PT  - JOURNAL ARTICLE
AU  - Yakir Reshef
AU  - Laurie Rumker
AU  - Joyce B. Kang
AU  - Aparna Nathan
AU  - Megan B. Murray
AU  - D. Branch Moody
AU  - Soumya Raychaudhuri
TI  - Axes of inter-sample variability among transcriptional neighborhoods reveal disease-associated cell states in single-cell data
AID  - 10.1101/2021.04.19.440534
DP  - 2021 Jan 01
TA  - bioRxiv
PG  - 2021.04.19.440534
4099  - http://biorxiv.org/content/early/2021/04/20/2021.04.19.440534.short
4100  - http://biorxiv.org/content/early/2021/04/20/2021.04.19.440534.full
AB  - 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 like clinical phenotypes. Current statistical approaches typically map cells to cell-type clusters and examine sample differences through that lens alone. Here we present covarying neighborhood analysis (CNA), an unbiased method to identify cell populations of interest with greater flexibility and granularity. CNA characterizes dominant axes of variation across samples by identifying groups of very small regions in transcriptional space—termed neighborhoods—that covary in abundance across samples, suggesting shared function or regulation. CNA can then rigorously test for associations between any sample-level attribute and the abundances of these covarying neighborhood groups. We show in simulation that CNA enables more powerful 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, redefines monocyte populations expanded in sepsis, and identifies a previously undiscovered T-cell population associated with progression to active tuberculosis.Competing Interest StatementThe authors have declared no competing interest.