msqrob2

msqrob2 provides a robust linear mixed model framework for assessing differential abundance in MS-based quantitative proteomics experiments.

Key Features:

• Flexible input: Workflows can start from raw peptide intensities or summarised protein expression values.
• Stabilized parameter estimation: Model parameter estimates are stabilized by ridge regression, empirical Bayes variance estimation, and robust M-estimation.
• Hurdle workflow for missing data: The hurde workflow handles missing data without relying on hard-to-verify imputation assumptions, outperforming state-of-the-art methods with and without imputation for both high and low missingness.
• QFeature integration: Builds on QFeature infrastructure for quantitative mass spectrometry data to store model results together with raw and preprocessed data.

Scientific Applications:

• Differential abundance analysis: Statistical inference for detecting differentially abundant proteins in LC-MS proteomics experiments.
• Missing data handling: Proteomics experiments with high or low levels of missing peptide or protein intensity values.

Methodology:

Robust linear mixed models with ridge regression, empirical Bayes variance estimation, robust M-estimation, and a hurdle workflow for missing data.