NeuroVelo: interpretable learning of cellular dynamics from single-cell transcriptomic data
Kouadri Boudjelthia, I.; Milite, S.; El Kazwini, N.; Fernandez-Mateos, J.; Valeri, N.; Huang, Y.; Sottoriva, A.; Sanguinetti, G.
Show abstract
Reconstructing temporal cellular dynamics from static single-cell transcriptomics remains a major challenge. Methods based on RNA velocity are useful, but interpreting their results to learn new biology remains difficult, and their predictive power is limited. Here we propose NeuroVelo, a method that couples learning of an optimal linear projection with non-linear Neural Ordinary Differential Equations. Unlike current methods, it uses dynamical systems theory to model biological processes over time, hence NeuroVelo can identify gene interactions that drive the observed temporal dynamics of gene expression. We benchmark NeuroVelo against several state-of-the-art methods using single-cell datasets, demonstrating that NeuroVelo simultaneously reconstructs correct cell-type transitions and identifies gene regulatory networks that drive cell fate directly from the data.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- ARTEMIS integrates autoencoders and schrodinger bridges to predict continuous dynamics of gene expression, cell population and perturbation from time-series single-cell data 96%
- Gene regulatory network inference from single-cell data using optimal transport 96%
- Identifying cancer pathway dysregulations using differential causal effects 96%
Similar papers in this journal
- Benchmarking imputation methods for network inference using a novel method of synthetic scRNA-seq data generation 96%
- Nested Stochastic Block Models Applied to the Analysis of Single Cell Data 95%
- eSVD-DE: Cohort-wide differential expression in single-cell RNA-seq data using exponential-family embeddings 95%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.