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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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.
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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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.).
S.K. is on the scientific advisory board of AI Therapeutics.
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 Figs. 1–8 and Notes 1–7.
List of inhibitors included in EMT drug-screen experiment
List of antibodies included in EMT drug-screen experiment
Clusters of inhibitors with similar effects in multiple-batch EMT drug-screen experiment
Cell yield of each experimental condition in EMT drug-screen experiment
Subgroups of inhibitors with similar effects in single-batch EMT drug-screen experiment
Subgroups of biospecimens with similar single-cell profiles in melanoma scRNA-seq expeiment
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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