DEqMS

DEqMS models protein-level variance as a function of peptide spectrum matches (PSMs) or peptide counts to improve inference of differential protein expression from mass spectrometry-based quantitative proteomics.

Key Features:

• Variance modeling: Explicitly models variance dependence on the number of peptide spectrum matches (PSMs) or peptides used for protein quantification.
• Single-peptide support: Incorporates single-peptide identifications into variance estimation and downstream differential expression analysis.
• Compatibility with quantification types: Demonstrated performance with both label-free and TMT-labeled quantitative proteomics data.
• Improved differential detection: Provides data-dependent protein variance estimates that improve detection accuracy of differential protein expression while controlling false discovery rates.
• Comparison to conventional methods: Addresses limitations of standard t-tests, linear models, and mixed-effect models for mass spectrometry proteomics data.
• Evaluated datasets: Performance validated on multiple datasets including E. coli proteome spike-in data.

Scientific Applications:

• Differential protein expression analysis: Infers differential expression in mass spectrometry-based quantitative proteomics experiments.
• Biomarker research: Supports identification of protein biomarkers by improving statistical inference from proteomics data.
• Phenotype-level cellular event analysis: Facilitates basic biological investigations that rely on proteome-level changes linked to cellular phenotypes.
• Label-free and TMT studies: Applicable to both label-free and tandem mass tag (TMT)-labeled experimental designs.

Methodology:

Models variance as a function of PSM or peptide counts, provides data-dependent protein variance estimation, and incorporates single-peptide identifications into the variance and differential-expression calculations.