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ROTS: An R package for reproducibility-optimized statistical testing.

Tomi Suomi1,2, Fatemeh Seyednasrollah1,3, Maria K Jaakkola1,3

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Differential expression analysis identifies significant gene or protein changes between sample groups. The reproducibility-optimized test statistic (ROTS) method and its R package offer a robust approach for various omics data types.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics and Proteomics

Background:

  • Differential expression analysis is crucial for biological data interpretation.
  • Selecting appropriate statistical methods is a key challenge.
  • Existing methods may not perform optimally across diverse datasets.

Purpose of the Study:

  • Introduce a Bioconductor R package for the reproducibility-optimized test statistic (ROTS) analysis.
  • Facilitate convenient ROTS analysis for various omics data.
  • Demonstrate the utility of ROTS across different biological applications.

Main Methods:

  • Developed a Bioconductor R package implementing the ROTS method.
  • ROTS adjusts a modified t-statistic based on data properties.
  • Applied the package to analyze proteomics and RNA-seq data.

Main Results:

  • The ROTS package provides a user-friendly interface for differential expression analysis.
  • Case studies showcase ROTS performance on bulk and single-cell omics data.
  • ROTS demonstrates competitive performance compared to other methods.

Conclusions:

  • The ROTS R package enhances the accessibility and application of this robust statistical method.
  • ROTS is a valuable tool for identifying differentially expressed features in transcriptomics and proteomics.
  • The package supports reproducible and efficient differential expression analysis across various omics studies.