Single-cell RNA sequencing studies of differentiating systems have raised fundamental questions regarding the discrete versus continuous nature of both differentiation and cell fate. Here we present Palantir, an algorithm that models trajectories of differentiating cells by treating cell fate as a probabilistic process and leverages entropy to measure cell plasticity along the trajectory. Palantir generates a high-resolution pseudo-time ordering of cells and, for each cell state, assigns a probability of differentiating into each terminal state. We apply our algorithm to human bone marrow single-cell RNA sequencing data and detect important landmarks of hematopoietic differentiation. Palantir’s resolution enables the identification of key transcription factors that drive lineage fate choice and closely track when cells lose plasticity. We show that Palantir outperforms existing algorithms in identifying cell lineages and recapitulating gene expression trends during differentiation, is generalizable to diverse tissue types, and is well-suited to resolving less-studied differentiating systems.
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Palantir is available as a Python module at https://github.com/dpeerlab/Palantir/. A Jupyter notebook detailing the workflow including data preprocessing, running Palantir along with a demonstration of various plots, and visualizations is available at http://nbviewer.jupyter.org/github/dpeerlab/Palantir/blob/master/notebooks/Palantir_sample_notebook.ipynb. The code and data for this article, along with an accompanying computational environment, are available and executable online as a Code Ocean capsule: https://doi.org/10.24433/CO.6f3a9d2b-82d6-45bd-a583-5346a30e0c5d (ref. 58).
Raw and processed data are available through the Human Cell Atlas data portal at https://data.humancellatlas.org/explore/projects/091cf39b-01bc-42e5-9437-f419a66c8a45.
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We thank R. Sharma for valuable conversations related to this manuscript, C. Trasande and T. Nawy for helping to write the manuscript, and E. Azizi, C. Burdziak, and K. Hadjantonakis for valuable comments. This study was supported by NIH grants nos. NIH DP1-HD084071 and NIH R01CA164729, Cancer Center Support Grant no. P30 CA008748, and the Gerry Center for Metastasis and Tumor Ecosystems.
The authors declare no competing interests.
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Setty, M., Kiseliovas, V., Levine, J. et al. Characterization of cell fate probabilities in single-cell data with Palantir. Nat Biotechnol 37, 451–460 (2019). https://doi.org/10.1038/s41587-019-0068-4
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