LimROTS: A Hybrid Method Integrating Empirical Bayes and Reproducibility-Optimized Statistics for Robust Differential Expression Analysis

issues pulls R-CMD-check

Differential expression analysis is a prevalent method utilised in the examination of diverse biological data. The reproducibility-optimized test statistic (ROTS) (Tomi Suomi et al.,) has been developed with a modified t-statistic based on the data’s intrinsic characteristics and ranks features according to their statistical significance for differential expression between two or more groups, as shown by the f-statistic. Focusing on proteomics and metabolomics, the current ROTS implementation cannot account for technical or biological covariates such as MS batches or gender differences among the samples. Consequently, we developed LimROTS, which employs a reproducibility-optimized test statistic utilizing the limma empirical bayes (Ritchie ME et al.,) methodology to simulate more complex experimental designs.

Installation instructions

The package is available on Bioconductor release version. To install it, follow these steps,

if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("LimROTS")

Option 2: Install from GitHub

You can install the package directly from GitHub,

if (!requireNamespace("LimROTS", quietly = TRUE)) {
  remotes::install_github("AliYoussef96/LimROTS")
}

Code of Conduct

Please note that the LimROTS project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms. Contributions are welcome in the form of feedback, issues and pull requests. You can find the contributor guidelines of the LimROTS here.

Acknowledgements

Please note that LimROTS was only made possible thanks to many other R and rOpenGov software authors, which are cited in the vignettes describing this package.

This package was developed using the following resources:

  • usethis to generate an initial template.
  • Continuous code testing is performed on GitHub actions and include R CMD check,
  • Code coverage assessment is possible thanks to codecov.
  • The documentation website is automatically updated thanks to pkgdown.
  • The documentation is formatted thanks to devtools and roxygen2.

Disclaimer

LimROTS is a standalone package that extends ROTS (Elo et al., 2008; Tomi Suomi et al., 2017) and the limma (Ritchie ME et al., 2015) framework. It is a newly developed, independently implemented method that incorporates covariates in reproducibility-optimized testing. LimROTS is not affiliated with, endorsed by, or maintained by the original ROTS or limma developers. Users are advised to cite the original publications when referencing these methods.

ROTS: Elo LL, Filén S, Lahesmaa R, Aittokallio T. Reproducibility-optimized test statistic for ranking genes in microarray studies. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 2008;5(3):423–31.

Suomi T, Seyednasrollah F, Jaakkola MK, Faux T, Elo LL (2017) ROTS: An R package for reproducibility-optimized statistical testing. PLOS Computational Biology 13(5): e1005562. https://doi.org/10.1371/journal.pcbi.1005562

limma: Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, Smyth GK. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Research. 2015;43(7):e47. https://doi.org/10.1093/nar/gkv007

Citation

If you use LimROTS in your research, please cite our publication:

Anwar, A. M., Jeba, A., Lahti, L., & Coffey, E. (2025). LimROTS: A Hybrid Method Integrating Empirical Bayes and Reproducibility-Optimized Statistics for Robust Differential Expression Analysis. Bioinformatics, btaf570. https://doi.org/10.1093/bioinformatics/btaf570