MSqRob

MSqRob performs quantitative protein-level statistical inference on label-free liquid chromatography-mass spectrometry (LC-MS) proteomics data to detect differential protein abundance.

Key Features:

• Peptide-Level Modeling: Uses peptide intensities from mass spectra for relative protein quantitation, retaining more peptide-level information than summarization-based methods.
• Ridge Regression: Applies L2 regularization to peptide-based linear models to mitigate multicollinearity and reduce overfitting.
• Empirical Bayes Variance Estimation: Borrows information across proteins to stabilize variance estimates and improve statistical inference.
• M-Estimation with Huber Weights: Provides robustness to outlying peptide intensities and unbalanced datasets caused by missing values.
• Improved Sensitivity and Specificity: Combines peptide-level modeling and advanced regression methods to yield more precise fold-change estimates and enhanced detection of true biological changes compared to summarization-based and traditional peptide-based regression methods.
• Ionization Competition Consideration: Accounts for ionization competition effects that can affect quantitation even at low spike-in concentrations.
• Data Aggregation Options: Supports analysis of peptide intensity values aggregated by charge state and modification status (e.g., MaxQuant peptides.txt) and of raw peptide intensities (e.g., MaxQuant evidence.txt), with aggregation shown to be slightly superior in some cases.
• MaxQuant Integration: Operates on MaxQuant search results as input files for downstream statistical inference.

Scientific Applications:

• Comparative Proteomics: Detects differential protein abundance in comparisons such as wild-type versus ArgP knock-out Francisella tularensis.
• Spike-In Studies: Enables precise quantitation in spike-in experiments, including studies by consortia such as CPTAC that require sensitivity at low concentrations.

Methodology:

Peptide-level linear regression models are fitted to peptide intensities (optionally aggregated by charge state and modification) using ridge regression, empirical Bayes variance estimation, and M-estimation with Huber weights on MaxQuant-derived data (peptides.txt or evidence.txt).