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Visualization and cellular hierarchy inference of single-cell data using SPADE

Nature Protocols volume 11, pages 12641279 (2016) | Download Citation

Abstract

High-throughput single-cell technologies provide an unprecedented view into cellular heterogeneity, yet they pose new challenges in data analysis and interpretation. In this protocol, we describe the use of Spanning-tree Progression Analysis of Density-normalized Events (SPADE), a density-based algorithm for visualizing single-cell data and enabling cellular hierarchy inference among subpopulations of similar cells. It was initially developed for flow and mass cytometry single-cell data. We describe SPADE's implementation and application using an open-source R package that runs on Mac OS X, Linux and Windows systems. A typical SPADE analysis on a 2.27-GHz processor laptop takes 5 min. We demonstrate the applicability of SPADE to single-cell RNA-seq data. We compare SPADE with recently developed single-cell visualization approaches based on the t-distribution stochastic neighborhood embedding (t-SNE) algorithm. We contrast the implementation and outputs of these methods for normal and malignant hematopoietic cells analyzed by mass cytometry and provide recommendations for appropriate use. Finally, we provide an integrative strategy that combines the strengths of t-SNE and SPADE to infer cellular hierarchy from high-dimensional single-cell data.

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References

  1. 1.

    et al. Extracting a cellular hierarchy from high-dimensional cytometry data with SPADE. Nat. Biotechnol. 29, 886–891 (2011).

  2. 2.

    et al. Single-cell mass cytometry of differential immune and drug responses across a human hematopoietic continuum. Science 332, 687–696 (2011).

  3. 3.

    et al. Multiplexed mass cytometry profiling of cellular states perturbed by small-molecule regulators. Nat. Biotechnol. 30, 858–867 (2012).

  4. 4.

    Inferring phenotypic properties from single-cell characteristics. PLoS One 7, e37038 (2012).

  5. 5.

    & The earthmover's distance is the Mallows distance: some insights from statistics. Proceedings of ICCV 2001 (Vancouver, Canada) 251–256 (2001).

  6. 6.

    et al. Pattern Recognition in Bioinformatics. Lecture Notes in Computer Science Vol. 7986 (Berlin: Springer, 2013).

  7. 7.

    et al. viSNE enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia. Nat. Biotechnol. 31, 545–552 (2013).

  8. 8.

    et al. RchyOptimyx: cellular hierarchy optimization for flow cytometry. Cytometry A 81, 1022–1030 (2012).

  9. 9.

    et al. Automatic classification of cellular expression by nonlinear stochastic embedding (ACCENSE). Proc. Natl. Acad. Sci. USA 111, 202–207 (2014).

  10. 10.

    et al. CCAST: a model-based gating strategy to isolate homogeneous subpopulations in a heterogeneous population of single cells. PLoS Comput. Biol. 10, e1003664 (2014).

  11. 11.

    et al. Data reduction for spectral clustering to analyze high throughput flow cytometry data. BMC Bioinformatics 11, 403 (2010).

  12. 12.

    et al. flowClust: a Bioconductor package for automated gating of flow cytometry data. BMC Bioinformatics 10, 145 (2009).

  13. 13.

    et al. SWIFT-scalable clustering for automated identification of rare cell populations in large, high-dimensional flow cytometry datasets, part 2: biological evaluation. Cytometry A 85, 422–433 (2014).

  14. 14.

    et al. Automated high-dimensional flow cytometric data analysis. Proc. Natl. Acad. Sci. USA 106, 8519–8524 (2009).

  15. 15.

    , , & SPADE – An analysis and visualization tool for Flow Cytometry. R package version 1.20. (2016).

  16. 16.

    et al. CytoSPADE: high-performance analysis and visualization of high-dimensional cytometry data. Bioinformatics 28, 2400–2401 (2012).

  17. 17.

    , & Web-based analysis and publication of flow cytometry experiments. Curr Protoc Cytom Chapter 10, 10.17 (2010).

  18. 18.

    & An optimal minimum spanning tree algorithm. JACM 49, 49–60 (1999).

  19. 19.

    Shortest connection networks and some generalizations. Bell Syst. Tech. J. 36, 1389–1401 (1957).

  20. 20.

    , , , & A continuous molecular roadmap to iPSC reprogramming through progression analysis of single-cell cytometry. Cell Stem Cell 16, 323–337 (2015).

  21. 21.

    & Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008).

  22. 22.

    Accelerating t-SNE using tree-based algorithms. J. Mach. Learn. Res. 15, 3221–3245 (2014).

  23. 23.

    et al. Reconstructing lineage hierarchies of the distal lung epithelium using single-cell RNA-seq. Nature 509, 371–375 (2014).

  24. 24.

    et al. Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat. Protoc. 7, 562–578 (2012).

  25. 25.

    , & Discovering biological progression underlying microarray samples. PLoS Comput. Biol. 7, e1001123 (2011).

  26. 26.

    et al. Critical assessment of automated flow cytometry data analysis techniques. Nat. Methods 10, 228–238 (2013).

  27. 27.

    et al. FlowSOM: using self-organizing maps for visualization and interpretation of cytometry data. Cytometry Part A 87A, 636–645 (2015).

  28. 28.

    et al. Hierarchical clustering in minimum spanning trees. Chaos 25, 023107 (2015).

  29. 29.

    & Graph drawing by force-directed placement Software: Practice & Experience. 21, 1129–1164 (1991).

  30. 30.

    & TreeVis: a MATLAB-based tool for tree visualization. Comput. Methods Programs Biomed. 109, 74–76 (2013).

  31. 31.

    & An algorithm for drawing general undirected graphs. Inform. Process. Lett. 31, 7–15 (1989).

Download references

Acknowledgements

This study was primarily supported by National Institutes of Health (NIH) grant U54CA149145, with S.K.P. as principal investigator. G.P.N. is supported by NIH grants U19 AI057229, 1U19AI100627, U54 CA149145, N01-HV-00242, 1R01CA130826, 5R01AI073724, R01 GM109836, R01CA184968, 1R01NS089533, P01 CA034233, R33 CA183654, R33 CA183692, 41000411217, 201303028, HHSN272201200028C, HHSN272200700038C, and 5U54CA143907; CIRM DR1-01477; Department of Defense grants OC110674 and 11491122; FDA grant HHSF223201210194C; Bill and Melinda Gates Foundation grant OPP1113682; Alliance for Lupus Research grant 218518; and the Rachford and Carlota A. Harris Endowed Professorship. P.Q. is supported by NIH grant R01 CA163481. S.C.B. is supported by the Damon Runyon Cancer Research Foundation Fellowship (DRG-2017-09) and NIH grant R00 GM104148-03.

Author information

Affiliations

  1. Department of Radiology, Center for Cancer Systems Biology, Stanford University, Stanford, California, USA.

    • Benedict Anchang
    • , Tom D P Hart
    •  & Sylvia K Plevritis
  2. Department of Pathology, School of Medicine, Stanford University, Stanford, California, USA.

    • Sean C Bendall
  3. Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia, USA.

    • Peng Qiu
  4. Department of Microbiology and Immunology, Stanford University, Stanford, California, USA.

    • Zach Bjornson
    •  & Garry P Nolan
  5. Computer Systems Laboratory, Stanford University, Stanford, California, USA.

    • Michael Linderman

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Contributions

B.A., T.D.P.H., S.C.B., P.Q., Z.B., M.L., G.P.N. and S.K.P. contributed to the concept of SPADE analyses. B.A., T.D.P.H. and S.K.P. were involved in the concept and design of the integrated SPADE–t-SNE analysis. B.A. and T.D.P.H. performed computational analyses. All authors interpreted the results. B.A. and S.K.P. wrote the initial drafts of the manuscript. All authors edited, read and approved the manuscript.

Competing interests

A patent (S10-010) for the SPADE algorithm has been applied for on behalf of Stanford University.

Corresponding author

Correspondence to Sylvia K Plevritis.

Integrated supplementary information

Supplementary information

PDF files

  1. 1.

    Combo PDF

    Supplementary Figures 1 and 2

  2. 2.

    Supplementary Software

    R code for how to combine SPADE and t-SNE to generate a ‘SPADE forest’ for a single FCS file.

Zip files

  1. 1.

    Supplementary Data 1

    Unlabeled subsample bone marrow data set from Bendall et al.2 used to explain the SPADE workflow in Figure 1.

  2. 2.

    Supplementary Data 2

    MCM FCS file containing expression data from manually gated normal human bone marrow cells from Bendall et al.2 used for comparison analysis. MCM FCS file of ALL single-cell data from Amir et al.7 used for comparison analysis. Data in FCS file format containing the mouse lung epithelial RNA-seq expression from Treutlein et al.23.

  3. 3.

    Supplementary Data 3

    MCM FCS file of ALL single-cell data from Amir et al. (2013)7 used for comparison analysis.

  4. 4.

    Supplementary Data 4

    Data in FCS file format containing the Mouse lung epithelial RNA-Seq expression from Treutlein et al. (2014)23

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DOI

https://doi.org/10.1038/nprot.2016.066

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