Metadata-Version: 2.1
Name: tidyms
Version: 0.2.1
Summary: Tools for working with MS data in metabolomics
Home-page: https://github.com/griquelme/tidyms
Author: Bioanalytical Mass Spectrometry Group at CIBION-CONICET
Author-email: griquelme.chm@gmail.com
Maintainer: Gabriel Riquelme
Maintainer-email: griquelme.chm@gmail.com
License: BSD (3-clause)
Platform: UNKNOWN
Classifier: License :: OSI Approved :: BSD License
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: Topic :: Scientific/Engineering :: Chemistry
Classifier: Topic :: Scientific/Engineering :: Medical Science Apps.
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: pandas
Requires-Dist: pyopenms
Requires-Dist: numpy
Requires-Dist: pyyaml
Requires-Dist: statsmodels
Requires-Dist: scipy
Requires-Dist: scikit-learn
Requires-Dist: bokeh
Requires-Dist: xlrd
Requires-Dist: cerberus
Requires-Dist: seaborn
Requires-Dist: ipython
Requires-Dist: pytest
Requires-Dist: openpyxl
Requires-Dist: requests

TidyMS: Tools for working with MS data in metabolomics
======================================================

TidyMS is a python library for processing Mass Spectrometry data. It aims to
provide easy to use tools to read, process and visualize MS data generated in
metabolomic studies.

Features
--------

TidyMS provides functionality to:

1. Read raw MS data in the mzML format
2. Spectrum and chromatogram creation.
3. Powerful and flexible peak picking functions optimized for chromatographic
   and spectral data.
4. Feature detection and feature correspondence in LC-MS data.
5. Reading processed data in a variety of formats (XCMS, MZMine2, ...)
5. Data matrix curation using widely accepted guidelines from the metabolomics
   community.
6. Interactive visualizations of raw and processed data using Bokeh, or
   publication quality plots using seaborn.

Installation
------------

The latest release can be installed from PyPI:

```
    pip install tidyms
```

Examples
--------

Jupyter notebooks with examples are available
[here](https://github.com/griquelme/tidyms-notebooks).

Documentation
-------------

The official documentation is available at 
[readthedocs](https://tidyms.readthedocs.io/en/latest/).


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.



