HHS Public Access cell fate probabilities in single-cell data with Palantir

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Nat Biotechnol. Author manuscript; available in PMC 2020 October 12.
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Nat Biotechnol. 2019 April ; 37(4): 451–460. doi:10.1038/s41587-019-0068-4.
Characterization of cell fate probabilities in single-cell data with Palantir
Manu Setty1, Vaidotas Kiseliovas1, Jacob Levine1, Adam Gayoso1, Linas Mazutis1, Dana Pe’er1,2
1Program for Computational and Systems Biology, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Abstract
Single-cell RNA sequencing (scRNA-seq) 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— treating cell fate as a probabilistic process—and leverages entropy to measure cell plasticity along the trajectory. Palantir generates a high-resolution pseudotime 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 scRNA-seq 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 generalizable to diverse tissue types and well-suited to resolve less-studied differentiating systems.
Introduction
Differentiation is among the most fundamental processes in biology. In the traditional view, cells transition from a less- to a more-differentiated state via a series of discrete, well- defined stages. Single-cell studies1-6 have, however, demonstrated that during differentiation, cell states reside along largely continuous spaces . Despite this evolution in thinking, cell fate decisions continue to be largely conceptualized as a series of discrete bifurcations along development, leading to terminal cell states7, 8.
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2Corresponding Author To whom the correspondence must be addressed: peerd@mskcc.org.
Author Contributions
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.
Competing Interests
None of the authors have any financial interests related to this research.