Inferring stochastic dynamics with growth from cross-sectional data
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866912869521555456 |
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| author | Zhang, Stephen Maddu, Suryanarayana Qiu, Xiaojie Chardès, Victor |
| author_facet | Zhang, Stephen Maddu, Suryanarayana Qiu, Xiaojie Chardès, Victor |
| contents | 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, 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_13197 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Inferring stochastic dynamics with growth from cross-sectional data Zhang, Stephen Maddu, Suryanarayana Qiu, Xiaojie Chardès, Victor Machine Learning Biological Physics Quantitative Methods 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, 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. |
| title | Inferring stochastic dynamics with growth from cross-sectional data |
| topic | Machine Learning Biological Physics Quantitative Methods |
| url | https://arxiv.org/abs/2505.13197 |