NeuroVelo: interpretable learning of cellular dynamics

NeuroVelo: physics-based interpretable learning of cellular dynamics. It is implemented on Python3 and PyTorch, the model estimate velocity field and genes that drives the splicing dynamics.

The main contributions of NeuroVelo are,

• Using linear projection and embedding for spliced and unspliced RNA to keep interpretability.
• Introducing sample specific velocity estimation, and sample specific interpretation of cellular dynamics.
• Presenting a loss function based on splicing dynamics.

Installation

pip install neurovelo

or

pip install git+https://github.com/idriskb/NeuroVelo

To avoid potential conflict, it is advised to create a seperate virtual envrionment to run the method

Getting started

1. Import package

from neurovelo.train import Trainer
from neurovelo.utils import ModelAnalyzer, latent_data

1. Training

model = Trainer(adata, sample_obs='sample')
mode.train()
model.save_model('/to/folder/','trained.pth')

2. Visualization

latent_adata = latent_data(adata, '/to/folder/trained.pth')
scv.pp.neighbors(latent_adata, use_rep='X_z', n_neighbors=20)
sc.tl.umap(latent_adata,  min_dist=0.1)
scv.tl.velocity_graph(latent_adata, vkey='spliced_velocity', xkey='spliced')
scv.tl.velocity_embedding(latent_adata, vkey='spliced_velocity', basis='umap')
scv.pl.velocity_embedding_stream(latent_adata,basis='umap',vkey='spliced_velocity', color='sample')

3. Analysis

analyzer = ModelAnalyzer(adata, n_vectors=10, '/path/to/trained_models/')
results = analyzer.models_output()
gene_ranking_order, gene_ranking_mean = analyzer.gene_ranking() #gene_ranking_mean can be directly used with prerank gene set enrichment analysis

For further and detailed instructions check the notebooks

Citation

If you find this work useful please cite:

@article {Idris2023.11.17.567500,
	author = {Idris Kouadri Boudjelthia and Salvatore Milite and Nour El Kazwini and Javier Fernandez-Mateos and Nicola Valeri and Yuanhua Huang and Andrea Sottoriva and Guido Sanguinetti},
	title = {NeuroVelo: interpretable learning of cellular dynamics from single-cell transcriptomic data},
	elocation-id = {2023.11.17.567500},
	year = {2023},
	doi = {10.1101/2023.11.17.567500},
	publisher = {Cold Spring Harbor Laboratory},
	URL = {https://www.biorxiv.org/content/early/2023/11/17/2023.11.17.567500},
	eprint = {https://www.biorxiv.org/content/early/2023/11/17/2023.11.17.567500.full.pdf},
	journal = {bioRxiv}
}