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

Nature Protocols volume 11, pages 1264–1279 (2016)Cite this article

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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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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 Treutlein et al.23.
Figure 9: SPADE analysis using different random seeds.
Figure 10: Comparison of SPADE analyses performed using improper and proper lineage markers.

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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.

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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

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  1. Benedict Anchang
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  4. Peng Qiu
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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.

Corresponding author

Correspondence to Sylvia K Plevritis.

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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 Bendall et 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 Bendall et al.2 used to explain the SPADE workflow in Figure 1. (ZIP 2000 kb)

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. (ZIP 22244 kb)

Supplementary Data 3

MCM FCS file of ALL single-cell data from Amir et 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 Treutlein et 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)

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Anchang, B., Hart, T., Bendall, S. et al. Visualization and cellular hierarchy inference of single-cell data using SPADE. Nat Protoc 11, 1264–1279 (2016). https://doi.org/10.1038/nprot.2016.066

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