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Model-based gene set analysis for Bioconductor

Summary: Gene Ontology and other forms of gene-category analysis play a major role in the evaluation of high-throughput experiments in molecular biology. Single-category enrichment analysis procedures such as Fisher's exact test tend to flag large numbers of redundant categories as significant,...

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Detalles Bibliográficos
Autores principales: Bauer, Sebastian, Robinson, Peter N., Gagneur, Julien
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3117381/
https://www.ncbi.nlm.nih.gov/pubmed/21561920
http://dx.doi.org/10.1093/bioinformatics/btr296
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author Bauer, Sebastian
Robinson, Peter N.
Gagneur, Julien
author_facet Bauer, Sebastian
Robinson, Peter N.
Gagneur, Julien
author_sort Bauer, Sebastian
collection PubMed
description Summary: Gene Ontology and other forms of gene-category analysis play a major role in the evaluation of high-throughput experiments in molecular biology. Single-category enrichment analysis procedures such as Fisher's exact test tend to flag large numbers of redundant categories as significant, which can complicate interpretation. We have recently developed an approach called model-based gene set analysis (MGSA), that substantially reduces the number of redundant categories returned by the gene-category analysis. In this work, we present the Bioconductor package mgsa, which makes the MGSA algorithm available to users of the R language. Our package provides a simple and flexible application programming interface for applying the approach. Availability: The mgsa package has been made available as part of Bioconductor 2.8. It is released under the conditions of the Artistic license 2.0. Contact: peter.robinson@charite.de; julien.gagneur@embl.de
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spelling pubmed-31173812011-06-17 Model-based gene set analysis for Bioconductor Bauer, Sebastian Robinson, Peter N. Gagneur, Julien Bioinformatics Applications Note Summary: Gene Ontology and other forms of gene-category analysis play a major role in the evaluation of high-throughput experiments in molecular biology. Single-category enrichment analysis procedures such as Fisher's exact test tend to flag large numbers of redundant categories as significant, which can complicate interpretation. We have recently developed an approach called model-based gene set analysis (MGSA), that substantially reduces the number of redundant categories returned by the gene-category analysis. In this work, we present the Bioconductor package mgsa, which makes the MGSA algorithm available to users of the R language. Our package provides a simple and flexible application programming interface for applying the approach. Availability: The mgsa package has been made available as part of Bioconductor 2.8. It is released under the conditions of the Artistic license 2.0. Contact: peter.robinson@charite.de; julien.gagneur@embl.de Oxford University Press 2011-07-01 2011-05-10 /pmc/articles/PMC3117381/ /pubmed/21561920 http://dx.doi.org/10.1093/bioinformatics/btr296 Text en © The Author(s) 2011. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/2.5 This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.5), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Applications Note
Bauer, Sebastian
Robinson, Peter N.
Gagneur, Julien
Model-based gene set analysis for Bioconductor
title Model-based gene set analysis for Bioconductor
title_full Model-based gene set analysis for Bioconductor
title_fullStr Model-based gene set analysis for Bioconductor
title_full_unstemmed Model-based gene set analysis for Bioconductor
title_short Model-based gene set analysis for Bioconductor
title_sort model-based gene set analysis for bioconductor
topic Applications Note
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3117381/
https://www.ncbi.nlm.nih.gov/pubmed/21561920
http://dx.doi.org/10.1093/bioinformatics/btr296
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