Inferring stochastic dynamics with growth from cross-sectional data
This work addresses a major problem in computational biology for researchers needing to model cell fate processes from limited data, though it appears incremental as it builds on existing frameworks.
The paper tackled the challenge of inferring stochastic dynamics with growth from cross-sectional single-cell omics data, presenting a novel method called unbalanced probability flow inference that achieved higher accuracy compared to existing methods on simulated and real datasets.
Time-resolved single-cell omics data offers high-throughput, genome-wide measurements of cellular states, which are instrumental to reverse-engineer the processes underpinning cell fate. Such technologies are inherently destructive, allowing only cross-sectional measurements of the underlying stochastic dynamical system. Furthermore, cells may divide or die in addition to changing their molecular state. Collectively these present a major challenge to inferring realistic biophysical models. We present a novel approach, \emph{unbalanced} probability flow inference, that addresses this challenge for biological processes modelled as stochastic dynamics with growth. By leveraging a Lagrangian formulation of the Fokker-Planck equation, our method accurately disentangles drift from intrinsic noise and growth. We showcase the applicability of our approach through evaluation on a range of simulated and real single-cell RNA-seq datasets. Comparing to several existing methods, we find our method achieves higher accuracy while enjoying a simple two-step training scheme.