ScNODE : generative model for temporal single cell transcriptomic data prediction.

ABSTRACT:

Summary

Measurement of single-cell gene expression at different timepoints enables the study of cell development. However, due to the resource constraints and technical challenges associated with the single-cell experiments, researchers can only profile gene expression at discrete and sparsely sampled timepoints. This missing timepoint information impedes downstream cell developmental analyses. We propose scNODE, an end-to-end deep learning model that can predict in silico single-cell gene expression at unobserved timepoints. scNODE integrates a variational autoencoder with neural ordinary differential equations to predict gene expression using a continuous and nonlinear latent space. Importantly, we incorporate a dynamic regularization term to learn a latent space that is robust a

SUBMITTER: Zhang J 

PROVIDER: S-EPMC11373355 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature