, methodology): “Based on these signatures, the activities of 14 functional states across cancer single cells in each dataset were evaluated using Gene Set Variation Analysis (GSVA) with the GSVA package in R (22).”