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Extracting a cellular hierarchy from high-dimensional cytometry data with SPADE - Nature Biotechnology

  • ️Plevritis, Sylvia K
  • ️Sun Oct 02 2011
  • Analysis
  • Published: 02 October 2011

Nature Biotechnology volume 29, pages 886–891 (2011)Cite this article

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Abstract

The ability to analyze multiple single-cell parameters is critical for understanding cellular heterogeneity. Despite recent advances in measurement technology, methods for analyzing high-dimensional single-cell data are often subjective, labor intensive and require prior knowledge of the biological system. To objectively uncover cellular heterogeneity from single-cell measurements, we present a versatile computational approach, spanning-tree progression analysis of density-normalized events (SPADE). We applied SPADE to flow cytometry data of mouse bone marrow and to mass cytometry data of human bone marrow. In both cases, SPADE organized cells in a hierarchy of related phenotypes that partially recapitulated well-described patterns of hematopoiesis. We demonstrate that SPADE is robust to measurement noise and to the choice of cellular markers. SPADE facilitates the analysis of cellular heterogeneity, the identification of cell types and comparison of functional markers in response to perturbations.

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Acknowledgements

The authors gratefully acknowledge funding from National Cancer Institute Integrative Cancer Biology Program (ICBP), grants U56CA112973 and U54CA149145 to S.K.P. A Damon Runyon Cancer Research Foundation Fellowship supports S.C.B. National Science Foundation Graduate Research Fellowship and Stanford DARE Fellowship support K.D.G. This work is also supported by US National Institutes of Health grants U19 AI057229, P01 CA034233, HHSN272200700038C, 1R01CA130826, 5U54 CA143907, RB2-01592, PN2EY018228, N01-HV-00242, HEALTH.2010.1.2-1 (European Commission), as well as the California Institute for Regenerative Medicine (DR1-01477) to G.P.N.

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Authors and Affiliations

  1. Department of Radiology, Stanford University, Stanford, California, USA

    Peng Qiu & Sylvia K Plevritis

  2. Department of Bioinformatics and Computational Biology, University of Texas, M.D. Anderson Cancer Center, Houston, Texas, USA

    Peng Qiu

  3. Department of Microbiology and Immunology, Stanford University, Stanford, California, USA

    Erin F Simonds, Sean C Bendall, Kenneth D Gibbs Jr, Robert V Bruggner, Karen Sachs & Garry P Nolan

  4. Computer Systems Laboratory, Stanford University, Stanford, California, USA

    Michael D Linderman

Authors

  1. Peng Qiu

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  2. Erin F Simonds

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  3. Sean C Bendall

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  4. Kenneth D Gibbs Jr

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  5. Robert V Bruggner

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  6. Michael D Linderman

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  7. Karen Sachs

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  8. Garry P Nolan

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  9. Sylvia K Plevritis

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Contributions

P.Q., G.P.N. and S.K.P. conceived the study and developed the method. E.F.S., S.C.B. and K.D.G.Jr. performed mass and flow cytometry experiments, and participated in the biological interpretation. P.Q., R.V.B., M.D.L. and K.S. performed robustness analysis of the method. P.Q., E.F.S., S.C.B., K.D.G.Jr., G.P.N. and S.K.P. wrote the manuscript and developed the figures.

Corresponding author

Correspondence to Peng Qiu.

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Competing interests

A patent for the SPADE algorithm has been applied for on behalf of Stanford University.

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Qiu, P., Simonds, E., Bendall, S. et al. Extracting a cellular hierarchy from high-dimensional cytometry data with SPADE. Nat Biotechnol 29, 886–891 (2011). https://doi.org/10.1038/nbt.1991

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  • Received: 10 January 2011

  • Accepted: 31 August 2011

  • Published: 02 October 2011

  • Issue Date: October 2011

  • DOI: https://doi.org/10.1038/nbt.1991

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