Simulation-based inference of differentiation trajectories from RNA velocity fields.

Revant Gupta, Dario Cerletti, Gilles Gut, Annette Oxenius, Manfred Claassen
Author Information
  1. Revant Gupta: Internal Medicine I, University Hospital Tübingen, Faculty of Medicine, University of Tübingen, Tübingen, Germany.
  2. Dario Cerletti: Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland.
  3. Gilles Gut: Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland.
  4. Annette Oxenius: Institute of Microbiology, ETH Zurich, Zurich, Switzerland.
  5. Manfred Claassen: Internal Medicine I, University Hospital Tübingen, Faculty of Medicine, University of Tübingen, Tübingen, Germany.

Abstract

We report Cytopath, a method for trajectory inference that takes advantage of transcriptional activity information from the 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 constructing 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 topologies studied in single-snapshot as well as time-series single-cell RNA sequencing experiments. We demonstrate the capability to reconstruct differentiation trajectories, assess the association of RNA velocity-based pseudotime with actually elapsed process time, and identify drawbacks in current state-of-the art trajectory inference approaches.

Keywords

References

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MeSH Term

RNA
Computer Simulation
Cell Differentiation
Markov Chains

Chemicals

RNA

Word Cloud

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