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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...

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Autores principales: Del Carratore, Francesco, Jankevics, Andris, Eisinga, Rob, Heskes, Tom, Hong, Fangxin, Breitling, Rainer
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2017
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 Moreover, the permutation-based P-value estimation methods have been replaced by exact methods, providing faster and more accurate results.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.
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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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