matchms 0.14.0

Python library for fuzzy comparison of mass spectrum data and other Python objects

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

Matchms is an open-source Python package to import, process, clean, and compare mass spectrometry data (MS/MS). It allows to implement and run an easy-to-follow, easy-to-reproduce workflow from raw mass spectra to pre- and post-processed spectral data. Spectral data can be imported from common formats such mzML, mzXML, msp, metabolomics-USI, MGF, or json (e.g. GNPS-syle json files). Matchms then provides filters for metadata cleaning and checking, as well as for basic peak filtering. Finally, matchms was build to import and apply different similarity measures to compare large amounts of spectra. This includes common Cosine scores, but can also easily be extended by custom measures. Example for spectrum similarity measures that were designed to work in matchms are `Spec2Vec <https://github.com/iomega/spec2vec>`_ and `MS2DeepScore <https://github.com/matchms/ms2deepscore>`_.

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

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

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**********************************
Latest changes (matchms >= 0.11.0)
**********************************

Matchms now allows proper logging. Matchms functions and method report unexpected or
undesired behavior as logging WARNING, and additional information as INFO.
The default logging level is set to WARNING. If you want to output additional
logging messages, you can lower the logging level to INFO using set_matchms_logger_level:

.. code-block:: python

from matchms import set_matchms_logger_level

set_matchms_logger_level("INFO")

If you want to suppress logging warnings, you can also raise the logging level
to ERROR by:

.. code-block:: python

set_matchms_logger_level("ERROR")

To write logging entries to a local file, you can do the following:

.. code-block:: python

from matchms.logging_functions import add_logging_to_file

add_logging_to_file("sample.log", loglevel="INFO")

If you want to write the logging messages to a local file while silencing the
stream of such messages, you can do the following:

.. code-block:: python

from matchms.logging_functions import add_logging_to_file

add_logging_to_file("sample.log", loglevel="INFO",
remove_stream_handlers=True)


**********************************
Latest changes (matchms >= 0.10.0)
**********************************

- 2 new filters in ``matchms.filtering``: ``add_retention_time()`` and ``add_retention_index()``, to consistently add retention time/index to the spectrum metadata
- Hashes! ``Spectrum``-objects now allow to compute different hashes:

.. code-block:: python

from matchms.importing import load_from_mgf

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

# Spectrum hashes are generated based on MS/MS peak m/z and intensities
# Those will change if any processing step affects the peaks.
spectrum_hashes = [s.spectrum_hash() for s in spectrums]
# Metadata hashes are generated based on the spectrum metadata
# Those will change if any processing step affects the metadata.
metadata_hashes = [s.metadata_hash() for s in spectrums]
# `hash(spectrum)` will return a hash that is a combination of spectrum and metadata hash
# Those will hence change if any processing step affects the peaks and/or the metadata.
hashes = [hash(s) for s in spectrums]

***********************
Documentation for users
***********************
For more extensive documentation `see our readthedocs <https://matchms.readthedocs.io/en/latest/>`_ and our `matchms introduction tutorial <https://blog.esciencecenter.nl/build-your-own-mass-spectrometry-analysis-pipeline-in-python-using-matchms-part-i-d96c718c68ee>`_.

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

Prerequisites:

- Python 3.7, 3.8 or 3.9
- Anaconda (recommended)

We recommend installing matchms from Anaconda Cloud with

.. code-block:: console

# install matchms in a new virtual environment to avoid dependency clashes
conda create --name matchms python=3.8
conda activate matchms
conda install --channel bioconda --channel conda-forge matchms

Alternatively, matchms can also be installed using ``pip`` but users will then either have to install ``rdkit`` on their own or won't be able to use the entire functionality. Without ``rdkit`` installed several filter functions related to processing and cleaning chemical metadata will not run.
To install matchms with ``pip`` simply run

.. code-block:: console

pip install matchms

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

Matchms functionalities can be complemented by additional packages.
To date we are aware of:

+ `Spec2Vec <https://github.com/iomega/spec2vec>`_ an alternative machine-learning spectral similarity score that can simply 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 <https://github.com/matchms/ms2deepscore>`_ a supervised, deep-learning based spectral similarity score that can simply 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 <https://github.com/matchms/matchmsextras>`_ which contains additional functions to create networks based on spectral similarities, to run spectrum searchers against `PubChem`, or additional plotting methods.

+ `memo <https://github.com/mandelbrot-project/memo>`_ a method allowing a Retention Time (RT) agnostic alignment of metabolomics samples using the fragmentation spectra (MS2) of their consituents.

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

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

To get started with matchms, we recommend following our `matchms introduction tutorial <https://blog.esciencecenter.nl/build-your-own-mass-spectrometry-analysis-pipeline-in-python-using-matchms-part-i-d96c718c68ee>`_.

Alternatively, here below is a small example of using matchms to calculate the Cosine score between mass Spectrums in the `tests/pesticides.mgf <https://github.com/matchms/matchms/blob/master/tests/pesticides.mgf>`_ file.

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

# Apply filters to clean and enhance each spectrum
spectrums = []
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)
spectrums.append(spectrum)

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

# Print the calculated scores for each spectrum pair
for score in scores:
(reference, query, score) = score
# Ignore scores between same spectrum and
# pairs which have less than 20 peaks in common
if reference is not query and score["matches"] >= 20:
print(f"Reference scan id: {reference.metadata['scans']}")
print(f"Query scan id: {query.metadata['scans']}")
print(f"Score: {score['score']:.4f}")
print(f"Number of matching peaks: {score['matches']}")
print("----------------------------")

Glossary of terms
=================

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

* - Term
- Description
* - adduct / addition product
- During ionization in a mass spectrometer, the molecules of the injected compound break apart
into fragments. When fragments combine into a new compound, this is known as an addition
product, or adduct. `Wikipedia <https://en.wikipedia.org/wiki/Adduct>`__
* - GNPS
- Knowledge base for sharing of mass spectrometry data (`link <https://gnps.ucsd.edu/ProteoSAFe/static/gnps-splash.jsp>`__).
* - InChI / :code:`INCHI`
- InChI is short for International Chemical Identifier. InChIs are useful
in retrieving information associated with a certain molecule from a
database.
* - InChIKey / InChI key / :code:`INCHIKEY`
- An identifier for molecules. For example, the InChI key for carbon
dioxide is :code:`InChIKey=CURLTUGMZLYLDI-UHFFFAOYSA-N` (yes, it
includes the substring :code:`InChIKey=`).
* - MGF File / Mascot Generic Format
- A plan ASCII file format to store peak list data from a mass spectrometry experiment. Links: `matrixscience.com <http://www.matrixscience.com/help/data_file_help.html#GEN>`__,
`fiehnlab.ucdavis.edu <https://fiehnlab.ucdavis.edu/projects/lipidblast/mgf-files>`__.
* - parent mass / :code:`parent_mass`
- Actual mass (in Dalton) of the original compound prior to fragmentation.
It can be recalculated from the precursor m/z by taking
into account the charge state and proton/electron masses.
* - precursor m/z / :code:`precursor_mz`
- Mass-to-charge ratio of the compound targeted for fragmentation.
* - SMILES
- A line notation for describing the structure of chemical species using
short ASCII strings. For example, water is encoded as :code:`O[H]O`,
carbon dioxide is encoded as :code:`O=C=O`, etc. SMILES-encoded species may be converted to InChIKey `using a resolver like this one <https://cactus.nci.nih.gov/chemical/structure>`__. The Wikipedia entry for SMILES is `here <https://en.wikipedia.org/wiki/Simplified_molecular-input_line-entry_system>`__.


****************************
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=3.8
conda activate matchms-dev
# Install rdkit using conda, rest of dependencies can be installed with pip
conda install -c conda-forge rdkit
python -m pip install --upgrade pip
pip install --editable .[dev]

Run the linter with:

.. code-block:: console

prospector

Automatically fix incorrectly sorted imports:

.. code-block:: console

isort .

Files will be changed in place and need to be committed manually. If you only want to inspect the isort suggestions then simply run:

.. code-block:: console

isort --check-only --diff .


Run tests (including coverage) with:

.. code-block:: console

pytest


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

The conda packaging is handled by a `recipe at Bioconda <https://github.com/bioconda/bioconda-recipes/blob/master/recipes/matchms/meta.yaml>`_.

Publishing to PyPI will trigger the creation of a `pull request on the bioconda recipes repository <https://github.com/bioconda/bioconda-recipes/pulls?q=is%3Apr+is%3Aopen+matchms>`_
Once the PR is merged the new version of matchms will appear on `https://anaconda.org/bioconda/matchms <https://anaconda.org/bioconda/matchms>`_

Flowchart
=========

.. figure:: paper/flowchart_matchms.png
:width: 400
:alt: Flowchart

Flowchart of matchms workflow. Reference and query spectrums 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.

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

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

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

Copyright (c) 2021, 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
<https://github.com/audreyr/cookiecutter>`_ and the `NLeSC/python-template
<https://github.com/NLeSC/python-template>`_.