scPD: a Python package for inferring continuous population dynamics from single-cell snapshot data
Clicks: 2
ID: 319497
2026
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This
article has not been analysed, so there is no overall score —
reader engagement is measured and shown alongside.
Reader Engagement
Emerging Content
0.3
/100
2 views
1 readers
AI Quality Assessment
Not analyzed
Readership in this journal
EmergingRanked #75 of 104 articles by views in Bioinformatics advances
Most read
Least read
Bar heights use a square-root scale.
Mint this article as an NFT
Not yet mintedCreate a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.
5
SUSD
one-off · no wallet required
Abstract
Abstract Summary 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. Availability and implementation 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. Supplementary information Supplementary data are available at Bioinformatics advances online.
| Reference Key |
openalex_W7167076887
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | Yusong Yin, Hong Qi, Huan Hu |
| Journal | Bioinformatics advances |
| Year | 2026 |
| DOI |
10.1093/bioadv/vbag188
|
| URL | |
| Keywords | Keywords not found |
Citations
No citations found. To add a citation, contact the admin at info@scimatic.org
Comments
No comments yet. Be the first to comment on this article.