CellRank is a scalable, easy to use framework to compute directed cell-state trajectories and uncover cellular dynamics based on Markov state modeling of single-cell data. It is applicable to many single-cell fate mapping scenarios including regeneration, reprogramming and disease, for which direction is unknown.
CellRank works in two parts: kernels compute cell-cell transition probabilities and estimators generate hypothesis based on the previous step. Our kernels are able to take many different types of data as input. Our main estimator is Generalized Perron Cluster Cluster Analysis (G-PCCA) [Reuter et al., 2018] which coarse-grains the Markov chain into a set of macrostates which represent initial, terminal and intermediate states. For each transient cell, we compute its fate probability towards any terminal state.
CellRank was developed in collaboration between the Theis lab and the Peer lab.
For more information, feel free to refer to our official page or our manuscript Lange et al. (2022) in Nature Methods.
For more in-depth instructions, visit our installation page.
CellRank can be installed via:
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
pip install cellrank
# or with extra libraries, useful for large datasets
pip install 'cellrank[krylov]'
# or with external modules, see External API
pip install 'cellrank[external]'
Please refer to our official page for various examples and tutorials on how to use CellRank.