TidyMS2

Tools to process mass spectrometry data.

TidyMS2 is an upgraded version from TidyMS, built from scratch and designed to work with Python latest practices.

TidyMS2 offers a highly customizable framework for processing mass spectrometry datasets:

For processing MS datasets:

• tools for processing LC-MS datasets: build pipelines to define how to process your data and TidyMS manages the rest.
• optimized for scalability: efficiently processes datasets with thousands of samples while maintaining a minimal memory footprint.
• test your pipelines with dataset simulation utilities
• data persistence for intermediate data and results
• result visualization

For creating new processing algorithms

• a highly extensive data model for expressing features
• a data flow model that validates and manages data through your data pipeline.

Installation

[!WARNING] TidyMS2 is currently in early development, and the API may be subject to breaking changes until the 1.0 release. Please be aware that future updates may alter functionality or behavior. We recommend keeping an eye on the release notes and updating your usage accordingly. Once the library reaches version 1.0, the API will be considered stable, and breaking changes will adapt to the SemVer policy.

TidyMS is installed using pip:

pip install tidyms2

Documentation

The library documentation is available here.

Getting help

The library documentation contains tutorials on a variety on topics. If you weren't able to find an answer to your problem, you can use the project discussion board

Development

If you encounter a problem or bug, you can report it using the issue tracker.

Before submitting a new issue, please search the issue tracker to see if the problem has already been reported.

If your question is about how to achieve a specific task or use the library in a certain way, we recommend posting it in the discussions section.

When reporting an issue, it's helpful to include the following details:

• A code snippet that reproduces the problem.
• If an error occurs, please include the full traceback.
• A brief explanation of why the current behavior is incorrect or unexpected.

For guidelines on how to write an issue report, refer to this post.

Contributing

Checkout the developer and contributing guides in the library documentation

Citation

If you find TidyMS useful, we would appreciate citations:

Riquelme, G.; Zabalegui, N.; Marchi, P.; Jones, C.M.; Monge, M.E. A Python-Based Pipeline for Preprocessing LC–MS Data for Untargeted Metabolomics Workflows. Metabolites 2020, 10, 416, doi:10.3390/metabo10100416.