Cytopath: Simulation based inference of differentiation trajectories from RNA velocity fields

Abstract

AO_SCPLOWBSTRACTC_SCPLOWTrajectory inference from single-cell RNA sequencing data bears the potential to systematically reconstruct complex differentiation processes, however inferring trajectories that accurately model the biological characteristics of varied processes continues to be a challenge, notwithstanding the many available solutions. In general, trajectory and pseudotime inference methods have so far suffered from the ambiguity of static single-cell transcriptome snapshots lacking a concept of directionality and rate of transcriptional activity. We report Cytopath, a method for trajectory inference that takes advantage of transcriptional activity information from RNA velocity of single-cells to perform trajectory inference. Cytopath performs this task by defining a Markov chain model, simulating an ensemble of possible differentiation trajectories and constructs a consensus trajectory. We show that Cytopath can recapitulate the topological and molecular characteristics of the differentiation process under study. In our analysis we include differentiation trajectories with varying bifurcated, circular, convergent and mixed topology studied in single-snapshot as well as time-series single-cell RNA sequencing experiments. We demonstrate superior and enabling capability to reconstruct differentiation trajectories in comparison to state-of-the art trajectory inference approaches.