Optimal transport analysis reveals trajectories in steady-state systems

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Abstract

Understanding how cells change their identity and behaviour in living systems is an important question in many fields of biology. The problem of inferring cell trajectories from single-cell measurements has been a major topic in the single-cell analysis community, with different methods developed for equilibrium and non-equilibrium systems (e.g. haematopoeisis vs. embryonic development). We show that optimal transport analysis, a technique originally designed for analysing time-courses, may also be applied to infer cellular trajectories from a single snapshot of a population in equilibrium. Therefore, optimal transport provides a unified approach to inferring trajectories that is applicable to both stationary and non-stationary systems. Our method, StationaryOT, is mathematically motivated in a natural way from the hypothesis of a Waddington’s epigenetic landscape. We implement StationaryOT as a software package and demonstrate its efficacy in applications to simulated data as well as single-cell data from Arabidopsis thaliana root development.

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Zhang, S., Afanassiev, A., Greenstreet, L., Matsumoto, T., & Schiebinger, G. (2021). Optimal transport analysis reveals trajectories in steady-state systems. PLoS Computational Biology, 17(12). https://doi.org/10.1371/JOURNAL.PCBI.1009466

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