RankProd 2.0: a refactored Bioconductor package for detecting differentially expressed features in molecular profiling datasets

Francesco Del Carratore; Andris Jankevics; Rob Eisinga; Tom Heskes; Fangxin Hong; Rainer Breitling

doi:10.1093/bioinformatics/btx292

RankProd 2.0: a refactored Bioconductor package for detecting differentially expressed features in molecular profiling datasets

Francesco Del Carratore, Andris Jankevics, Rob Eisinga, Tom Heskes, Fangxin Hong, Rainer Breitling

Research output: Contribution to journal › Article › peer-review

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 Moreover, the permutation-based p-value estimation methods have been replaced by exact methods, providing faster and more accurate results.

Original language	English
Journal	Bioinformatics
Early online date	8 May 2017
DOIs	https://doi.org/10.1093/bioinformatics/btx292
Publication status	Published - 2017

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Manchester Institute of Biotechnology

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10.1093/bioinformatics/btx292Licence: CC BY

Manchester Synthetic Biology Research Centre for Fine and Speciality Chemicals
Scrutton, N., Azapagic, A., Balmer, A., Barran, P., Breitling, R., Delneri, D., Dixon, N., Faulon, J., Flitsch, S., Goble, C., Goodacre, R., Hay, S., Kell, D., Leys, D., Lloyd, J., Lockyer, N., Martin, P., Micklefield, J., Munro, A., Pedrosa Mendes, P., Randles, S., Salehi Yazdi, F., Shapira, P., Takano, E., Turner, N. & Winterburn, J.
14/11/14 → 13/05/20
Project: Research

Cite this

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title = "RankProd 2.0: a refactored Bioconductor package for detecting differentially expressed features in molecular profiling datasets",

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 beenavailable 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.",

author = "{Del Carratore}, Francesco and Andris Jankevics and Rob Eisinga and Tom Heskes and Fangxin Hong and Rainer Breitling",

year = "2017",

doi = "10.1093/bioinformatics/btx292",

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AU - Del Carratore, Francesco

AU - Jankevics, Andris

AU - Eisinga, Rob

AU - Heskes, Tom

AU - Hong, Fangxin

AU - Breitling, Rainer

PY - 2017

Y1 - 2017

N2 - 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 beenavailable 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.

AB - 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 beenavailable 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.

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DO - 10.1093/bioinformatics/btx292

M3 - Article

SN - 1460-2059

JO - Bioinformatics

JF - Bioinformatics

ER -

RankProd 2.0: a refactored Bioconductor package for detecting differentially expressed features in molecular profiling datasets

Abstract

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Manchester Synthetic Biology Research Centre for Fine and Speciality Chemicals

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