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). 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 pancreatic endocrinogenesis.
CellRank's key applications
- 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
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]'