ROTS: An R package for reproducibility-optimized statistical testing

Tomi Suomi, Fatemeh Seyednasrollah, Maria K Jaakkola, Thomas Faux and Laura L Elo

PLOS Computational Biology, 2017, vol. 13, issue 5, 1-10

Abstract: Differential expression analysis is one of the most common types of analyses performed on various biological data (e.g. RNA-seq or mass spectrometry proteomics). It is the process that detects features, such as genes or proteins, showing statistically significant differences between the sample groups under comparison. A major challenge in the analysis is the choice of an appropriate test statistic, as different statistics have been shown to perform well in different datasets. To this end, the reproducibility-optimized test statistic (ROTS) adjusts a modified t-statistic according to the inherent properties of the data and provides a ranking of the features based on their statistical evidence for differential expression between two groups. ROTS has already been successfully applied in a range of different studies from transcriptomics to proteomics, showing competitive performance against other state-of-the-art methods. To promote its widespread use, we introduce here a Bioconductor R package for performing ROTS analysis conveniently on different types of omics data. To illustrate the benefits of ROTS in various applications, we present three case studies, involving proteomics and RNA-seq data from public repositories, including both bulk and single cell data. The package is freely available from Bioconductor (https://www.bioconductor.org/packages/ROTS).

Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1005562

DOI: 10.1371/journal.pcbi.1005562

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