Skip to main content

CellRank: dynamics from multi-view single-cell data

Project description

PyPI Downloads CI Documentation Coverage Discourse

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

docs/_static/img/light_mode_overview.png#gh-light-mode-only docs/_static/img/dark_mode_overview.png#gh-dark-mode-only

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.

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

cellrank-2.0.4.tar.gz (193.5 kB view hashes)

Uploaded Source

Built Distribution

cellrank-2.0.4-py3-none-any.whl (225.7 kB view hashes)

Uploaded Python 3

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page