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Single-cell manifold-preserving feature selection for detecting rare cell populations

Nature Computational Science volume 1, pages 374–384 (2021)Cite this article

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Abstract

A key challenge in studying organisms and diseases is to detect rare molecular programs and rare cell populations that drive development, differentiation and transformation. Molecular features, such as genes and proteins, defining rare cell populations are often unknown and are difficult to detect from unenriched single-cell data using conventional dimensionality reduction and clustering-based approaches. Here, we propose an unsupervised approach, SCMER (‘single-cell manifold-preserving feature selection’), which selects a compact set of molecular features with definitive meanings that preserve the manifold of the data. We apply SCMER in the context of hematopoiesis, lymphogenesis, tumorigenesis and drug resistance and response. We find that SCMER can identify non-redundant features that sensitively delineate both common cell lineages and rare cellular states. SCMER can be used for discovering molecular features in a high-dimensional dataset, designing targeted, cost-effective assays for clinical applications and facilitating multi-modality integration.

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Fig. 1: The SCMER approach.
Fig. 2: Results for the data of melanoma patients
Fig. 3: Results for the ileum lamina propria immunocytes data.
Fig. 4: Results for the A549 lung cancer cell line data.
Fig. 5: Results for the CITE-seq bone marrow mononuclear cell data.

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Data availability

All original datasets are accessible through the original publications34,35,36,37,38,39,40,41, including the melanoma data (GSE72056), pan-cancer cell line data (https://singlecell.broadinstitute.org/single_cell/study/SCP542), immune cell subtypes data (https://singlecell.broadinstitute.org/single_cell/study/SCP359), hematopoiesis data (GSE116256), A549 data (GSE128639), CITE-seq data (GSE128639 and GSE100866) and CyTOF data (https://cytobank.org/nolanlab/reports/Levine2015.html). Source data are provided with this paper.

Code availability

The open-source implementation of SCMER is available at https://github.com/KChen-lab/SCMER under an MIT License. Scripts for reproducing all the results are deposited in Code Ocean64.

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Acknowledgements

We thank H. Abbas, Y. Wang and L. Wang for their comments. We acknowledge the support of the High Performance Computing for Research facility at the University of Texas MD Anderson Cancer Center for providing computational resources that contributed to the research results reported in this Article. This project has been made possible in part by Human Cell Atlas Seed Network grants (nos. CZF2019-002432 and CZF2019-02425) to K.C. from the Chan Zuckerberg Initiative DAF, an advised fund of Silicon Valley Community Foundation; grants RP180248 (K.C.) and RP200520 (W.P.) from Cancer Prevention & Research Institute of Texas; grants U01CA247760 (K.C.) and U24CA211006 (L.D.) and Cancer Center Support Grant P30 CA016672 (P.P.) from the National Cancer Institute.

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Authors and Affiliations

  1. Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

    Shaoheng Liang, Vakul Mohanty, Jinzhuang Dou, Qi Miao, Yuefan Huang & Ken Chen

  2. Department of Computer Science, Rice University, Houston, TX, USA

    Shaoheng Liang

  3. Department of Biostatistics & Data Science, School of Public Health, The University of Texas Health Science Center at Houston (UTHealth), Houston, TX, USA

    Qi Miao & Yuefan Huang

  4. Department of Leukemia, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

    Muharrem Müftüoğlu

  5. Department of Medicine, Washington University School of Medicine, St Louis, MO, USA

    Li Ding

  6. Department of Biology and Biochemistry, University of Houston, Houston, TX, USA

    Weiyi Peng

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Contributions

S.L., M.M., W.P., L.D. and K.C. conceptualized the project. S.L. designed the SCMER algorithm and implemented the software. All authors collectively designed the experiments and analyzed the results. All authors drafted the manuscript. All authors have read and approved the manuscript.

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Correspondence to Ken Chen.

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The authors declare no competing interests.

Additional information

Peer review information Nature Computational Science thanks Ting Chen, Kevin Menden and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Jie Pan was the primary editor 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 Notes 1–9, Results 1–4, Tables 1–11 and Figs. 1–21.

Supplementary Data 1

Human phenotype pathways enriched in novel markers found for melanoma.

Supplementary Data 2

Biological process pathways enriched in novel markers found for melanoma.

Supplementary Data 3

Markers for 198 pan-cancer cell lines.

Supplementary Data 4

Biological process pathways enriched in novel markers found for Crohn’s disease immune cells.

Supplementary Data 5

Biological process pathways enriched in novel markers found for hematopoietic cells.

Supplementary Data 6

Pathways for novel markers found for hematopoietic cells.

Supplementary Data 7

Biological processes pathways enriched in markers uncorrelated with NR3C1 TF.

Source data

Source Data Fig. 1

UMAP embedding and feature values.

Source Data Fig. 2

UMAP embedding, cell labels, gene expression, method comparison results and survival analysis data.

Source Data Fig. 3

UMAP embedding, cell labels, gene expression and method comparison results.

Source Data Fig. 4

UMAP embedding, cell labels, gene expression and ATAC peak levels.

Source Data Fig. 5

UMAP embedding, cell labels, gene expression and protein levels.

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Liang, S., Mohanty, V., Dou, J. et al. Single-cell manifold-preserving feature selection for detecting rare cell populations. Nat Comput Sci 1, 374–384 (2021). https://doi.org/10.1038/s43588-021-00070-7

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