Movatterモバイル変換

[0]ホーム
URL:

Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Advertisement

Nature Protocols
  • Protocol
  • Published:

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

Nature Protocolsvolume 11, pages1264–1279 (2016)Cite this article

Subjects

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 thet-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.

This is a preview of subscription content,access via your institution

Access options

Access through your institution

Subscription info for Japanese customers

We have a dedicated website for our Japanese customers. Please go tonatureasia.com to subscribe to this journal.

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Overview of the SPADE algorithm.
Figure 2: SPADE analysis of normal human bone marrow trees colored according to the median intensities of 12 of 13 surface markers.
Figure 3: A SPADE tree derived from normal human bone marrow cells annotated with known cell types on the basis of surface marker expression.
Figure 4: SPADE analysis from multiple FCS files.
Figure 5: SPADE analysis with minimal branching.
Figure 6: A SPADE tree compared with viSNE and ACCENSE analysis of a sample from normal bone marrow cells and a sample from a patient with acute lymphoblastic leukemia (ALL).
Figure 7: Integrated analysis of SPADE with t-SNE.
Figure 8: SPADE versus t-SNE analysis of an RNA-seq single-cell data set of mouse lung epithelium from Treutleinet al.23.
Figure 9: SPADE analysis using different random seeds.
Figure 10: Comparison of SPADE analyses performed using improper and proper lineage markers.

Similar content being viewed by others

References

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

    Article CAS  Google Scholar 

  2. Bendall, S.C. et al. Single-cell mass cytometry of differential immune and drug responses across a human hematopoietic continuum.Science332, 687–696 (2011).

    Article CAS  Google Scholar 

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

    Article CAS  Google Scholar 

  4. Qiu, P. Inferring phenotypic properties from single-cell characteristics.PLoS One7, e37038 (2012).

    Article CAS  Google Scholar 

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

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

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

    Article  Google Scholar 

  8. Aghaeepour, N. et al. RchyOptimyx: cellular hierarchy optimization for flow cytometry.Cytometry A81, 1022–1030 (2012).

    Article  Google Scholar 

  9. Shekhar, K. et al. Automatic classification of cellular expression by nonlinear stochastic embedding (ACCENSE).Proc. Natl. Acad. Sci. USA111, 202–207 (2014).

    Article CAS  Google Scholar 

  10. Anchang, B. 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).

    Article  Google Scholar 

  11. Zare, H. et al. Data reduction for spectral clustering to analyze high throughput flow cytometry data.BMC Bioinformatics11, 403 (2010).

    Article  Google Scholar 

  12. Lo, K. et al. flowClust: a Bioconductor package for automated gating of flow cytometry data.BMC Bioinformatics10, 145 (2009).

    Article  Google Scholar 

  13. Mosmann, T.R. et al. SWIFT-scalable clustering for automated identification of rare cell populations in large, high-dimensional flow cytometry datasets, part 2: biological evaluation.Cytometry A85, 422–433 (2014).

    Article  Google Scholar 

  14. Pyne, S. et al. Automated high-dimensional flow cytometric data analysis.Proc. Natl. Acad. Sci. USA106, 8519–8524 (2009).

    Article CAS  Google Scholar 

  15. Linderman, M., Qiu, P., Simonds, E. & Bjornson, Z. SPADE – An analysis and visualization tool for Flow Cytometry. R package version 1.20.http://bioconductor.org (2016).

  16. Linderman, M.D. et al. CytoSPADE: high-performance analysis and visualization of high-dimensional cytometry data.Bioinformatics28, 2400–2401 (2012).

    Article CAS  Google Scholar 

  17. Kotecha, N., Krutzik, P.O. & Irish, J.M. Web-based analysis and publication of flow cytometry experiments.Curr Protoc CytomChapter 10, 10.17 (2010).

    Google Scholar 

  18. Pettie, S. & Ramach, V. An optimal minimum spanning tree algorithm.JACM49, 49–60 (1999).

    Google Scholar 

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

    Article  Google Scholar 

  20. Zunder, E.R., Lujan, E., Goltsev, Y., Wernig, M. & Nolan, G. A continuous molecular roadmap to iPSC reprogramming through progression analysis of single-cell cytometry.Cell Stem Cell16, 323–337 (2015).

    Article CAS  Google Scholar 

  21. van der Maaten, L. & Hinton, G. Visualizing data using t-SNE.J. Mach. Learn. Res.9, 2579–2605 (2008).

    Google Scholar 

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

    Google Scholar 

  23. Treutlein, B. et al. Reconstructing lineage hierarchies of the distal lung epithelium using single-cell RNA-seq.Nature509, 371–375 (2014).

    Article CAS  Google Scholar 

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

    Article CAS  Google Scholar 

  25. Qiu, P., Gentles, A.J. & Plevritis, S.K. Discovering biological progression underlying microarray samples.PLoS Comput. Biol.7, e1001123 (2011).

    Article CAS  Google Scholar 

  26. Aghaeepour, N. et al. Critical assessment of automated flow cytometry data analysis techniques.Nat. Methods10, 228–238 (2013).

    Article CAS  Google Scholar 

  27. Van Gassen, S. et al. FlowSOM: using self-organizing maps for visualization and interpretation of cytometry data.Cytometry Part A87A, 636–645 (2015).

    Article  Google Scholar 

  28. Yu, M. et al. Hierarchical clustering in minimum spanning trees.Chaos25, 023107 (2015).

    Article  Google Scholar 

  29. Fruchterman, T.M.J. & Reingold, E.M.Graph drawing by force-directed placementSoftware: Practice & Experience.21, 1129–1164 (1991).

    Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

Authors and 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

Authors
  1. Benedict Anchang
  2. Tom D P Hart
  3. Sean C Bendall
  4. Peng Qiu
  5. Zach Bjornson
  6. Michael Linderman
  7. Garry P Nolan
  8. Sylvia K Plevritis

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.

Corresponding author

Correspondence toSylvia K Plevritis.

Ethics declarations

Competing interests

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

Integrated supplementary information

Supplementary Figure 1 SPADE analysis of normal human bone marrow trees colored by the median intensities of CD45RA for 5 different number of clusters denoted by k.

Reducing k below 100 produces a sparse tree. The trees with most cells concentrated in the branches correspond to k=100 and k=200. In general the results are not highly sensitive to the number of clusters chosen.

Supplementary Figure 2 SPADE analysis of normal human bone marrow using 5 different bootstrapping samples of the same data.

The trees are colored by all 24 subpopulations from Bendallet al. (2011)2 denoted here as subpopulations 1-24. Visual analysis of the SPADE tree branches between different runs and cluster colors will show that the branches and relative positioning of the clusters within a branch are often preserved.

Supplementary information

Combo PDF

Supplementary Figures 1 and 2 (PDF 581 kb)

Supplementary Data 1

Unlabeled subsample bone marrow data set from Bendallet al.2 used to explain the SPADE workflow inFigure 1. (ZIP 2000 kb)

Supplementary Data 2

MCM FCS file containing expression data from manually gated normal human bone marrow cells from Bendallet al.2 used for comparison analysis. MCM FCS file of ALL single-cell data from Amiret al.7 used for comparison analysis. Data in FCS file format containing the mouse lung epithelial RNA-seq expression from Treutleinet al.23. (ZIP 22244 kb)

Supplementary Data 3

MCM FCS file of ALL single-cell data from Amiret al. (2013)7 used for comparison analysis. (ZIP 18185 kb)

Supplementary Data 4

Data in FCS file format containing the Mouse lung epithelial RNA-Seq expression from Treutleinet al. (2014)23 (ZIP 4 kb)

Supplementary Software

R code for how to combine SPADE and t-SNE to generate a ‘SPADE forest’ for a single FCS file. (PDF 1511 kb)

Rights and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Anchang, B., Hart, T., Bendall, S.et al. Visualization and cellular hierarchy inference of single-cell data using SPADE.Nat Protoc11, 1264–1279 (2016). https://doi.org/10.1038/nprot.2016.066

Download citation

This article is cited by

Access through your institution
Buy or subscribe

Associated content

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

  • Peng Qiu
  • Erin F Simonds
  • Sylvia K Plevritis
Nature BiotechnologyAnalysis

Advertisement

Search

Advanced search

Quick links

Nature Briefing AI and Robotics

Sign up for theNature Briefing: AI and Robotics newsletter — what matters in AI and robotics research, free to your inbox weekly.

Get the most important science stories of the day, free in your inbox.Sign up for Nature Briefing: AI and Robotics
[8]ページ先頭
©2009-2025 Movatter.jp