Uncertainty-Aware Protein-Level Quantification and Differential Expression Analysis of Proteomics Data with seaMass

Area of Science:

• Proteomics
• Bioinformatics
• Computational Biology

Background:

• Mass spectrometry-based proteomics generates complex datasets requiring sophisticated analysis.
• Existing tools may not adequately handle the variability and scale of modern proteomics studies.
• Accurate protein quantification and differential expression analysis are crucial for biological discovery.

Purpose of the Study:

• To introduce seaMass, an R package for comprehensive proteomics data analysis.
• To provide a unified framework for quantification, normalization, and differential expression analysis.
• To enable robust analysis across diverse proteomics experimental paradigms.

Main Methods:

• Utilizes a blocked experimental design concept for broad applicability.
• Employs hierarchical Bayesian modeling for feature and peptide reliability assessment.
• Integrates differential expression analysis with false discovery rate control and uncertainty-aware principal components analysis.

Main Results:

• seaMass effectively assesses quantification reliability and captures unexplained assay variation.
• Provides standard deviation of posterior uncertainty for protein group quantification.
• Scalable to large studies with hundreds of assays, offering quality control metrics.

Conclusions:

• seaMass offers a powerful and flexible tool for end-to-end proteomics data analysis.
• The Bayesian approach enhances the reliability and interpretability of protein quantification.
• The package supports various proteomics workflows, including label-free, SILAC, and isotope labeling experiments.