PT  - JOURNAL ARTICLE
AU  - Luca Vincent Eyring
AU  - Dominik Klein
AU  - Giovanni Palla
AU  - Soeren Becker
AU  - Philipp Weiler
AU  - Niki Kilbertus
AU  - Fabian J. Theis
TI  - Modeling Single-Cell Dynamics Using Unbalanced Parameterized Monge Maps
AID  - 10.1101/2022.10.04.510766
DP  - 2022 Jan 01
TA  - bioRxiv
PG  - 2022.10.04.510766
4099  - http://biorxiv.org/content/early/2022/10/05/2022.10.04.510766.short
4100  - http://biorxiv.org/content/early/2022/10/05/2022.10.04.510766.full
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.