Welcome to memo’s documentation!
MEMO is a method allowing a Retention Time (RT) agnostic alignment of metabolomics samples using the fragmentation spectra (MS2) of their constituents. The occurrence 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, i.e. 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 (MaXis Q-ToF vs Q-Exactive).
To install it:
First, make sure to have anaconda installed.
Create a new conda environment to avoid clashes:
conda create --name memo python=3.8 conda activate memo
Install with pip:
pip install numpy pip install memo-ms
If you have an error, try installing scikit-bio from conda-forge before installing the package with pip:
conda install -c conda-forge scikit-bio pip install memo-ms
You can clone the Github package repository to get the demo files and the tutorial!