1Faculty of Science and Engineering, Manchester Institute of Biotechnology, University of Manchester, Manchester M1 7DN, UK2Department of Electrical Engineering and Electronics, University of Liverpool, Liverpool L69 3GJ, UK3Department of Social Science Research Methods, Radboud University Nijmegen, Nijmegen 6525 GD, Netherlands4Institute for Computing and Information Sciences, Radboud University Nijmegen, Nijmegen 6525 EC, Netherlands5Harvard School of Public Health, Dana-Farber Cancer Institute, Boston, MA 02115, USA
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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 Moreover, the permutation-based P-value estimation methods have been replaced by exact methods, providing faster and more accurate results.Availability and implementation:RankProd 2.0 is available at Bioconductor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).Contact:firstname.lastname@example.orgSupplementary information:Supplementary data are available at Bioinformatics online.