MeRN tutorial

MeRN tutorial#

A walkthrough of MeRN on mouse embryogenesis data: loading a pretrained model, extracting the metabolic and background embeddings, decoding reaction activity, building KEGG pathway scores and data-driven pathways (DDPs), and comparing metabolic coordination between wild-type and Folr1 knockout embryos.

Running it#

The notebook, example data, and pretrained models are included in the GitHub repository. Installing mern with pip installs the library; clone the repository to get the complete tutorial:

git clone https://github.com/wagnerlab-berkeley/mern.git
cd mern/docs/source/tutorials
conda env create -f environment.yml
conda activate mern-tutorial
jupyter lab MeRN_tutorial.ipynb

If you already have a clone, start from its docs/source/tutorials directory and run the environment and notebook commands above.

Create the environment from this directory: environment.yml installs the mern package from the surrounding clone by relative path.

The supplied data are already prepared, and the notebook loads pretrained models on CPU by default. You can run the analyses without preprocessing data or training new models. Everything the notebook reads is in this directory.

The first run expands the five model checkpoints to about 524 MB. Kaleido 1.x also requires Chrome for the notebook’s static image exports; if Chrome is not installed, run plotly_get_chrome after creating the environment.

Installing without conda#

brew install graphviz            # or: apt-get install graphviz graphviz-dev
pip install -e ../../.. kaleido jupyterlab ipywidgets

What this folder contains#

path

contents

data/main_wt_anndata_pp_subsample.h5ad.gz

wild-type cells, model feature space, log1p X plus raw counts_RNA

data/folr1_anndata_subsample.h5ad.gz

Folr1 knockout cells, same feature space

data/folr1_average_enzyme_activity.csv.gz

knockout reaction activity, its own replicate ensemble

data/wt_ref_color_mapping.csv

cell-type colors used in the paper’s figures

model/mern_rep_0/model.pt.gz … mern_rep_4/

five pretrained replicate checkpoints, gzipped

model/mern_rep_0/metabolic_clustering_0.4.csv

the paper’s metabolic clusters, for comparison with the ones recomputed here

About the subsampled data#

This is a cell subsample of the published dataset. The full study covers 94,204 cells (62,433 wild-type, 31,771 Folr1); here is a stratified sample of those, drawn proportionally across (metabolic state x germ layer).

Only cells were sampled. The gene space, the highly_variable_metabolic / highly_variable_background flags, the reaction graph, and the models are exactly those of the published run, so the pretrained checkpoints load and decode as they do on the full data. The wild-type reaction activity is decoded in the notebook from the 5 included replicates. The Folr1 reaction activity table is the published 8-decode, 10-replicate ensemble, subset to these cells.

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