LimROTS: A Hybrid Method Integrating Empirical Bayes and Reproducibility-Optimized Statistics for Robust Differential Expression Analysis
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Motivation
Differential expression analysis plays a vital role in omics research enabling precise identification of features that associate with different phenotypes. This process is critical for uncovering biological differences between conditions, such as disease versus healthy states. In proteomics, several statistical methods have been used, ranging from simple t-tests to more advanced methods like limma and ROTS. However, a flexible method for reproducibility-optimized statistics tailored for clinical omics data has been lacking.
Results
In this study, we developed LimROTS, a hybrid method integrating the linear model and empirical Bayes method from the limma framework with the Reproducibility-Optimized Statistics from ROTS, to create a novel moderated ranking statistic, for robust and flexible analysis of proteomics data. We validated its performance using twenty-one proteomics gold standard spike-in datasets with different protein mixtures, MS instruments, and techniques for benchmarking. This hybrid approach improves accuracy and reproducibility of complex proteomics data, making LimROTS a powerful tool for high-dimensional omics data analysis.
Availability and Implementation
LimROTS has been implemented as an R/Bioconductor package, available at https://bioconductor.org/packages/LimROTS/ . Additionally, the code used in this study is available in GitHub repository https://github.com/AliYoussef96/LimROTSmanuscript
