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Title: RankProd 2.0: A refactored bioconductor package for detecting differentially expressed features in molecular profiling datasets
Author(s): Carratore, F. del; Jankevics, A.; Eisinga, R.N. ; Heskes, T. ; Hong, F.; Breitling, R.
Publication year: 2017
Source: Bioinformatics, vol. 33, iss. 17, (2017), pp. 2774-2775
Number of Pages: 2 p.
ISSN: 1367-4803
DOI: https://doi.org/10.1093/bioinformatics/btx292
Publication type: Article / Letter to editor

Please use this identifier to cite or link to this item : http://hdl.handle.net/2066/175881   http://hdl.handle.net/2066/175881

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Subject: Biophysics
Data Science
Inequality Cohesion Rationalization
Ongelijkheid Cohesie Rationalisatie
Organization: SW OZ NISCO SOC
Data Science
Donders Centre for Neuroscience
Journal title:
Bioinformatics
Volume: vol. 33
Issue: iss. 17
Page start: p. 2774
Page end: p. 2775
Abstract:
Motivation: The Rank Products 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 Rank Product (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 Moreover, the permutation-based p-value estimation methods have been replaced by exact methods, providing faster and more accurate results.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: 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).
Languages used: English (eng)

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