cellrank 1.1.0

CellRank - Probabilistic Fate Mapping using RNA Velocity

CellRank - Probabilistic Fate Mapping using RNA Velocity

CellRank is a toolkit to uncover cellular dynamics based on scRNA-seq data with RNA velocity annotation, see La Manno et al. (2018) and Bergen et al. (2020). In short, CellRank models cellular dynamics as a Markov chain, where transition probabilities are computed based on RNA velocity and transcriptomic similarity, taking into account uncertainty in the velocities and the stochastic nature of cell fate decisions. The Markov chain is coarse-grained into a set of macrostates which represent initial & terminal states as well as transient intermediate states. For each transient cell, i.e. for each cell that’s not assigned to a terminal state, we then compute its fate probability of it reaching any of the terminal states. We show an example of such a fate map in the figure above, which has been computed using the data of pancreatic endocrinogenesis.

CellRank scales to large cell numbers, is fully compatible with scanpy and scvelo and is easy to use. For installation instructions, documentation and tutorials, visit cellrank.org.

Manuscript

Please see our preprint on bioRxiv to learn more.

CellRank’s key applications

• compute initial & terminal as well as intermediate macrostates of your biological system

• infer fate probabilities towards the terminal states for each individual cell

• visualize gene expression trends along specific linegeages while accounting for the continous nature of fate determination

• identify potential driver genes for each identified cellular trajectory

Installation

Install CellRank by running:

conda install -c conda-forge -c bioconda cellrank
# or with extra libraries, useful for large datasets
conda install -c conda-forge -c bioconda cellrank-krylov

or via PyPI:

pip install cellrank
# or with extra libraries, useful for large datasets
pip install 'cellrank[krylov]'

Why is it called “CellRank”?

CellRank does not rank cells, we gave the package this name because just like Google’s original PageRank algorithm, it works with Markov chains to aggregate relationships between individual objects (cells vs. websites) to learn about more global properties of the underlying dynamics (initial & terminal states and fate probabilities vs. website relevance).

Support

We welcome your feedback! 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 was developed in collaboration between the Theislab and the Peerlab.