scPD: a Python package for inferring continuous population dynamics from single-cell snapshot data

Quantitative inference of developmental dynamics from single-cell snapshot data is essential for disentangling differentiation and proliferation processes. The pseudodynamics framework provides a principled approach to this problem but lacks a scalable and user-friendly implementation for modern single-cell workflows. Here, we present scPD, a high-performance Python toolkit that implements and extends the pseudodynamics framework within the Scanpy ecosystem. scPD implements an efficient and scalable inference strategy, enabling the analysis of large-scale single-cell datasets with substantially reduced computational cost. This scalability enables kinetic parameter inference to be readily integrated into standard Python-based pipelines, facilitating quantitative characterization of population dynamics from time-resolved single-cell data.

scPD is implemented in Python and is freely available as an open-source package on GitHub at https://github.com/yys-arch/scPD. Documentation and example notebooks are provided. The data used in this study are publicly available under DOI: 10.5281/zenodo.18337517.