Package: memo-ms
MEMODescriptionMs2 basEd saMple vectOrization (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 anMS2 fingerprintof 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).DocumentationFor documentation, see ourreadthedocs. Different examples of application and comparison to other MS/MS based metrics are availablehereand the corresponding notebooks are available onGitHub.PublicationIf 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/10.1101/2021.12.24.474089Huber, 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.02411Huber, 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.1008724Installation :First make sure to haveanacondainstalled.A) Recommended: using pip installA.1. Create a new conda environment to avoid clashes:conda create --name memo python=3.8
conda activate memoA.2. Install with pip:pip install numpy
pip install memo-msIf 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 helphttps://stackoverflow.com/a/50210015):conda install -c conda-forge scikit-bio#orforWindowsuserpip install scikit-bio
pip install memo-msYou 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:ValueError: numpy.ndarray size changed, may indicate binary incompatibility. Expected 96 from C header, got 88 from PyObjectUninstall and reinstall scikit-bio with no dependencies using this command:pip uninstall scikit-bio
pip install scikit-bio --no-cache-dir --no-binary :all:B) Alternatively: clone and install locallyB.1. First clone the repository using git clone in command line:git clone https://github.com/mandelbrot-project/memo.git # or sshB.2. Create a new conda environment to avoid clashes:conda create --name memo python=3.8
conda activate memoB.3. Install the package locally using pippip install .Run example notebookIt is located in thetutorial folderYou can also find a list of notebook to reproduce results of the MEMO paper. The repo is over therehttps://github.com/mandelbrot-project/memo_publication_examplesDocumentation for developersInstallationCreate an environment withgit clone https://github.com/mandelbrot-project/memo.git
cd memo
conda create --name memo-dev python=3.8
conda activate memo-devThen install dependencies and memo:python -m pip install --upgrade pip
pip install numpy
pip install --editable .[dev]#pipinstall-e.'[dev]'(onmac)Run testsMemo tests can be run by:pytestAnd the code linter withprospectorLicenseMEMO 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.