Metadata-Version: 2.1
Name: memo-ms
Version: 0.1.4
Summary: Python package to perform MS2 Based Sample Vectorization and visualization
Home-page: https://github.com/mandelbrot-project/memo
Author: Arnaud Gaudry
Author-email: arnaud.gaudry@unige.ch
License: UNKNOWN
Description: |GitHub Workflow Status| |GitHub| |PyPI| |Docs|
        
        MEMO
        ===============
        .. image:: https://github.com/mandelbrot-project/memo_publication_examples/blob/main/docs/memo_logo.jpg
           :width: 200 px
           :align: right
        
        Description
        -----------------
        
        **M**\ s2 bas\ **E**\ d sa\ **M**\ ple vect\ **O**\ rization (**MEMO**)
        is a method allowing a Retention Time (RT) agnostic alignment of
        metabolomics samples using the fragmentation spectra (MS2) of their
        consituents. The occurence of MS2 peaks and neutral losses (to the precursor) in each sample is counted
        and used to generate an *MS2 fingerprint* of the sample. These
        fingerprints can in a second stage be aligned to compare different
        samples. Once obtained, different filtering (remove peaks/losses from
        blanks for example) and visualization techniques (MDS/PCoA, TMAP,
        Heatmap, ...) can be used. MEMO suits particularly well to compare chemodiverse samples, ie with a
        poor features overlap, or to compare samples with a strong RT shift,
        acquired using different LC methods or even different mass spectrometers
        technology (Maxiis Q-ToF vs Q-Exactive).
        
        Documentation
        ------------------
        For documentation, see our `readthedocs`_. Different examples of application and comparison to other MS/MS based metrics are available `here`_ and the corresponding notebooks are available on `GitHub`_.
        
        Publication
        -----------
        
        If you use MEMO, please cite the following papers:
           - MEMO preprint - MEMO: Mass Spectrometry-based Sample Vectorization to Explore Chemodiverse Datasets Arnaud Gaudry, Florian Huber, Louis-Felix Nothias, Sylvian Cretton, Marcel Kaiser, Jean-Luc Wolfender, Pierre-Marie Allard bioRxiv 2021.12.24.474089; doi: https://doi.org/10.1101/2021.12.24.474089
           - Huber, Florian, Stefan Verhoeven, Christiaan Meijer, Hanno Spreeuw, Efraín Castilla, Cunliang Geng, Justin van der Hooft, et al. 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 
           - Huber, Florian, Lars Ridder, Stefan Verhoeven, Jurriaan H. Spaaks, Faruk Diblen, Simon Rogers, and Justin J. J. van der Hooft. 2021. “Spec2Vec: Improved Mass Spectral Similarity Scoring through Learning of Structural Relationships.” PLoS Computational Biology 17 (2): e1008724. https://doi.org/10.1371/journal.pcbi.1008724
        
        Installation :
        -------------------------
        
        First make sure to have `anaconda`_ installed.
        
        A) Recommended: using pip install
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
        
        A.1. Create a new conda environment to avoid clashes:
        
        .. code-block:: console
        
           conda create --name memo python=3.8
           conda activate memo
        
        A.2. Install with pip:
        
        .. code-block:: console
        
           pip install numpy
           pip install memo-ms
        
        If you have an error, try installing scikit-bio from conda-forge (available for Mac and Linux users) or pip (for Windows users) before
        installing the package with pip. For Windows users, you will need to install C++ build tools (download here: https://visualstudio.microsoft.com/visual-cpp-build-tools/, see this answer for help https://stackoverflow.com/a/50210015):
        
        .. code-block:: console
        
           conda install -c conda-forge scikit-bio
           # or for Windows user
           pip install scikit-bio
           pip install memo-ms
        
        You can clone the repository to get the demo spectra and quant table
        files and test the package using the Tutorial notebook!
        
        NB: If you have this error when loading the memo package:
        
        .. code-block:: console
        
           ValueError: numpy.ndarray size changed, may indicate binary incompatibility. Expected 96 from C header, got 88 from PyObject
        
        Uninstall and reinstall scikit-bio with no dependencies using this command:
        
        .. code-block:: console
        
           pip uninstall scikit-bio
           pip install scikit-bio --no-cache-dir --no-binary :all:
        
        
        B) Alternatively: clone and install locally
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
        
        B.1. First clone the repository using git clone in command line:
        
        .. code-block:: console
        
           git clone https://github.com/mandelbrot-project/memo.git # or ssh
        
        B.2. Create a new conda environment to avoid clashes:
        
        .. code-block:: console
        
           conda create --name memo python=3.8
           conda activate memo
        
        B.3. Install the package locally using pip
        
        .. code-block:: console
        
           pip install .
           
        Run example notebook
        -----------------------------------
        
        It is located in the `tutorial folder`_
        
        You can also find a list of notebook to reproduce results of the MEMO paper. The repo is over there https://github.com/mandelbrot-project/memo_publication_examples
           
        
        Documentation for developers
        ----------------------------------
        
        Installation
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
        
        Create an environment with
        
        .. code-block:: console
        
           git clone https://github.com/mandelbrot-project/memo.git
           cd memo
           conda create --name memo-dev python=3.8
           conda activate memo-dev
        
        Then install dependencies and memo:
        
        .. code-block:: console
        
           python -m pip install --upgrade pip
           pip install numpy
           pip install --editable .[dev]
           # pip install -e .'[dev]' (on mac)
        
        Run tests
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
        
        Memo tests can be run by:
        
        .. code-block:: console
        
           pytest
        
        And the code linter with
        
        .. code-block:: console
        
           prospector
        
        License
        -----------
        
        MEMO is licensed under the GNU General Public License v3.0. Permissions of this strong copyleft license are conditioned on making available complete source code of licensed works and modifications, which include larger works using a licensed work, under the same license. Copyright and license notices must be preserved. Contributors provide an express grant of patent rights.
        
        .. _Qemistree Evaluation Dataset: https://www.nature.com/articles/s41589-020-00677-3
        .. _matchms: https://github.com/matchms/matchms
        .. _spec2vec: https://github.com/iomega/spec2vec
        .. _here: https://mandelbrot-project.github.io/memo_publication_examples/
        .. _GitHub: https://github.com/mandelbrot-project/memo_publication_examples
        .. _readthedocs: https://memo-docs.readthedocs.io/en/latest/index.html#
        .. _anaconda: https://www.anaconda.com/products/individual
        .. _`tutorial folder`: https://github.com/mandelbrot-project/memo/blob/b14409a545aa499992b92c3eb9445405ceba9a78/tutorial/tutorial_memo.ipynb
        
        
        .. |GitHub Workflow Status| image:: https://img.shields.io/github/workflow/status/mandelbrot-project/memo/CI%20Build
           :target: https://github.com/mandelbrot-project/memo/actions
        .. |GitHub| image:: https://img.shields.io/github/license/mandelbrot-project/memo?color=blue
        .. |PyPI| image:: https://img.shields.io/pypi/v/memo_ms?color=blue)
           :target: https://pypi.org/project/memo-ms/
        .. |Docs| image:: https://readthedocs.org/projects/memo-docs/badge/?version=stable
           :target: https://memo-docs.readthedocs.io/en/stable/?badge=stable
           :alt: Documentation Status
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.7
Description-Content-Type: text/x-rst
Provides-Extra: dev
