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RankProd 2.0: a refactored bioconductor package for detecting differentially expressed features in molecular profiling datasets
MOTIVATION: The Rank Product (RP) is a statistical technique widely used to detect differentially expressed features in molecular profiling experiments such as transcriptomics, metabolomics and proteomics studies. An implementation of the RP and the closely related Rank Sum (RS) statistics has been...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5860065/ https://www.ncbi.nlm.nih.gov/pubmed/28481966 http://dx.doi.org/10.1093/bioinformatics/btx292 |
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author | Del Carratore, Francesco Jankevics, Andris Eisinga, Rob Heskes, Tom Hong, Fangxin Breitling, Rainer |
author_facet | Del Carratore, Francesco Jankevics, Andris Eisinga, Rob Heskes, Tom Hong, Fangxin Breitling, Rainer |
author_sort | Del Carratore, Francesco |
collection | PubMed |
description | MOTIVATION: The Rank Product (RP) is a statistical technique widely used to detect differentially expressed features in molecular profiling experiments such as transcriptomics, metabolomics and proteomics studies. An implementation of the RP and the closely related Rank Sum (RS) statistics has been available in the RankProd Bioconductor package for several years. However, several recent advances in the understanding of the statistical foundations of the method have made a complete refactoring of the existing package desirable. RESULTS: We implemented a completely refactored version of the RankProd package, which provides a more principled implementation of the statistics for unpaired datasets. Moreover, the permutation-based P-value estimation methods have been replaced by exact methods, providing faster and more accurate results. AVAILABILITY AND IMPLEMENTATION: RankProd 2.0 is available at Bioconductor (https://www.bioconductor.org/packages/devel/bioc/html/RankProd.html) and as part of the mzMatch pipeline (http://www.mzmatch.sourceforge.net). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. |
format | Online Article Text |
id | pubmed-5860065 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-58600652018-03-23 RankProd 2.0: a refactored bioconductor package for detecting differentially expressed features in molecular profiling datasets Del Carratore, Francesco Jankevics, Andris Eisinga, Rob Heskes, Tom Hong, Fangxin Breitling, Rainer Bioinformatics Applications Notes MOTIVATION: The Rank Product (RP) is a statistical technique widely used to detect differentially expressed features in molecular profiling experiments such as transcriptomics, metabolomics and proteomics studies. An implementation of the RP and the closely related Rank Sum (RS) statistics has been available in the RankProd Bioconductor package for several years. However, several recent advances in the understanding of the statistical foundations of the method have made a complete refactoring of the existing package desirable. RESULTS: We implemented a completely refactored version of the RankProd package, which provides a more principled implementation of the statistics for unpaired datasets. Moreover, the permutation-based P-value estimation methods have been replaced by exact methods, providing faster and more accurate results. AVAILABILITY AND IMPLEMENTATION: RankProd 2.0 is available at Bioconductor (https://www.bioconductor.org/packages/devel/bioc/html/RankProd.html) and as part of the mzMatch pipeline (http://www.mzmatch.sourceforge.net). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Oxford University Press 2017-09-01 2017-05-08 /pmc/articles/PMC5860065/ /pubmed/28481966 http://dx.doi.org/10.1093/bioinformatics/btx292 Text en © The Author 2017. Published by Oxford University Press. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Applications Notes Del Carratore, Francesco Jankevics, Andris Eisinga, Rob Heskes, Tom Hong, Fangxin Breitling, Rainer RankProd 2.0: a refactored bioconductor package for detecting differentially expressed features in molecular profiling datasets |
title | RankProd 2.0: a refactored bioconductor package for detecting differentially expressed features in molecular profiling datasets |
title_full | RankProd 2.0: a refactored bioconductor package for detecting differentially expressed features in molecular profiling datasets |
title_fullStr | RankProd 2.0: a refactored bioconductor package for detecting differentially expressed features in molecular profiling datasets |
title_full_unstemmed | RankProd 2.0: a refactored bioconductor package for detecting differentially expressed features in molecular profiling datasets |
title_short | RankProd 2.0: a refactored bioconductor package for detecting differentially expressed features in molecular profiling datasets |
title_sort | rankprod 2.0: a refactored bioconductor package for detecting differentially expressed features in molecular profiling datasets |
topic | Applications Notes |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5860065/ https://www.ncbi.nlm.nih.gov/pubmed/28481966 http://dx.doi.org/10.1093/bioinformatics/btx292 |
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