Differential protein expression analysis of quantitative mass spectrometry data using DEqMS

Area of Science:

• Proteomics
• Bioinformatics
• Statistical analysis

Background:

• Quantitative mass spectrometry is crucial for protein expression analysis.
• Accurate variance estimation is essential for reliable differential expression findings.
• Existing tools may not fully account for variations in feature counts across different mass spectrometry acquisition methods.

Purpose of the Study:

• To introduce an updated R package, DEqMS, for differential protein expression analysis.
• To extend the applicability of DEqMS to data-independent acquisition (DIA) workflows.
• To provide researchers with a robust tool for identifying altered protein abundance in quantitative proteomics.

Main Methods:

• Utilizes a robust Bayesian method for variance estimation.
• Incorporates mass spectrometry feature count (e.g., peptide precursors, PSMs) into the analysis.
• Applies the method to both data-dependent acquisition (DDA) and data-independent acquisition (DIA) proteomics data.

Main Results:

• DEqMS provides accurate protein quantification by accounting for feature counts.
• The package successfully extends to DIA workflows, validated with spike-in and real-world datasets.
• Outputs include fold changes, t-values, and P values adjusted for feature count.

Conclusions:

• The updated DEqMS R package enhances differential protein expression analysis in quantitative proteomics.
• It offers improved accuracy by considering feature counts and supports both DDA and DIA data.
• Enables researchers with basic R knowledge to identify significant protein abundance changes across various datasets.