Characterization of cell fate probabilities in single-cell data with Palantir

Abstract

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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Fig. 1: Palantir characterizes cell fate choices in a continuous model of differentiation.
Fig. 2: Differentiation landscape of early human hematopoiesis.
Fig. 3: Palantir DP identifies landmarks of hematopoietic differentiation.
Fig. 4: Transcriptional regulation of erythroid differentiation.
Fig. 5: Palantir generalizes to mouse hematopoiesis and colon differentiation datasets.

Code availability

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).

Data availability

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.

Change history

  • 18 September 2019

    An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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Acknowledgements

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.

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M.S. and D.P. conceived the study, designed and developed Palantir, developed additional analysis methods, analyzed the data, and wrote the manuscript. M.S. implemented Palantir and all other analysis methods. V.K. and L.M. designed, optimized, and executed all single-cell RNA-seq experiments. J.L. and D.P. developed an early theory on application of Markov chains to single-cell data. M.S. and A.G. developed trend-based clustering analysis.

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Correspondence to Dana Pe’er.

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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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