TY - GEN
T1 - Learning Latent Trajectories in Developmental Time Series with Hidden-Markov Optimal Transport
AU - Halmos, Peter
AU - Gold, Julian
AU - Liu, Xinhao
AU - Raphael, Benjamin J.
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
PY - 2025
Y1 - 2025
N2 - 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 cell types from single-cell or spatial developmental time series. HM-OT uses low-rank optimal transport to simultaneously align samples in a time series and learn a sequence of clusterings and a differentiation map with minimal total transport cost.
AB - 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 cell types from single-cell or spatial developmental time series. HM-OT uses low-rank optimal transport to simultaneously align samples in a time series and learn a sequence of clusterings and a differentiation map with minimal total transport cost.
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U2 - 10.1007/978-3-031-90252-9_41
DO - 10.1007/978-3-031-90252-9_41
M3 - Conference contribution
AN - SCOPUS:105004252466
SN - 9783031902512
T3 - Lecture Notes in Computer Science
SP - 367
EP - 370
BT - Research in Computational Molecular Biology - 29th International Conference, RECOMB 2025, Proceedings
A2 - Sankararaman, Sriram
PB - Springer Science and Business Media Deutschland GmbH
T2 - 29th International Conference on Research in Computational Molecular Biology, RECOMB 2025
Y2 - 26 April 2025 through 29 April 2025
ER -