Journal
NATURE BIOTECHNOLOGY
Volume 40, Issue 3, Pages 355-+Publisher
NATURE PORTFOLIO
DOI: 10.1038/s41587-021-01066-4
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Funding
- National Institutes of Health (NIH) [UH2AR067677, U19 AI111224, U01 HG009379, 1R01AR063759]
- Swiss National Science Foundation postdoctoral mobility fellowships [P2ELP3_172101, P400PB_183823]
- NIH [T32GM007753]
- Swiss National Science Foundation (SNF) [P400PB_183823, P2ELP3_172101] Funding Source: Swiss National Science Foundation (SNF)
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CNA is an unbiased method that can identify associated cell populations with greater flexibility than cluster-based approaches, by characterizing small regions in transcriptional space that co-vary in abundance across samples. It enables more sensitive and accurate identification of disease-associated cell states than a cluster-based approach.
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. Inter-sample variability reveals disease-associated cell subpopulations in single-cell RNA sequencing.
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