WORK IN PROGRESS
This pipeline implements our TrajectoryNet pipeline for discovering gene regulatory structure from single cell dynamics in continuous time and space. We first learn likely individual cell trajectories in a maximum likelihood fashion. We then learn a graph between genes using a granger causality based score to control the edge weights. We reference this graph and known regulatory interactions to discovery likely regulatory relationships in the mesenchymal to epithelial transition.
In brief, TrajectoryNet is a Continuous Normalizing Flow model which can perform dynamic optimal transport using energy regularization and / or a combination of velocity, density, and growth regularizations to better match cellular trajectories.
Our setting is similar to that of WaddingtonOT. In that we have access to a bunch of population measurements of cells over time and would like to model the dynamics of cells over that time period. TrajectoryNet is trained end-to-end and is continuous both in gene space and in time.