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Uncovering axes of variation among single-cell cancer specimens

Nature Methods volume 17, pages 302–310 (2020)Cite this article

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Abstract

While several tools have been developed to map axes of variation among individual cells, no analogous approaches exist for identifying axes of variation among multicellular biospecimens profiled at single-cell resolution. For this purpose, we developed ‘phenotypic earth mover’s distance’ (PhEMD). PhEMD is a general method for embedding a ‘manifold of manifolds’, in which each datapoint in the higher-level manifold (of biospecimens) represents a collection of points that span a lower-level manifold (of cells). We apply PhEMD to a newly generated drug-screen dataset and demonstrate that PhEMD uncovers axes of cell subpopulational variation among a large set of perturbation conditions. Moreover, we show that PhEMD can be used to infer the phenotypes of biospecimens not directly profiled. Applied to clinical datasets, PhEMD generates a map of the patient-state space that highlights sources of patient-to-patient variation. PhEMD is scalable, compatible with leading batch-effect correction techniques and generalizable to multiple experimental designs.

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Fig. 1: The PhEMD approach.
Fig. 2: Experimental design for measuring perturbation effects of small-molecule inhibitors on EMT.
Fig. 3: Axes of variation among EMT perturbation conditions.
Fig. 4: Nyström extension predicts single-cell profiles of unmeasured EMT perturbation conditions.
Fig. 5: PhEMD applied to single-cell RNA-seq data of 17 melanoma samples (nontumor cells only) highlights heterogeneous immune profiles among different patients.
Fig. 6: PhEMD applied to mass cytometry data of 75 ccRCC samples gated for T cells.

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Data availability

The mass cytometry data that support the findings of this study are available at https://community.cytobank.org/cytobank/projects/1296. Source data for Figs. 3–6 are provided with the paper. Any additional data supporting the findings of this study are available from the corresponding author upon request.

Code availability

PhEMD takes as input a list of \(N\) matrices representing \(N\) single-cell specimens. An R implementation of PhEMD is publicly available as a Bioconductor R package (package name: ‘phemd’) and can alternatively be downloaded from https://github.com/wschen/phemd. Note that the cell-state space for all analyses presented in this manuscript was modeled using the PHATE method8. However, alternative approaches are viable and we have provided support for PHATE, Monocle2 (ref. 41) and Louvain community detection (as implemented in the Seurat software package)16 for this purpose in the R package.

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Acknowledgements

We thank the Krishnaswamy and Bodenmiller laboratories for fruitful discussions. This study was supported in part by the Chan–Zuckerberg Initiative Seed Networks for the Human Cell Atlas (S.K.), a Swiss National Science Foundation (SNSF) R’Equip grant (B.B.), a SNSF Assistant Professorship grant no. PP00P3-144874 (B.B.), the SystemsX Transfer Project ‘Friends and Foes’ (B.B.), the SystemX grants Metastasix and PhosphoNEtX (B.B.), the European Research Council (ERC) under the European Union’s Seventh Framework Program (no. FP/2007-2013)/ERC Grant Agreement no. 336921 (B.B.), the CRUK IMAXT Grand Challenge (B.B.) and the following National Institutes of Health (NIH) grants: nos. R01GM135929 (S.K. and G.W.), UC4 DK108132 (B.B.) and NIH–NIDDK T35DK104689 (W.S.C.).

Author information

Author notes

  1. These authors supervised this work: Bernd Bodenmiller, Smita Krishnaswamy.

  2. These authors contributed equally: William S. Chen, Nevena Zivanovic.

Authors and Affiliations

  1. Department of Genetics, Yale School of Medicine, New Haven, CT, USA

    William S. Chen, David van Dijk & Smita Krishnaswamy

  2. Institute of Molecular Life Sciences, University of Zurich, Zurich, Switzerland

    Nevena Zivanovic & Bernd Bodenmiller

  3. Department of Computer Science, Yale University, New Haven, CT, USA

    David van Dijk & Smita Krishnaswamy

  4. Department of Mathematics and Statistics, Université de Montréal, Montreal, Quebec, Canada

    Guy Wolf

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Contributions

W.S.C., N.Z., G.W., B.B. and S.K. conceived the study. W.S.C. and S.K. developed the PhEMD algorithm. W.S.C. wrote the software implementation. W.S.C. and D.v.D. performed all computational analyses. N.Z. performed all single-cell profiling experiments and data quality assessments. W.S.C., N.Z., B.B. and S.K. interpreted the results and drafted the manuscript.

Corresponding authors

Correspondence to Bernd Bodenmiller or Smita Krishnaswamy.

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Competing interests

S.K. is on the scientific advisory board of AI Therapeutics.

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Peer review information Rita Strack was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Figs. 1–8 and Notes 1–7.

Reporting Summary

Supplementary Table 1

List of inhibitors included in EMT drug-screen experiment

Supplementary Table 2

List of antibodies included in EMT drug-screen experiment

Supplementary Table 3

Clusters of inhibitors with similar effects in multiple-batch EMT drug-screen experiment

Supplementary Table 4

Cell yield of each experimental condition in EMT drug-screen experiment

Supplementary Table 5

Subgroups of inhibitors with similar effects in single-batch EMT drug-screen experiment

Supplementary Table 6

Subgroups of biospecimens with similar single-cell profiles in melanoma scRNA-seq expeiment

Supplementary Table 7

Subgroups of biospecimens with similar single-cell profiles in ccRCC mass cytometry expeiment

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Chen, W.S., Zivanovic, N., van Dijk, D. et al. Uncovering axes of variation among single-cell cancer specimens. Nat Methods 17, 302–310 (2020). https://doi.org/10.1038/s41592-019-0689-z

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