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


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Article Info

DATE

2013-01-16

AUTHORS

Sonja Hänzelmann, Robert Castelo, Justin Guinney

ABSTRACT

BackgroundGene 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.ResultsTo 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.ConclusionsGSVA 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. More... »

PAGES

7

References to SciGraph publications

  • 2008-10-20. Expression-based Pathway Signature Analysis (EPSA): Mining publicly available microarray data for insight into human disease in BMC MEDICAL GENOMICS
  • <error retrieving object. in <ERROR RETRIEVING OBJECT
  • 2009-10-21. Systematic RNA interference reveals that oncogenic KRAS-driven cancers require TBK1 in NATURE
  • 1985-12. Comparing partitions in JOURNAL OF CLASSIFICATION
  • 2011-06-29. Integrated genomic analyses of ovarian carcinoma in NATURE
  • 2005-03. X-inactivation profile reveals extensive variability in X-linked gene expression in females in NATURE
  • 2001-12-03. MLL translocations specify a distinct gene expression profile that distinguishes a unique leukemia in NATURE GENETICS
  • 2008-07-31. Integrative analysis of RUNX1 downstream pathways and target genes in BMC GENOMICS
  • 2006-10-17. Pathway and gene-set activation measurement from mRNA expression data: the tissue distribution of human pathways in GENOME BIOLOGY
  • 2005-06-19. An integrative genomics approach to infer causal associations between gene expression and disease in NATURE GENETICS
  • 2008-05-30. Mapping and quantifying mammalian transcriptomes by RNA-Seq in NATURE METHODS
  • 2007-09-30. A gene expression bar code for microarray data in NATURE METHODS
  • 2010-03-10. Understanding mechanisms underlying human gene expression variation with RNA sequencing in NATURE
  • 2003-06-15. PGC-1α-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes in NATURE GENETICS
  • 2005-06-08. PAGE: Parametric Analysis of Gene Set Enrichment in BMC BIOINFORMATICS
  • 2005-09-12. Pathway level analysis of gene expression using singular value decomposition in BMC BIOINFORMATICS
  • 2006-08-28. Rb2/p130 and protein phosphatase 2A: key mediators of ovarian carcinoma cell growth suppression by all-trans retinoic acid in ONCOGENE
  • 2004-12-19. An oncogenic KRAS2 expression signature identified by cross-species gene-expression analysis in NATURE GENETICS
  • 2003-06. The male-specific region of the human Y chromosome is a mosaic of discrete sequence classes in NATURE
  • 2010-02-04. Gene ontology analysis for RNA-seq: accounting for selection bias in GENOME BIOLOGY
  • 2004-09-15. Bioconductor: open software development for computational biology and bioinformatics in GENOME BIOLOGY
  • Journal

    TITLE

    BMC Bioinformatics

    ISSUE

    1

    VOLUME

    14

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  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1186/1471-2105-14-7

    DOI

    http://dx.doi.org/10.1186/1471-2105-14-7

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1022196002

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/23323831


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    42 schema:description BackgroundGene 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.ResultsTo 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.ConclusionsGSVA 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.
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