RT Journal Article
SR Electronic
T1 Modeling Single-Cell Dynamics Using Unbalanced Parameterized Monge Maps
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2022.10.04.510766
DO 10.1101/2022.10.04.510766
A1 Luca Vincent Eyring
A1 Dominik Klein
A1 Giovanni Palla
A1 Soeren Becker
A1 Philipp Weiler
A1 Niki Kilbertus
A1 Fabian J. Theis
YR 2022
UL http://biorxiv.org/content/early/2022/10/05/2022.10.04.510766.abstract
AB Optimal Transport (OT) has proven useful to infer single-cell trajectories of developing biological systems by aligning distributions across time points. Recently, Parameterized Monge Maps (PMM) were introduced to learn the optimal map between two distributions. Here, we apply PMM to model single-cell dynamics and show that PMM fails to account for asymmetric shifts in cell state distributions. To alleviate this limitation, we propose Unbalanced Parameterised Monge Maps (UPMM). We first describe the novel formulation and show on synthetic data how our method extends discrete unbalanced OT to the continuous domain. Then, we demonstrate that UPMM outperforms well-established trajectory inference methods on real-world developmental single-cell data.Competing Interest StatementFabian J. Theis consults for Immunai Inc., Singularity Bio B.V., CytoReason Ltd, and Omniscope Ltd, and has ownership interest in Dermagnostix GmbH and Cellarity.