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TrajectoryNet: A Dynamic Optimal Transport Network for Modeling Cellular Dynamics.

Alexander Tong1, Jessie Huang1, Guy Wolf2,3

  • 1Department of Computer Science, Yale University, New Haven, CT, USA.

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TrajectoryNet models continuous cellular dynamics using dynamic optimal transport and continuous normalizing flows. This approach improves trajectory inference from single-cell RNA sequencing data compared to static methods.

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Area of Science:

  • Computational Biology
  • Biophysics
  • Machine Learning

Background:

  • Dynamic processes are often measured using static, cross-sectional data, especially in biomedical research.
  • Existing optimal transport methods model trajectories but struggle with continuous dynamics and non-linear paths.
  • Single-cell RNA sequencing (scRNA-seq) generates complex temporal data requiring advanced modeling.

Purpose of the Study:

  • To develop a novel method for modeling continuous dynamics and inferring individual cell trajectories from time-series data.
  • To address limitations of static optimal transport in capturing non-linear biological processes.
  • To enhance the analysis of cellular dynamics using single-cell RNA sequencing data.

Main Methods:

  • Establishing a theoretical link between continuous normalizing flows and dynamic optimal transport.
  • Introducing TrajectoryNet, a model that controls paths between distributions for dynamic optimal transport.
  • Applying TrajectoryNet to scRNA-seq data for modeling cellular dynamics.

Main Results:

  • TrajectoryNet successfully models continuous paths between distributions, enabling dynamic optimal transport.
  • The model demonstrates improved performance in inferring cellular trajectories compared to static optimal transport methods.
  • TrajectoryNet provides a robust framework for analyzing complex cellular dynamics.

Conclusions:

  • TrajectoryNet offers a powerful new approach for understanding dynamic biological systems from static measurements.
  • The integration of continuous normalizing flows and dynamic optimal transport advances trajectory inference in computational biology.
  • This method significantly enhances the utility of scRNA-seq data for studying cellular differentiation and development.