TrajectoryNet: A Dynamic Optimal Transport Network for Modeling Cellular Dynamics
This addresses the challenge of accurately capturing cellular dynamics in biomedical research, offering a more precise method for studying processes like development or disease progression from single-cell data.
The paper tackles the problem of modeling continuous and non-linear cellular trajectories from static cross-sectional data by linking continuous normalizing flows with dynamic optimal transport, resulting in TrajectoryNet, which improves upon static optimal transport-based models for interpolating cellular distributions in scRNA-seq data.
It is increasingly common to encounter data from dynamic processes captured by static cross-sectional measurements over time, particularly in biomedical settings. Recent attempts to model individual trajectories from this data use optimal transport to create pairwise matchings between time points. However, these methods cannot model continuous dynamics and non-linear paths that entities can take in these systems. To address this issue, we establish a link between continuous normalizing flows and dynamic optimal transport, that allows us to model the expected paths of points over time. Continuous normalizing flows are generally under constrained, as they are allowed to take an arbitrary path from the source to the target distribution. We present TrajectoryNet, which controls the continuous paths taken between distributions to produce dynamic optimal transport. We show how this is particularly applicable for studying cellular dynamics in data from single-cell RNA sequencing (scRNA-seq) technologies, and that TrajectoryNet improves upon recently proposed static optimal transport-based models that can be used for interpolating cellular distributions.