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Uncovering cell identity through differential stability with Cepo

A preprint version of the article is available at bioRxiv.


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.

Data availability

All the datasets used in this study are publicly available. The Molecular Signatures Database gene sets were downloaded from The Tabula Muris data collection was downloaded from The CellBench data collection was downloaded from The Embryogenesis atlas data, which profiles 48 h of mouse embryonic development, was downloaded from The parsed Gastrulation data, sequenced using scNMT-seq, were downloaded from the link provided in The processed Gastrulation data were downloaded from The hematopoietic stem cells differentiation data were downloaded from 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 (ref. 50).


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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



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.

Corresponding author

Correspondence to Pengyi Yang.

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The authors declare no competing interests.

Peer review information

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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Supplementary information

Supplementary Information

Supplementary Figs. 1–21.

Reporting Summary

Source data

Source Data Fig. 1

Statistical Source Data for Fig. 1

Source Data Fig. 2

Statistical Source Data for Fig. 2

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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).

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