Metadata-Version: 2.1
Name: memo-ms
Version: 0.1.2
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|
        
        MEMO
        ===============
        
        **M**\ s2 bas\ **E**\ d sa\ **M**\ ple vect\ **O**\ rization (**MEMO**)
        package
        
        Description
        -----------------
        
        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 qToF vs Q-Exactive).
        
        MEMO is mainly built on `matchms`_ and `spec2vec`_ packages for handling
        the MS2 spectra and convert them into documents. Huge thanks to them for
        the amazing work done with these packages!
        
        Examples
        ------------------
        
        Different examples of application and comparison to other MS/MS based metrics are avalable `here`_ and notebooks are available on `GitHub`_
        
        Publication
        -----------
        
        To add
        
        To install it:
        -------------------------
        
        First make sure to have `anaconda`_ installed.
        
        A) 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 insstalling scikit-bio from conda-forge before
        installing the package with pip:
        
        .. code-block:: console
        
           conda install -c conda-forge scikit-bio
           pip install memo-ms
        
        You can clone the repository to get the demo spectra and quant table
        files!
        
        B) Clone and install locally
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
        
        B.1. First clone the repository using git clone in command line:
        
        .. code-block:: console
        
           git clone <ssh_key or https>
        
        B.2. Create a new conda environment to avoid clashes:
        
        .. code-block:: console
        
           conda create --name memo python
           conda activate memo
        
        B.3. Install the package locally using pip
        
        .. code-block:: console
        
           pip install .
           
        C) Test it using the Tutorial notebook
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
        
        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
        
        .. _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
        .. _anaconda: https://www.anaconda.com/products/individual
        
        .. |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/
          
        
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
