LimROTS

LimROTS integrates a linear regression model and the empirical Bayes approach with reproducibility-optimized statistics to create a moderated ranking statistic for robust differential expression analysis in high-dimensional omics data.

Key Features:

• Hybrid statistical framework: Combines reproducibility-optimized test statistic with the limma empirical Bayes approach to enable analysis of complex experimental designs.
• Covariate incorporation: Accommodates technical and biological covariates, addressing a limitation of the original ROTS Bioconductor implementation.
• Moderated ranking statistic: Creates a novel moderated ranking statistic that improves accuracy and reproducibility for proteomics data analysis.
• Flexible experimental design support: Enables analysis of more complex experimental designs beyond simple two-group comparisons.

Scientific Applications:

• Proteomics differential expression: Identifies proteins that differ between conditions such as disease versus healthy states in proteomics datasets.
• Clinical omics data analysis: Provides reproducibility-optimized statistics tailored for clinical omics data with complex experimental structures.
• Spike-in benchmark validation: Validated using twenty-one proteomics gold standard spike-in datasets with different protein mixtures, MS instruments, and techniques.
• High-dimensional omics analysis: Applicable to diverse high-dimensional omics data types requiring robust differential expression testing.

Methodology:

LimROTS combines a linear regression model and the empirical Bayes approach with reproducibility-optimized statistics to create a moderated ranking statistic. The method was validated using twenty-one proteomics gold standard spike-in datasets with different protein mixtures, MS instruments, and techniques for benchmarking.