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Co-varying neighborhood analysis identifies cell populations associated with phenotypes of interest from single-cell transcriptomics


As single-cell datasets grow in sample size, there is a critical need to characterize cell states that vary across samples and associate with sample attributes, such as clinical phenotypes. Current statistical approaches typically map cells to clusters and then assess differences in cluster abundance. Here we present co-varying neighborhood analysis (CNA), an unbiased method to identify associated cell populations with greater flexibility than cluster-based approaches. CNA characterizes dominant axes of variation across samples by identifying groups of small regions in transcriptional space—termed neighborhoods—that co-vary in abundance across samples, suggesting shared function or regulation. CNA performs statistical testing for associations between any sample-level attribute and the abundances of these co-varying neighborhood groups. Simulations show that CNA enables more sensitive and accurate identification of disease-associated cell states than a cluster-based approach. When applied to published datasets, CNA captures a Notch activation signature in rheumatoid arthritis, identifies monocyte populations expanded in sepsis and identifies a novel T cell population associated with progression to active tuberculosis.

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Fig. 1: Method schematic.
Fig. 2: Power and signal recovery assessed in simulation.
Fig. 3: CNA captures Notch activation gradient in RA dataset.
Fig. 4: CNA refines sepsis-associated blood cell populations.
Fig. 5: CNA characterizes biologically meaningful structure in TB dataset.
Fig. 6: CNA improves characterization of diverse sample attributes in a TB cohort.

Data availability

All data analyzed during this study are available in three previously published articles6,7,8. Source data are provided with this paper.

Code availability

An open-source repository containing code for running CNA is available at; an open-source repository containing code underlying all figures and tables is available at; and an open-source repository containing code underlying all simulations is available at Source data are provided with this paper.


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We thank A. Gupta, D. Kotliar, Y. Luo, N. Millard, M. Reyes, S. Sakaue, F. Zhang, the members of the CGTA discussion group and the Raychaudhuri lab for helpful discussions and feedback. This work is supported, in part, by funding from the National Institutes of Health (NIH) including UH2AR067677, U19 AI111224, U01 HG009379 and 1R01AR063759. S.A. was supported by the Swiss National Science Foundation postdoctoral mobility fellowships P2ELP3_172101 and P400PB_183823 and NIH grant T32HG010464. L.R. was supported, in part, by NIH 5T32HG2295-17. J.K. was supported by NIH grant T32GM007753. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute of General Medical Sciences or the National Institutes of Health.

Author information




Y.A.R., L.R. and S.R. designed and conceptualized the study. Y.A.R. and L.R. designed and implemented the algorithm and performed simulations. Y.A.R., L.R. and J.K. performed analysis of real data. A.N., S.A. and I.K. provided input on methodologic design and real data analysis. D.B.M., M.M., A.N. and I.K. provided dataset-specific expertise. Y.A.R., L.R. and S.R. wrote the manuscript with input from the remaining authors.

Corresponding author

Correspondence to Soumya Raychaudhuri.

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Competing interests

S.R. serves as a consultant for Gilead, Pfizer, Janssen and Rheos Medicines and is a founder of Mestag Therapeutics. I.K. serves as a consultant for Mestag Therapeutics. The other authors declare no competing interests.

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Peer review information Nature Biotechnology thanks the anonymous reviewers for their contribution to the peer review of this work.

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Supplementary information

Supplementary Information

Supplementary Tables 1–20 and Supplementary Figs. 1–13

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Source Data Fig. 3

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Reshef, Y.A., Rumker, L., Kang, J.B. et al. Co-varying neighborhood analysis identifies cell populations associated with phenotypes of interest from single-cell transcriptomics. Nat Biotechnol (2021).

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