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

Area of Science:

• Computational Biology
• Single-cell Genomics
• Developmental Biology

Background:

• Quantitative inference of developmental dynamics from single-cell data is crucial for understanding cell differentiation and proliferation.
• Existing pseudodynamics frameworks lack scalable and user-friendly implementations for modern single-cell workflows.

Purpose of the Study:

• To present scPD, a high-performance Python toolkit that implements and extends the pseudodynamics framework.
• To enable efficient and scalable kinetic parameter inference for large-scale single-cell datasets.
• To facilitate the integration of quantitative population dynamics analysis into standard Python-based pipelines.

Main Methods:

• Implementation of an efficient and scalable inference strategy within the Scanpy ecosystem.
• Development of scPD as a Python toolkit for pseudodynamics analysis.
• Integration with existing Python-based single-cell analysis pipelines.

Main Results:

• scPD enables the analysis of large-scale single-cell datasets with substantially reduced computational cost.
• The toolkit provides a scalable and user-friendly implementation of the pseudodynamics framework.
• Facilitates quantitative characterization of population dynamics from time-resolved single-cell data.

Conclusions:

• scPD offers a powerful and accessible tool for advancing quantitative analysis of single-cell developmental dynamics.
• The toolkit's scalability and integration capabilities support the analysis of complex biological systems.
• Enables deeper insights into cell differentiation and proliferation processes through efficient kinetic parameter inference.