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CellRank: dynamics from multi-view single-cell data#

CellRank [Lange et al., 2022] is a modular framework to study cellular dynamics based on Markov state modeling of multi-view single-cell data. It estimates differentiation direction based on a varied (and growing!) number of biological priors including RNA velocity, pseudotime, developmental potential and experimental time points. See about CellRank to learn more.

CellRank scales to large cell numbers, is fully compatible with the scverse ecosystem, and is easy to use. In the backend, it is powered by the pyGPCCA package [Reuter et al., 2019, Reuter et al., 2022]. Feel free to open an issue or send us an email if you encounter a bug, need our help or just want to make a comment/suggestion.

CellRank’s key applications#

  • compute initial, terminal and intermediate macrostates [Reuter et al., 2019, Reuter et al., 2022].

  • infer fate probabilities towards terminal states.

  • visualize gene expression trends along specific trajectories.

  • identify potential driver genes for each trajectory.

  • … and much more, check out our API.

Getting started with CellRank#

We have Tutorials and Examples that help you getting started. Tutorials are longer and explain computational pipelines, examples are short and demonstrate individual steps. To see CellRank in action, explore our manuscript [Lange et al., 2022] in Nature Methods.


We actively encourage any contribution! To get started, please check out the Contributing guide.