Benchmarking tools for detecting longitudinal differential expression in proteomics data allows establishing a robust...

Area of Science:

• Proteomics
• Bioinformatics
• Systems Biology

Background:

• Quantitative proteomics is a key tool in biological research.
• Longitudinal proteomics experiments are increasingly common but lack effective analysis methods.
• Existing methods struggle with noisy data, missing values, and limited replicates typical in longitudinal studies.

Purpose of the Study:

• To evaluate existing differential expression methods for longitudinal omics data.
• To introduce a novel, robust method for analyzing longitudinal proteomics data.
• To provide a user-friendly tool for researchers.

Main Methods:

• Comprehensive evaluation of multiple differential expression methods.
• Development and application of the Robust longitudinal Differential Expression (RolDE) approach.
• Testing on over 3000 simulated and three large experimental proteomics datasets.

Main Results:

• RolDE demonstrated superior performance compared to existing methods.
• RolDE exhibits high tolerance to missing values and excellent reproducibility.
• The method effectively ranks results in a biologically meaningful manner.

Conclusions:

• RolDE is a robust and effective method for differential expression analysis in longitudinal proteomics.
• The approach is suitable for various data types and can be used by researchers with limited experience.
• RolDE addresses a critical need for analyzing complex, time-series omics data.