scTour: a deep learning architecture for robust inference and accurate prediction of cellular dynamics

Despite the continued efforts to computationally dissect developmental processes using single-cell genomics, a batch-unaffected tool that is able to both infer and predict the underlying dynamics is lacking. Here, I present scTour, a novel deep learning architecture to perform robust inference and accurate prediction of the cellular dynamics in diverse processes. For inference, scTour can efficiently and simultaneously estimate the developmental pseudotime, intronic read-independent vector field, and transcriptomic latent space under a single, integrated framework. For prediction, scTour can precisely reconstruct the underlying dynamics of unseen cellular states or an independent dataset agnostic to the model. Of note, both the inference and prediction are invariant to batch effects. scTours functionalities are successfully applied to a variety of biological processes from 17 datasets such as cell differentiation, reprogramming and zonation, providing a comprehensive infrastructure to investigate the cellular mechanisms underpinning development in an efficient manner.