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GOing Bayesian: model-based gene set analysis of genome-scale data
The interpretation of data-driven experiments in genomics often involves a search for biological categories that are enriched for the responder genes identified by the experiments. However, knowledge bases such as the Gene Ontology (GO) contain hundreds or thousands of categories with very high over...
Autores principales: | , , |
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Formato: | Texto |
Lenguaje: | English |
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Oxford University Press
2010
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2887944/ https://www.ncbi.nlm.nih.gov/pubmed/20172960 http://dx.doi.org/10.1093/nar/gkq045 |
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author | Bauer, Sebastian Gagneur, Julien Robinson, Peter N. |
author_facet | Bauer, Sebastian Gagneur, Julien Robinson, Peter N. |
author_sort | Bauer, Sebastian |
collection | PubMed |
description | The interpretation of data-driven experiments in genomics often involves a search for biological categories that are enriched for the responder genes identified by the experiments. However, knowledge bases such as the Gene Ontology (GO) contain hundreds or thousands of categories with very high overlap between categories. Thus, enrichment analysis performed on one category at a time frequently returns large numbers of correlated categories, leaving the choice of the most relevant ones to the user's; interpretation. Here we present model-based gene set analysis (MGSA) that analyzes all categories at once by embedding them in a Bayesian network, in which gene response is modeled as a function of the activation of biological categories. Probabilistic inference is used to identify the active categories. The Bayesian modeling approach naturally takes category overlap into account and avoids the need for multiple testing corrections met in single-category enrichment analysis. On simulated data, MGSA identifies active categories with up to 95% precision at a recall of 20% for moderate settings of noise, leading to a 10-fold precision improvement over single-category statistical enrichment analysis. Application to a gene expression data set in yeast demonstrates that the method provides high-level, summarized views of core biological processes and correctly eliminates confounding associations. |
format | Text |
id | pubmed-2887944 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2010 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-28879442010-06-22 GOing Bayesian: model-based gene set analysis of genome-scale data Bauer, Sebastian Gagneur, Julien Robinson, Peter N. Nucleic Acids Res Computational Biology The interpretation of data-driven experiments in genomics often involves a search for biological categories that are enriched for the responder genes identified by the experiments. However, knowledge bases such as the Gene Ontology (GO) contain hundreds or thousands of categories with very high overlap between categories. Thus, enrichment analysis performed on one category at a time frequently returns large numbers of correlated categories, leaving the choice of the most relevant ones to the user's; interpretation. Here we present model-based gene set analysis (MGSA) that analyzes all categories at once by embedding them in a Bayesian network, in which gene response is modeled as a function of the activation of biological categories. Probabilistic inference is used to identify the active categories. The Bayesian modeling approach naturally takes category overlap into account and avoids the need for multiple testing corrections met in single-category enrichment analysis. On simulated data, MGSA identifies active categories with up to 95% precision at a recall of 20% for moderate settings of noise, leading to a 10-fold precision improvement over single-category statistical enrichment analysis. Application to a gene expression data set in yeast demonstrates that the method provides high-level, summarized views of core biological processes and correctly eliminates confounding associations. Oxford University Press 2010-06 2010-02-19 /pmc/articles/PMC2887944/ /pubmed/20172960 http://dx.doi.org/10.1093/nar/gkq045 Text en © The Author(s) 2010. 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 | Computational Biology Bauer, Sebastian Gagneur, Julien Robinson, Peter N. GOing Bayesian: model-based gene set analysis of genome-scale data |
title | GOing Bayesian: model-based gene set analysis of genome-scale data |
title_full | GOing Bayesian: model-based gene set analysis of genome-scale data |
title_fullStr | GOing Bayesian: model-based gene set analysis of genome-scale data |
title_full_unstemmed | GOing Bayesian: model-based gene set analysis of genome-scale data |
title_short | GOing Bayesian: model-based gene set analysis of genome-scale data |
title_sort | going bayesian: model-based gene set analysis of genome-scale data |
topic | Computational Biology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2887944/ https://www.ncbi.nlm.nih.gov/pubmed/20172960 http://dx.doi.org/10.1093/nar/gkq045 |
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