An article published in GenomeBiology presents scTour: an innovative and comprehensive method for dissecting cellular dynamics by analyzing datasets derived from single-cell genomics.
], scTour delivers three major innovations. Firstly, scTour introduces a neural network for inferring the developmental time of a given cell based on its transcriptome. This operation enables the model to bypass the dependence on the prior knowledge of the cell timeline, and endows scTour with the ability to suit any data beyond the timestamped ones.
As a result, scTour provides two main functionalities in deciphering cellular dynamics in a batch-insensitive manner: inference and prediction . For inference, the time neural network in scTour allows estimates of cell-level pseudotime along the trajectory. The learned differential equation by another neural network provides an alternative way of inferring the transcriptomic vector field.
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