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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:info@cellrank.org .. _issue: https://github.com/theislab/cellrank/issues/new/choose