Featurization of single cell trajectories through kernel mean embedding of optimal transport maps

Alec Plotkin, Justin Milner, Natalie Stanley

Track: Main track (up to 8 pages)

Abstract:

Longitudinal single-cell data has spurred the development of computational
trajectory models with the power to make time-resolved, testable predictions
about cell fates. As ”real-time” trajectory inference methods proliferate, there is
a growing need for tools that integrate their inherently high-dimensional outputs.
In this work, we propose a novel strategy to facilitate downstream analysis of
single-cell optimal-transport trajectory models, by constructing feature vectors
that contain information about a cell’s state across the entirety of its trajectory.
This approach leverages kernel mean embedding of distributions to create
trajectory features with applications in several domains, including cell clustering
and comparison of perturbation response trajectories. We demonstrate how
k-means clustering on trajectory features produces interpretable clusters that
respect the underlying cell trajectories. Furthermore, we develop a divergence
metric between single-cell trajectories based on the maximum mean discrepancy
(MMD). We use this trajectory divergence to show that modeling perturbation
trajectories may help uncover experimentally interesting perturbations at higher
significance levels than by comparing perturbation responses at only a single time
point.

Submission Number: 62