Learning Latent Trajectories in Developmental Time Series with Hidden-Markov Optimal Transport.

ABSTRACT: Deriving the sequence of transitions between cell types, or differentiation events, that occur during organismal development is one of the fundamental challenges in developmental biology. Single-cell and spatial sequencing of samples from different developmental timepoints provide data to investigate differentiation but inferring a sequence of differentiation events requires: (1) finding trajectories, or ancestor:descendant relationships, between cells from consecutive timepoints; (2) coarse-graining these trajectories into a differentiation map, or collection of transitions between cell types, rather than individual cells. We introduce Hidden-Markov Optimal Transport (HM-OT), an algorithm that simultaneously groups cells into cell types and learns transitions between these c

SUBMITTER: Halmos P 

PROVIDER: S-EPMC11870411 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature