ROTS: An R package for reproducibility-optimized statistical testing

• DOI: 10.1371/journal.pcbi.1005562
• Publication Date: 2017-05-25T13:26:53Z
• Authors: Tomi Suomi; Fatemeh Seyednasr...; Maria K. Jaakkola; Thomas Faux; Laura L. Elo
• Keywords: Bioconductor; Statistic; R package; Sample (material); False Discovery Rate

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