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TooManyCells identifies and visualizes relationships of single-cell clades

Nature Methods volume 17, pages 405–413 (2020)Cite this article

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Abstract

Identifying and visualizing transcriptionally similar cells is instrumental for accurate exploration of the cellular diversity revealed by single-cell transcriptomics. However, widely used clustering and visualization algorithms produce a fixed number of cell clusters. A fixed clustering ‘resolution’ hampers our ability to identify and visualize echelons of cell states. We developed TooManyCells, a suite of graph-based algorithms for efficient and unbiased identification and visualization of cell clades. TooManyCells introduces a visualization model built on a concept intentionally orthogonal to dimensionality-reduction methods. TooManyCells is also equipped with an efficient matrix-free divisive hierarchical spectral clustering different from prevalent single-resolution clustering methods. TooManyCells enables multiresolution and multifaceted exploration of single-cell clades. An advantage of this paradigm is the immediate detection of rare and common populations that outperforms popular clustering and visualization algorithms, as demonstrated using existing single-cell transcriptomic data sets and new data modeling drug-resistance acquisition in leukemic T cells.

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Fig. 1: The TooManyCells visualization and clustering algorithms.
Fig. 2: Example of TooManyCells visualization capabilities using 11 mouse organs.
Fig. 3: Comparative analysis of clustering performance and scalability.
Fig. 4: Detection of cells from two rare populations mixed with a common population was benchmarked for widely used clustering algorithms.
Fig. 5: TooManyCells stratifies rare plasmablasts in mouse spleen.
Fig. 6: TooManyCells identifies GSI-resistant cell heterogeneity and detects resistant-like T-ALL cells.

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Data availability

The accession number for the new data sets reported in this paper is Gene Expression Omnibus: GSE138892. Microfluidics single-cell RNA-seq count data from 11 organs in 3 female and 4 male, C57BL/6 NIA, 3-month-old mice were obtained from https://figshare.com/articles/_/5715025, removing P8 libraries due to outlier cell counts22. FACS-purified CD14+ monocytes, CD19+ B and CD4+ T cells were obtained from https://support.10xgenomics.com/single-cell-gene-expression/datasets (ref. 23). Data for seven cancer-cell lines were obtained from GSE81861 (ref. 17). FACS-purified B lymphocytes/natural killer, megakaryocyte-erythroid, and granulocyte-monocyte progenitors were obtained from GSE117498 (ref. 25).

Code availability

TooManyCells is available at https://github.com/faryabib/too-many-cells or as a Docker image https://cloud.docker.com/repository/docker/gregoryschwartz/too-many-cells/. An R wrapper for TooManyCells is available at https://cran.r-project.org/web/packages/TooManyCellsR. BirchBeer is available at https://github.com/faryabib/birch-beer or as a Docker image https://cloud.docker.com/repository/docker/gregoryschwartz/birch-beer. Codes necessary to reproduce the presented analyses are available at https://github.com/faryabib/NatMethods_TooManyCells_analysis.

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Acknowledgements

This work was supported by T32-CA-009140 (to G.W.S.), LLS-5456-17 (to J.P.), R01-CA-215518 (to W.S.P.), R01-HL-145754, the Penn Epigenetics pilot award and the Sloan Foundation Grant (to G.V.), Therapeutics Translational Medicine and Therapeutics program for Transdisciplinary Awards Program in Translational Medicine and Therapeutics, Concern Foundation’s The Conquer Cancer Now Award, Susan G. Komen CCR185472448 and R01-CA-230800 (to R.B.F.).

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Authors and Affiliations

  1. Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Gregory W. Schwartz, Yeqiao Zhou, Jelena Petrovic, Lanwei Xu, Sydney M. Shaffer, Warren S. Pear & Robert B. Faryabi

  2. Abramson Family Cancer Research Institute Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Gregory W. Schwartz, Yeqiao Zhou, Jelena Petrovic, Lanwei Xu, Sydney M. Shaffer, Warren S. Pear & Robert B. Faryabi

  3. Department of Genetics, University of Pennsylvania, Philadelphia, PA, USA

    Maria Fasolino & Golnaz Vahedi

  4. Penn Epigenetics Institute, University of Pennsylvania, Philadelphia, PA, USA

    Maria Fasolino & Golnaz Vahedi

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Contributions

Conceptualization: R.B.F., G.W.S.; Methodology: G.W.S., R.B.F.; Software: G.W.S.; Investigation: G.W.S., R.B.F., J.P., Y.Z.; Formal Analysis: G.W.S., R.B.F., J.P., M.F., S.M.S., L.X., Y.Z.; Resources and Reagents: R.B.F., G.V.; Writing, Review and Editing: G.W.S., R.B.F., W.S.P., J.P., Y.Z.; Writing, Original Draft: G.W.S., R.B.F.; Supervision: R.B.F.; Funding Acquisition: R.B.F.

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Correspondence to Robert B. Faryabi.

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Peer review information Nicole Rusk and Lin Tang were the primary editors on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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Supplementary information

Supplementary Information

Supplementary Figures 1–33 and Notes 1–6

Reporting Summary

Supplementary Table 1

Differential expression analysis between the ascending (n = 1,780 cells) and sustained (n = 2,299 cells) subtrees in Supplementary Fig. 33e. QL F-test used with Benjamin–Hochberg method for multiple-hypothesis correction.

Supplementary Table 2

Differential expression analysis between untreated (n =2,338 cells) and short-term (n = 2,616 cells) populations in Supplementary Fig. 33i. QL F-test used with Benjamini–Hochberg method for multiple-hypothesis correction.

Supplementary Table 3

Differential expression analysis between parental (n =4,954 cells) and sustained (n = 2,417 cells) populations in Supplementary Fig. 33j. QL F-test used with Benjamini–Hochberg method for multiple-hypothesis correction.

Supplementary Table 4

Differential expression analysis between other parental (n = 4,926 cells) and resistant-like (n = 28 cells) populations in Supplementary Fig. 33k. QL F-test used with Benjamini–Hochberg method for multiple-hypothesis correction.

Supplementary Table 5

Differential expression analysis between sustained (n = 2,417 cells) and resistant-like (n = 28 cells) populations in Supplementary Fig. 33l. QL F-test used with Benjamini–Hochberg method for multiple-hypothesis correction.

Supplementary Table 6

Sequences of MYC and GAPDH RNA FISH probes.

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Schwartz, G.W., Zhou, Y., Petrovic, J. et al. TooManyCells identifies and visualizes relationships of single-cell clades. Nat Methods 17, 405–413 (2020). https://doi.org/10.1038/s41592-020-0748-5

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