LimROTS

LimROTS implements a hybrid method for robust differential expression analysis in proteomics data by integrating linear models and empirical Bayes methods with Reproducibility-Optimized Statistics.

Key Features:

• Hybrid Statistical Framework: Combines the linear model and empirical Bayes method from the limma framework with Reproducibility-Optimized Statistics from ROTS to create a novel moderated ranking statistic.
• Benchmarking Validation: Performance was validated using twenty-one proteomics gold standard spike-in datasets with different protein mixtures, MS instruments, and techniques.
• R/Bioconductor Implementation: Implemented as an R/Bioconductor package for high-dimensional omics data analysis.

Scientific Applications:

• Proteomics Differential Expression: Identifies features that associate with different phenotypes in proteomics data, such as disease versus healthy states.
• Clinical Omics Analysis: Provides reproducibility-optimized statistics tailored for clinical omics data analysis.

Methodology:

LimROTS integrates 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.