ProteoMM

ProteoMM performs model-based peptide-level differential expression analysis of single or multiple datasets to produce a single fold change and p-value for each protein across multiple datasets.

Key Features:

• Normalization: Implements EigenMS normalization using singular value decomposition for peak intensities in bottom-up MS-based proteomics.
• Missing Value Imputation: Performs model-based peptide-level imputation for proteomics data.
• Differential Expression: Conducts model-based peptide-level differential expression analysis producing a single fold change and p-value per protein across multiple datasets.

Scientific Applications:

• Multi-Dataset Proteomics Integration: Enables combined analysis of multiple bottom-up MS-based proteomics datasets to derive unified protein-level statistics.
• Bottom-Up Proteomics Quantitation: Supports protein quantitation workflows following the statistical framework for bottom-up MS-based proteomics.

Methodology:

Model-based peptide-level imputation and differential expression analysis follows Karpievitch et al. Bioinformatics 2009; EigenMS normalization uses singular value decomposition as described in Karpievitch et al. Bioinformatics 2009.