Citing CellRank¶

If you find CellRank useful for your research, please consider citing our work as follows: If you are using CellRank’s VelocityKernel with classical RNA velocity, cite [Lange et al., 2022] as:

@article{lange:22,
    author    = {Lange, Marius and Bergen, Volker and Klein, Michal and Setty, Manu and Reuter, Bernhard and Bakhti, Mostafa and Lickert, Heiko and Ansari, Meshal and Schniering, Janine and Schiller, Herbert B. and Pe'er, Dana and Theis, Fabian J.},
    publisher = {Nature Publishing Group},
    doi       = {10.1038/s41592-021-01346-6},
    journal   = {Nat. Methods},
    title     = {CellRank for directed single-cell fate mapping},
    year      = {2022},
}

If you are using the PseudotimeKernel, CytoTRACEKernel, RealTimeKernel, or the VelocityKernel with velocities inferred from metabolic labeling data using the CellRank 2 approach, cite [Weiler et al., 2024] as:

@article{weiler:24,
    author    = {Weiler, Philipp and Lange, Marius and Klein, Michal and Pe'er, Dana and Theis, Fabian},
    publisher = {Springer Science and Business Media LLC},
    url       = {https://doi.org/10.1038/s41592-024-02303-9},
    doi       = {10.1038/s41592-024-02303-9},
    issn      = {1548-7105},
    journal   = {Nature Methods},
    month     = jun,
    number    = {7},
    pages     = {1196--1205},
    title     = {CellRank 2: unified fate mapping in multiview single-cell data},
    volume    = {21},
    year      = {2024},
}

In addition, if you use the GPCCA estimator to compute initial, terminal or intermediate states, you are using the pyGPCCA package [Reuter et al., 2022] under the hood, which implements the Generalized Perron Cluster Cluster Analysis (GPCCA) algorithm. Thus, additionally to CellRank, please cite GPCCA [Reuter et al., 2019] as:

@article{reuter:19,
  author  = {Reuter, Bernhard and Fackeldey, Konstantin and Weber, Marcus},
  doi     = {10.1063/1.5064530},
  journal = {The Journal of Chemical Physics},
  number  = {17},
  pages   = {174103},
  title   = {Generalized Markov modeling of nonreversible molecular kinetics},
  volume  = {150},
  year    = {2019},
}

Finally, if you are following the CellRank Protocol [Weiler and Theis, 2026], please cite it as:

@article{weiler:25,
    author    = {Weiler, Philipp and Theis, Fabian J.},
    doi       = {10.1038/s41596-025-01314-w},
    journal   = {Nature Protocols},
    title     = {CellRank: consistent and data view agnostic fate mapping for single-cell genomics},
    year      = {2026},
}

CellRank papers¶

CellRank 1 — Nature Methods, 2022¶

The original CellRank publication introduced a framework for directed single-cell fate mapping based on RNA velocity and gene expression similarity. It combines these signals via a Markov chain formulation, uses the GPCCA algorithm to identify initial and terminal states, and builds on Markov chain theory to compute fate probabilities and infer driver genes.

Lange, M. et al. CellRank for directed single-cell fate mapping. Nat. Methods 19, 159–170 (2022).

See the RNA velocity tutorial to get started with the VelocityKernel.

CellRank 2 — Nature Methods, 2024¶

CellRank 2 generalizes the framework beyond RNA velocity, unifying multiple biological signals — pseudotime, developmental potential (CytoTRACE), real-time experimental time points via optimal transport, and more — into a single coherent API. It introduces the PseudotimeKernel, CytoTRACEKernel, and RealTimeKernel alongside the original VelocityKernel.

Weiler, P. et al. CellRank 2: unified fate mapping in multiview single-cell data. Nat. Methods 21, 1196–1205 (2024).

See CellRank Meets Pseudotime, CellRank Meets CytoTRACE, and CellRank Meets Experimental Time for kernel-specific tutorials.

CellRank Protocol — Nature Protocols, 2026¶

A step-by-step practical guide for applying CellRank to single-cell data. The protocol covers the full workflow from data preprocessing to fate probability computation and downstream analysis, making it the ideal starting point for new users.

Weiler, P. & Theis, F. J. CellRank: consistent and data view agnostic fate mapping for single-cell genomics. Nat. Protoc. (2026).

See the protocol GitHub repository for all accompanying notebooks and data.