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CellRank 2: Unified fate mapping in multiview single-cell data

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CellRank is a modular framework to study cellular dynamics based on Markov state modeling of multi-view single-cell data. See our documentation, and the CellRank 1 and CellRank 2 manuscript_ to learn more. See here <https://github.com/theislab/cellrank/blob/main/docs/about/cite.rst>_ for how to properly cite our work.

CellRank scales to large cell numbers, is fully compatible with the scverse_ ecosystem, and easy to use. In the backend, it is powered by pyGPCCA_ (Reuter et al. (2018)). 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

• Estimate differentiation direction based on a varied number of biological priors, including RNA velocity (La Manno et al. (2018), Bergen et al. (2020)), any pseudotime or developmental potential, experimental time points, metabolic labels, and more.
• Compute initial, terminal and intermediate macrostates.
• Infer fate probabilities and driver genes.
• Visualize and cluster gene expression trends.
• ... and much more, check out our documentation_.

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.. _La Manno et al. (2018): https://doi.org/10.1038/s41586-018-0414-6 .. _Bergen et al. (2020): https://doi.org/10.1038/s41587-020-0591-3 .. _Reuter et al. (2018): https://doi.org/10.1021/acs.jctc.8b00079

.. _scverse: https://scverse.org/ .. _pyGPCCA: https://github.com/msmdev/pyGPCCA

.. _CellRank 1: https://www.nature.com/articles/s41592-021-01346-6 .. _CellRank 2 manuscript: https://doi.org/10.1101/2023.07.19.549685 .. _documentation: https://cellrank.org

.. _email: mailto:[email protected] .. _issue: https://github.com/theislab/cellrank/issues/new/choose