TY  - JOUR
AU  - Aivazidis, Alexander
AU  - Memi, Fani
AU  - Kleshchevnikov, Vitalii
AU  - Er, Sezgin
AU  - Clarke, Brian
AU  - Stegle, Oliver
AU  - Bayraktar, Omer Ali
TI  - Cell2fate infers RNA velocity modules to improve cell fate prediction.
JO  - Nature methods
VL  - 22
IS  - 4
SN  - 1548-7091
CY  - London [u.a.]
PB  - Nature Publishing Group
M1  - DKFZ-2025-00475
SP  - 698-707
PY  - 2025
N1  - 2025 Apr;22(4):698-707
AB  - RNA velocity exploits the temporal information contained in spliced and unspliced RNA counts to infer transcriptional dynamics. Existing velocity models often rely on coarse biophysical simplifications or numerical approximations to solve the underlying ordinary differential equations (ODEs), which can compromise accuracy in challenging settings, such as complex or weak transcription rate changes across cellular trajectories. Here we present cell2fate, a formulation of RNA velocity based on a linearization of the velocity ODE, which allows solving a biophysically more accurate model in a fully Bayesian fashion. As a result, cell2fate decomposes the RNA velocity solutions into modules, providing a biophysical connection between RNA velocity and statistical dimensionality reduction. We comprehensively benchmark cell2fate in real-world settings, demonstrating enhanced interpretability and power to reconstruct complex dynamics and weak dynamical signals in rare and mature cell types. Finally, we apply cell2fate to the developing human brain, where we spatially map RNA velocity modules onto the tissue architecture, connecting the spatial organization of tissues with temporal dynamics of transcription.
LB  - PUB:(DE-HGF)16
C6  - pmid:40032996
DO  - DOI:10.1038/s41592-025-02608-3
UR  - https://inrepo02.dkfz.de/record/299517
ER  -