Metadata-Version: 2.4
Name: msmu
Version: 0.2.8
Summary: BERTIS LC-MS/MS analysis library through MuData
Author-email: Hyung-wook Choi <hyungwook.choi@bertis.com>, Byeong-chan Lee <byeongchan.lee@bertis.com>
License: BSD 3-Clause License
        
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Project-URL: Repository, https://github.com/bertis-informatics/msmu
Keywords: LC-MS/MS,Proteomics,Protein,Peptide,Mass Spectrometry
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License-File: LICENSE
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<img src="https://raw.githubusercontent.com/bertis-informatics/msmu/refs/heads/main/docs/assets/logo.svg" width="240px" alt="msmu logo"/>

**Python toolkit for modular and traceable LC-MS/<u>MS</u> proteomics analysis based on <u>Mu</u>Data**

## 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.

<img src="https://raw.githubusercontent.com/bertis-informatics/msmu/refs/heads/main/docs/assets/overview.svg" width="100%" alt="MuData logo"/>

## 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
- **Protein inference**: infer protein groups from peptide evidence using parsimony rule
- **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 analysis, dimensionality reduction
- **PTM data support** and stoichiometry adjustment with matched global dataset (if available)
- **Visualization**: PCA, UMAP, volcano plots, heatmaps, QC metrics

## Supporting DB Search Tools

- Sage: [https://sage-docs.vercel.app](https://sage-docs.vercel.app)
- DIA-NN: [https://github.com/vdemichev/DIA-NN](https://github.com/vdemichev/DIA-NN)
- MaxQuant: [https://www.maxquant.org/](https://www.maxquant.org/)
- FragPipe: [https://fragpipe.nesvilab.org/](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/](https://bertis-informatics.github.io/msmu/)

## Citation

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

> msmu: a Python toolkit for modular and traceable LC-MS proteomics data analysis based on MuData
>
> Hyung-Wook Choi, Byeongchan Lee, Un-Beom Kang, Sunghyun Huh
>
> bioRxiv 2026.01.07.698308; doi: [10.64898/2026.01.07.698308](https://doi.org/10.64898/2026.01.07.698308)

## License

BSD 3-Clause License. See LICENSE for details.
