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Uncovering axes of variation among single-cell cancer specimens


Chen, William S; Zivanovic, Nevena; van Dijk, David; Wolf, Guy; Bodenmiller, Bernd; Krishnaswamy, Smita (2020). Uncovering axes of variation among single-cell cancer specimens. Nature Methods, 17(3):302-310.

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.

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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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:07 Faculty of Science > Department of Quantitative Biomedicine
Dewey Decimal Classification:610 Medicine & health
Scopus Subject Areas:Life Sciences > Biotechnology
Life Sciences > Biochemistry
Life Sciences > Molecular Biology
Life Sciences > Cell Biology
Uncontrolled Keywords:Biotechnology, Cell Biology, Biochemistry, Molecular Biology
Language:English
Date:1 March 2020
Deposited On:02 Feb 2021 12:57
Last Modified:01 Apr 2021 00:01
Publisher:Nature Publishing Group
ISSN:1548-7091
OA Status:Green
Free access at:PubMed ID. An embargo period may apply.
Publisher DOI:https://doi.org/10.1038/s41592-019-0689-z
PubMed ID:31932777

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