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  Model-based gene set analysis for Bioconductor

Bauer, S., Robinson, P. N., & Gagneur, J. (2011). Model-based gene set analysis for Bioconductor. Bioinformatics, 27(13), 1882-3. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/21561920 http://bioinformatics.oxfordjournals.org/content/27/13/1882.full.pdf.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0010-7838-1 Version Permalink: http://hdl.handle.net/11858/00-001M-0000-0010-7839-0
Genre: Journal Article

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Bauer, S.1, Author
Robinson, P. N.2, Author              
Gagneur, J., Author
Affiliations:
1Max Planck Society, ou_persistent13              
2Research Group Development & Disease (Head: Stefan Mundlos), Max Planck Institute for Molecular Genetics, Max Planck Society, ou_1433557              

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Free keywords: Algorithms; Gene Expression Profiling/*methods; *Models, Genetic; Oligonucleotide Array Sequence Analysis; Programming Languages; *Software
 Abstract: 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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 Dates: 2011
 Publication Status: Published in print
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Title: Bioinformatics
Source Genre: Journal
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Pages: - Volume / Issue: 27 (13) Sequence Number: - Start / End Page: 1882 - 3 Identifier: ISSN: 1367-4811 (Electronic) 1367-4803 (Linking)