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Uncovering cell identity through differential stability with Cepo

Nature Computational Science volume 1, pages 784–790 (2021)Cite this article

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A preprint version of the article is available at bioRxiv.

Abstract

The use of single-cell RNA-sequencing (scRNA-seq) allows observation of different cells at multi-tiered complexity in the same microenvironment. To get insights into cell identity using scRNA-seq data, we present Cepo, which generates cell-type-specific gene statistics of differentially stable genes from scRNA-seq data to define cell identity. When applied to multiple datasets, Cepo outperforms current methods in assigning cell identity and enhances several cell identification applications such as cell-type characterisation, spatial mapping of single cells and lineage inference of single cells.

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Fig. 1: Uncovering differentially stable genes in synthetic and experimental single-cell RNA-sequencing datasets.
Fig. 2: Retrieving CIGs to enhance interpretation of diverse single-cell applications associated with cell identity.

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Data availability

All the datasets used in this study are publicly available. The Molecular Signatures Database gene sets were downloaded from http://www.gsea-msigdb.org/gsea/msigdb/. The Tabula Muris data collection was downloaded from https://tabula-muris.ds.czbiohub.org/. The CellBench data collection was downloaded from https://github.com/LuyiTian/sc_mixology/. The Embryogenesis atlas data, which profiles 48 h of mouse embryonic development, was downloaded from https://github.com/MarioniLab/EmbryoTimecourse2018. The parsed Gastrulation data, sequenced using scNMT-seq, were downloaded from the link provided in https://github.com/rargelaguet/scnmt_gastrulation. The processed Gastrulation data were downloaded from http://www.human-gastrula.net. The hematopoietic stem cells differentiation data were downloaded from https://cytotrace.stanford.edu/. The Fetal tissue atlas data were downloaded from NCBI Gene Expression Omnibus under accession number GSE156793. The spatial embryo data were downloaded from NCBI Gene Expression Omnibus under accession number GSE120963.

Code availability

Cepo R package, source code to generate figures, and the detailed vignette including various applications such as its usage together with scRNA-seq data normalisation, batch correction and integration pipelines are available from https://github.com/PYangLab/Cepo (ref. 50).

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Acknowledgements

We thank all of our colleagues—particularly at the School of Mathematics and Statistics, The University of Sydney and Sydney Precision Bioinformatics Alliance—for their support and intellectual engagement. This work is supported by an Australian Research Council (ARC)/Discovery Early Career Researcher Award (DE170100759) and a National Health and Medical Research Council (NHMRC) Investigator Grant (1173469) to P.Y., an Australian Research Council Discovery Project grant (DP170100654) to P.Y. and J.Y.H.Y., and an Australian Research Council (ARC) Postgraduate Research Scholarship and Children’s Medical Research Institute Postgraduate Scholarship to H.J.K.

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Authors and Affiliations

  1. School of Mathematics and Statistics, The University of Sydney, Sydney, New South Wales, Australia

    Hani Jieun Kim, Kevin Wang, Yingxin Lin, Jean Y. H. Yang & Pengyi Yang

  2. Computational Systems Biology Group, Children’s Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, New South Wales, Australia

    Hani Jieun Kim, Carissa Chen & Pengyi Yang

  3. Charles Perkins Centre, The University of Sydney, Sydney, New South Wales, Australia

    Hani Jieun Kim, Carissa Chen, Yingxin Lin, Jean Y. H. Yang & Pengyi Yang

  4. Embryology Unit, Children’s Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, New South Wales, Australia

    Patrick P. L. Tam

  5. School of Medical Science, Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia

    Patrick P. L. Tam & Pengyi Yang

  6. Department of Biomedical Sciences, Cornell University, Ithaca, NY, USA

    David M. Lin

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Contributions

P.Y. and H.J.K. conceived the study with input J.Y.H.Y. and D.M.L. H.J.K. and K.W. developed the method and software with input from P.Y. H.J.K., P.Y. and K.W. performed data analyses with input from C.C. and Y.L. H.J.K., P.Y., K.W. and J.Y.H.Y. interpreted the results with input from P.P.L.T. H.J.K., P.Y. and K.W. wrote the manuscript with input from J.Y.H.Y. All of the authors read and approved the final version of the manuscript.

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Correspondence to Pengyi Yang.

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The authors declare no competing interests.

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Nature Computational Science thanks the anonymous reviewers for their contribution to the peer review of this work. Handling editor: Ananya Rastogi, in collaboration with the Nature Computational Science team.

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Kim, H.J., Wang, K., Chen, C. et al. Uncovering cell identity through differential stability with Cepo. Nat Comput Sci 1, 784–790 (2021). https://doi.org/10.1038/s43588-021-00172-2

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