msmu 0.4.5

Python toolkit for modular and traceable LC-MS/MS proteomics analysis based on MuData

Overview

msmu is an open-source Python package for modular and traceable post-DB search preprocessing and statistical analysis of bottom-up proteomics data.

It supports modules for every step of end-to-end processing—from search output parsing through hierarchical summarization, normalization, batch correction, statistical analysis, and visualization—implemented with commonly used analytical and statistical methods.

Central to msmu is the highly versatile and standardized MuData (and AnnData) as a unifying, provenance-aware data container for organizing and storing annotations and representations of multi-dimensional MS data and processing history.

This unique marriage between flexible processing pipeline and MuData empowers FAIR principle-aligned downstream analysis for biomarker discovery and systems biology.

Key features

• Flexible data ingestion from Sage, DIA-NN, and other popular DB search tools
• MuData/AnnData-compatible object structure for organizing multi-level MS data
• SDRF-native sample metadata: attach a validated SDRF as the immutable source of truth and project it onto samples
• Command provenance: supported processing calls record arguments, execution times, environment versions, lineage, and optional content hashes in the container; console output and dimension summaries are not stored
• Protein inference: infer protein groups from peptide evidence using parsimony rule
• Hierarchical summarization: PSM/precursor → peptide → protein/PTM rollup (median, mean, sum, median polish, and DirectLFQ)
• Normalization: median centering, quantile normalization, etc.
• Batch correction for discrete and continuous variations
• Built-in QC: identification count, peptide length, charge, missed cleavage, intensity distribution, etc.
• Statistical analysis: differential expression (limma moderated-t by default, or a label-permutation test), dimensionality reduction
• PTM data support and stoichiometry adjustment with matched global dataset (if available)
• Visualization: PCA, UMAP, volcano plots, heatmaps, and QC metrics

Supported DB search tools

• Sage: https://sage-docs.vercel.app
• DIA-NN: https://github.com/vdemichev/DIA-NN
• MaxQuant: https://www.maxquant.org/
• FragPipe: https://fragpipe.nesvilab.org/
• and more upcoming.

Documentation

Comprehensive documentation, including installation instructions, tutorials, and API references, is available at: https://bertis-informatics.github.io/msmu/

Repository documentation describes the checked-out revision. The published site is built from release tags; development APIs may require installing from dev.

License

BSD 3-Clause License. See LICENSE for details.

Citation

If you use msmu in your research, please cite the following publication (preprint):

Choi, H.-W., Lee, B., Kang, U.-B., and Huh, S. msmu: a Python toolkit for modular and traceable LC-MS proteomics data analysis based on MuData. bioRxiv 2026.01.07.698308 (2026). https://doi.org/10.64898/2026.01.07.698308.