scverse/cellrank
CellRank: dynamics from multi-view single-cell data
This tool helps single-cell biologists understand how cells differentiate and what their future states might be. You feed it multi-view single-cell data, such as RNA velocity, pseudotime, or experimental time points, and it outputs predictions for cell fate probabilities, identifies key cellular states (initial, terminal, intermediate), and pinpoints driver genes involved in these transitions. Researchers studying cell development, disease progression, or cellular response to stimuli would find this useful.
433 stars.
Use this if you need to determine cellular differentiation paths, estimate cell fate probabilities, or identify the genes driving these transitions from your single-cell sequencing data.
Not ideal if you are looking for general cell type annotation or need to map cells between datasets without focusing on developmental trajectories.
Stars
433
Forks
53
Language
Python
License
BSD-3-Clause
Category
Last pushed
Mar 09, 2026
Commits (30d)
0
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