PSEA-Quant: a protein set enrichment analysis on label-free and label-based protein quantification data

Area of Science:

• Proteomics
• Computational Biology
• Bioinformatics

Background:

• Large-scale proteomics quantification often results in overwhelming, difficult-to-interpret protein lists.
• Reproducibility and interpretation challenges necessitate advanced computational analysis for quantitative proteomics data.

Purpose of the Study:

• To introduce PSEA-Quant, a novel statistical algorithm for analyzing quantitative proteomics data.
• To computationally identify protein sets significantly enriched with abundant and reproducibly quantified proteins.

Main Methods:

• Developed PSEA-Quant, a protein set enrichment analysis algorithm for label-free and label-based quantification.
• PSEA-Quant models protein annotation biases and analyzes single-condition samples.
• Utilized PSEA-Quant on cystic fibrosis cell line data and multi-species brain cortex samples.

Main Results:

• PSEA-Quant provides results complementary to traditional Gene Ontology (GO) analyses.
• Identified biological processes in cystic fibrosis using label-free quantification.
• Highlighted mechanistic differences in human, rat, and mouse brain cortices via tandem mass tag quantification.

Conclusions:

• PSEA-Quant enhances the analysis of proteomics quantification data by delivering significant biological insights.
• The algorithm offers a valuable alternative to existing methods like GSEA and PSEA.
• PSEA-Quant improves the interpretation of complex proteomics datasets for biological discovery.