LiQian-XC/sctour
A deep learning architecture for robust inference and accurate prediction of cellular dynamics
This tool helps cell biologists and developmental scientists understand how individual cells change and develop over time. By inputting single-cell genomics data, you get a comprehensive view of cellular developmental processes, including the 'age' of cells (pseudotime), their predicted future states (vector fields), and how they relate in a hidden 'latent space.' This is designed for researchers studying cell differentiation, disease progression, or tissue development.
No commits in the last 6 months. Available on PyPI.
Use this if you need to analyze single-cell genomics datasets to infer and predict cellular dynamics without being bothered by experimental batch effects or needing to specify starting cells.
Not ideal if you are working with bulk RNA-seq data or need a tool for basic gene expression analysis rather than dynamic cellular trajectory mapping.
Stars
79
Forks
4
Language
Python
License
MIT
Category
Last pushed
Jul 20, 2025
Commits (30d)
0
Dependencies
9
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