CellRank is a toolkit to uncover cellular dynamics based on scRNA-seq data with RNA velocity annotation, see [Manno18]_ and [Bergen19]_. 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. The Markov chain is coarse grained into a set of metastable states which represent root & final states as well as transient intermediate states. For each cell, we obtain the probability of it belonging to each metastable state, i.e. we compute a fate map on the single cell level. We show an example of such a fate map in the figure above, which has been computed using the data of [Panc19]_.
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 http://cellrank.org.
- compute root & final as well as intermediate metastable states of your developmental/dynamical process
- infer fate probabilities towards these states for each single cell
- visualise gene expression trends towards/from specific states
- identify potential driver genes for each state
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.