matchms - processing and similarity evaluation of mass spectrometry data.

matchms - processing and similarity evaluation of mass spectrometry data. - Published in JOSS (2020)

Keywords

analysis fuzzy-matching fuzzy-search mass-spectrometry metabolomics python3 similarity-measures

Keywords from Contributors

gass-chromatography annotations point-cloud parallel blackhole meshes gravitational-lenses usegalaxy ecology psychology-experiments

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Chemistry Physical Sciences - 69% confidence

Sociology Social Sciences - 64% confidence

Engineering Computer Science - 60% confidence

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Python library for processing (tandem) mass spectrometry data and for computing spectral similarities.

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Host: GitHub
Owner: matchms
License: apache-2.0
Language: Python
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Topics

analysis fuzzy-matching fuzzy-search mass-spectrometry metabolomics python3 similarity-measures

Created over 5 years ago · Last pushed 4 months ago

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.. image:: readthedocs/_static/matchms_header.png
   :target: readthedocs/_static/matchms.png
   :align: left
   :alt: matchms

Matchms is a versatile open-source Python package developed for importing, processing, cleaning, and comparing mass spectrometry data (MS/MS). It facilitates the implementation of straightforward, reproducible workflows, transforming raw data from common mass spectra file formats into pre- and post-processed spectral data, and enabling large-scale spectral similarity comparisons.

The software supports a range of popular spectral data formats, including mzML, mzXML, msp, metabolomics-USI, MGF, and JSON. Matchms offers an array of tools for metadata cleaning and validation, alongside basic peak filtering, to ensure data accuracy and integrity. A key feature of matchms is its ability to apply various pairwise similarity measures for comparing extensive amounts of spectra. This encompasses not only common Cosine-related scores but also molecular fingerprint-based comparisons and other metadata-related assessments.

One of the strengths of matchms is its extensibility, allowing users to integrate custom similarity measures. Notable examples of spectrum similarity measures tailored for Matchms include `Spec2Vec `_ and `MS2DeepScore `_. Additionally, Matchms enhances efficiency by using faster similarity measures for initial pre-selection and supports storing results in sparse data formats, enabling the comparison of several hundred thousands of spectra. This combination of features positions Matchms as a comprehensive tool for mass spectrometry data analysis.

If you use matchms in your research, please cite the following software papers:  

F Huber, S. Verhoeven, C. Meijer, H. Spreeuw, E. M. Villanueva Castilla, C. Geng, J.J.J. van der Hooft, S. Rogers, A. Belloum, F. Diblen, J.H. Spaaks, (2020). matchms - processing and similarity evaluation of mass spectrometry data. Journal of Open Source Software, 5(52), 2411, https://doi.org/10.21105/joss.02411

de Jonge NF, Hecht H, Michael Strobel, Mingxun Wang, van der Hooft JJJ, Huber F. (2024). Reproducible MS/MS library cleaning pipeline in matchms. Journal of Cheminformatics, 2024, https://jcheminf.biomedcentral.com/articles/10.1186/s13321-024-00878-1


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**********************************
Latest changes (matchms >= 0.18.0)
**********************************

Pipeline class
==============

To make typical matchms workflows (data import, processing, score computations) more accessible to users, matchms now offers a `Pipeline` class to handle complex workflows. This also allows to create, import, export, or modify workflows using yaml files. See code examples below (and soon: updated tutorial).

Sparse scores array
===================

We realized that many matchms-based workflows aim to compare many-to-many spectra whereby not all pairs and scores are equally important. Often, for instance, it will be about searching similar or related spectra/compounds. This also means that often not all scores need to be stored (or computed). For this reason, we now shifted to a sparse handling of scores in matchms (that means: only storing actually computed, non-null values).

.. image:: readthedocs/_static/matchms_sketch.png
   :target: readthedocs/_static/matchms_sketch.png
   :align: left
   :alt: matchms code design


***********************
Documentation for users
***********************
For more extensive documentation `see our readthedocs `_, our `matchms introduction tutorial `_ or the `user documentation `_.

Installation
============

Prerequisites:  

- Python 3.10 - 3.13, (higher versions should work as well, but are not yet tested systematically)
- Anaconda (recommended)

We recommend installing matchms in a new virtual environment to avoid dependency clashes

.. code-block:: console

  conda create --name matchms python=3.12
  conda activate matchms
  conda install --channel bioconda --channel conda-forge matchms

matchms ecosystem -> additional functionalities
===============================================

Additional packages can complement Matchms functionalities.  
To date, we are aware of:

+ `Spec2Vec `_ an alternative machine-learning spectral similarity score that can be installed by `pip install spec2vec` and be imported as `from spec2vec import Spec2Vec` following the same API as the scores in `matchms.similarity`.

+ `MS2DeepScore `_ a supervised, deep-learning based spectral similarity score that can be installed by `pip install ms2deepscore` and be imported as `from ms2deepscore import MS2DeepScore` following the same API as the scores in `matchms.similarity`.

+ `matchmsextras `_ contains additional functions to create networks based on spectral similarities, run spectrum searchers against `PubChem`, or additional plotting methods.

+ `MS2Query `_ Reliable and fast MS/MS spectral-based analogue search, running on top of matchms.

+ `memo `_ a method allowing a Retention Time (RT) agnostic alignment of metabolomics samples using the fragmentation spectra (MS2) of their constituents.

+ `RIAssigner `_ a tool for retention index calculation for gas chromatography - mass spectrometry (GC-MS) data.

+ `MSMetaEnhancer `_ is a Python package to collect mass spectral library metadata using various web services and computational chemistry packages.

+ `SimMS `_ is a python package with fast GPU-based implementations of common similarity classes such as `CudaCosineGreedy`, and `CudaModifiedCosine`.

*(if you know of any other packages that are fully compatible with matchms, let us know!)*

Ecosystem compatibility
-----------------------

.. compatibility matrix start

.. list-table::
   :header-rows: 1

   * - NumPy Version
     - spec2vec Status
     - ms2deepscore Status
     - ms2query Status
   * - .. image:: https://img.shields.io/badge/numpy-1.25-lightgrey?logo=numpy :alt: numpy
     - .. image:: https://img.shields.io/badge/spec2vec-0.8.0-red
     - .. image:: https://img.shields.io/badge/ms2deepscore-2.5.4-green
     - .. image:: https://img.shields.io/badge/ms2query-1.5.4-red
   * - .. image:: https://img.shields.io/badge/numpy-2.1-lightgrey?logo=numpy :alt: numpy
     - .. image:: https://img.shields.io/badge/spec2vec-0.8.0-red
     - .. image:: https://img.shields.io/badge/ms2deepscore-2.5.4-green
     - .. image:: https://img.shields.io/badge/ms2query-1.5.4-red

.. compatibility matrix end

Introduction
============

To get started with matchms, we recommend following our `matchms introduction tutorial `_.

Below is an example of using default filter steps for cleaning spectra, 
followed by calculating the Cosine score between mass Spectra in the `tests/testdata/pesticides.mgf `_ file.

.. code-block:: python

    from matchms.Pipeline import Pipeline, create_workflow

    workflow = create_workflow(
        yaml_file_name="my_config_file.yaml", # The workflow will be stored in a yaml file, this can be used to rerun your workflow or to share it with others.
        score_computations=[["cosinegreedy", {"tolerance": 1.0}]],
        )
    pipeline = Pipeline(workflow)
    pipeline.logging_file = "my_pipeline.log"  # for pipeline and logging message
    pipeline.run("tests/testdata/pesticides.mgf")
    
Below is a more advanced code example showing how you can make a specific pipeline for your needs.

.. code-block:: python

    import os
    from matchms.Pipeline import Pipeline, create_workflow
    from matchms.filtering.default_pipelines import DEFAULT_FILTERS, LIBRARY_CLEANING
    
    results_folder = "./results"
    os.makedirs(results_folder, exist_ok=True)
    
    workflow = create_workflow(
        yaml_file_name=os.path.join(results_folder, "my_config_file.yaml"),  # The workflow will be stored in a yaml file.
        query_filters=DEFAULT_FILTERS,
        reference_filters=LIBRARY_CLEANING + ["add_fingerprint"],
        score_computations=[["precursormzmatch", {"tolerance": 100.0}],
                            ["cosinegreedy", {"tolerance": 1.0}],
                            ["filter_by_range", {"name": "CosineGreedy_score", "low": 0.2}]],
    )
    pipeline = Pipeline(workflow)
    pipeline.logging_file = os.path.join(results_folder, "my_pipeline.log")  # for pipeline and logging message
    pipeline.logging_level = "WARNING"  # To define the verbosety of the logging
    pipeline.run("tests/testdata/pesticides.mgf", "my_reference_library.mgf",
                 cleaned_query_file=os.path.join(results_folder, "cleaned_query_spectra.mgf"),
                 cleaned_reference_file=os.path.join(results_folder,
                                                     "cleaned_library_spectra.mgf"))  # choose your own files


Alternatively, in particular, if you need more room to add custom functions and steps, the individual steps can run without using the matchms ``Pipeline``:

.. code-block:: python
    
    from matchms.importing import load_from_mgf
    from matchms.filtering import default_filters, normalize_intensities
    from matchms import calculate_scores
    from matchms.similarity import CosineGreedy

    # Read spectra from a MGF formatted file, for other formats see https://matchms.readthedocs.io/en/latest/api/matchms.importing.html 
    file = load_from_mgf("tests/testdata/pesticides.mgf")

    # Apply filters to clean and enhance each spectrum
    spectra = []
    for spectrum in file:
        # Apply default filter to standardize ion mode, correct charge and more.
        # Default filter is fully explained at https://matchms.readthedocs.io/en/latest/api/matchms.filtering.html .
        spectrum = default_filters(spectrum)
        # Scale peak intensities to maximum of 1
        spectrum = normalize_intensities(spectrum)
        spectra.append(spectrum)

    # Calculate Cosine similarity scores between all spectra
    # For other similarity score methods see https://matchms.readthedocs.io/en/latest/api/matchms.similarity.html .
    scores = calculate_scores(references=spectra,
                              queries=spectra,
                              similarity_function=CosineGreedy())

    # Matchms allows to get the best matches for any query using scores_by_query
    query = spectra[15]  # just an example
    best_matches = scores.scores_by_query(query, 'CosineGreedy_score', sort=True)

    # Print the calculated scores for each spectrum pair
    for (reference, score) in best_matches[:10]:
        # Ignore scores between same spectra
        if reference is not query:
            print(f"Reference scan id: {reference.metadata['scans']}")
            print(f"Query scan id: {query.metadata['scans']}")
            print(f"Score: {score[0]:.4f}")
            print(f"Number of matching peaks: {score[1]}")
            print("----------------------------")


Different spectrum similarity scores
====================================

Matchms comes with numerous different scoring methods in `matchms.similarity` but can also be supplemented by scores from external packages such as `Spec2Vec` or `MS2DeepScore`.

Code example: 

.. code-block:: python

    from matchms.importing import load_from_usi
    import matchms.filtering as msfilters
    import matchms.similarity as mssim


    usi1 = "mzspec:GNPS:GNPS-LIBRARY:accession:CCMSLIB00000424840"
    usi2 = "mzspec:MSV000086109:BD5_dil2x_BD5_01_57213:scan:760"

    mz_tolerance = 0.1

    spectrum1 = load_from_usi(usi1)
    spectrum1 = msfilters.select_by_mz(spectrum1, 0, spectrum1.get("precursor_mz"))
    spectrum1 = msfilters.remove_peaks_around_precursor_mz(spectrum1,
                                                           mz_tolerance=0.1)

    spectrum2 = load_from_usi(usi2)
    spectrum2 = msfilters.select_by_mz(spectrum2, 0, spectrum1.get("precursor_mz"))
    spectrum2 = msfilters.remove_peaks_around_precursor_mz(spectrum2,
                                                           mz_tolerance=0.1)
    # Compute scores:
    similarity_cosine = mssim.CosineGreedy(tolerance=mz_tolerance).pair(spectrum1, spectrum2)
    similarity_modified_cosine = mssim.ModifiedCosine(tolerance=mz_tolerance).pair(spectrum1, spectrum2)
    similarity_neutral_losses = mssim.NeutralLossesCosine(tolerance=mz_tolerance).pair(spectrum1, spectrum2)

    print(f"similarity_cosine: {similarity_cosine}")
    print(f"similarity_modified_cosine: {similarity_modified_cosine}")
    print(f"similarity_neutral_losses: {similarity_neutral_losses}")

    spectrum1.plot_against(spectrum2)


****************************
Documentation for developers
****************************

Installation
============

To install matchms, do:

.. code-block:: console

  git clone https://github.com/matchms/matchms.git
  cd matchms
  conda create --name matchms-dev python=3.12
  conda activate matchms-dev

  # If you use poetry
  python -m pip install --upgrade pip poetry
  poetry install --with dev

  # If you use pip
  pip install -r dev-requirements.txt
  pip install --editable .

Run the linter and formatter and automatically fix issues with:

.. code-block:: console

  ruff check --fix matchms/YOUR-MODIFIED-FILE.py
  ruff format matchms/YOUR-MODIFIED-FILE.py

You can automate the previous steps by using a pre-commit hook. This will automatically run the linter and formatter on
the modified files before a commit. If the linter or formatter fixes any issues, you will need to recommit your code.

.. code-block:: console

  pre-commit install


Run tests (including coverage) with:

.. code-block:: console

  pytest


Conda package
=============

The conda packaging is handled by a `recipe at Bioconda `_.

Publishing to PyPI will trigger the creation of a `pull request on the bioconda recipes repository `_
Once the PR is merged the new version of matchms will appear on `https://anaconda.org/bioconda/matchms `_

Flowchart
=========

.. figure:: paper/flowchart_matchms.png
  :width: 400
  :alt: Flowchart
  
  Flowchart of matchms workflow. Reference and query spectra are filtered using the same
  set of set filters (here: filter A and filter B). Once filtered, every reference spectrum is compared to
  every query spectrum using the matchms.Scores object.

Support
============

To get support join the public `Slack channel `_.

Contributing
============

If you want to contribute to the development of matchms,
have a look at the `contribution guidelines `_.

*******
License
*******

Copyright (c) 2024, Düsseldorf University of Applied Sciences & Netherlands eScience Center

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.

*******
Credits
*******

This package was created with `Cookiecutter
`_ and the `NLeSC/python-template
`_.

Owner

Name: matchms
Login: matchms
Kind: organization

Repositories: 4
Profile: https://github.com/matchms

JOSS Publication

matchms - processing and similarity evaluation of mass spectrometry data.

Published

August 31, 2020

DOI

10.21105/joss.02411

Volume 5, Issue 52, Page 2411