CellRank is a pyGPCCA-powered tool for analyzing cellular dynamics through Markov state modeling of single-cell data, offering insights into differentiation directions, macrostates, and gene trends in the scverse ecosystem.
CellRank is a modular framework designed to analyze cellular dynamics through Markov state modeling of multi-view single-cell data. It is fully compatible with the scverse ecosystem and leverages pyGPCCA for backend operations. The tool is essential for estimating differentiation directions based on various biological priors, computing macrostates, inferring fate probabilities and driver genes, and visualizing gene expression trends.
The project operates under the BSD-3-Clause license, promoting open redistribution and usage with or without modifications.
Detailed guidance and information are available in the official documentation, facilitating a deeper understanding of CellRank's applications and functionalities.
The CellRank tool and its methodologies are detailed in two manuscripts referred to as "CellRank 1" and "CellRank 2." To learn more and for proper citation, refer to the manuscripts section on the GitHub page.
The main contributors to the CellRank project are the members of the Theis Lab. You can find more about the team and their contributions on the GitHub page.
For bugs, help, or suggestions, feel free to open an issue or reach out via email at firstname.lastname@example.org.
Should you have any specific inquiries regarding CellRank, please do not hesitate to reach out to Philipp Weiler at email@example.com.