Defining cell states and unraveling cell states transition through models of stochastic dynamics during differentiation

Abstract

Cell fate decisions play pivotal roles in many fundamental biological processes such as cell differentiation, tissue reprogramming, and cancer metastasis. Computational trajectory inference using single-cell RNA sequencing data enables the discovery of cell states and state transitions. Here we present a novel method combining trajectory inference with the direction information derived from the stochastic models of the process of RNA transcription. Our approach treats cell state transition as a probabilistic process, automatically distinguishes initial starting states and the terminal states, and further delineates the underlying trajectories of cellular differentiation.