Inferring stochastic dynamics with growth from cross-sectional data
Journal:
arXiv
Published Date:
May 19, 2025
Abstract
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.
