cellrank 2.0.5
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cellrank 2.0.5
CellRank 2: Unified fate mapping in multiview single-cell data
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 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.
License:
For personal and professional use. You cannot resell or redistribute these repositories in their original state.
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