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

ABSTRACT: 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.

SUBMITTER: Reshef YA

PROVIDER: S-EPMC8930733 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature

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

Reshef Yakir A YA Rumker Laurie L Kang Joyce B JB Nathan Aparna A Korsunsky Ilya I Asgari Samira S Murray Megan B MB Moody D Branch DB Raychaudhuri Soumya S

Nature biotechnology 20211021 3

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 ...[more]

PMID: 34675423

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Project description:AimsGenome-wide association studies (GWASs) have discovered hundreds of common genetic variants for atherosclerotic disease and cardiovascular risk factors. The translation of susceptibility loci into biological mechanisms and targets for drug discovery remains challenging. Intersecting genetic and gene expression data has led to the identification of candidate genes. However, previously studied tissues are often non-diseased and heterogeneous in cell composition, hindering accurate candidate prioritization. Therefore, we analysed single-cell transcriptomics from atherosclerotic plaques for cell-type-specific expression to identify atherosclerosis-associated candidate gene-cell pairs.Methods and resultsWe applied gene-based analyses using GWAS summary statistics from 46 atherosclerotic and cardiovascular disease, risk factors, and other traits. We then intersected these candidates with single-cell RNA sequencing (scRNA-seq) data to identify genes specific for individual cell (sub)populations in atherosclerotic plaques. The coronary artery disease (CAD) loci demonstrated a prominent signal in plaque smooth muscle cells (SMCs) (SKI, KANK2, and SORT1) P-adj. = 0.0012, and endothelial cells (ECs) (SLC44A1, ATP2B1) P-adj. = 0.0011. Finally, we used liver-derived scRNA-seq data and showed hepatocyte-specific enrichment of genes involved in serum lipid levels.ConclusionWe discovered novel and known gene-cell pairs pointing to new biological mechanisms of atherosclerotic disease. We highlight that loci associated with CAD reveal prominent association levels in mainly plaque SMC and EC populations. We present an intuitive single-cell transcriptomics-driven workflow rooted in human large-scale genetic studies to identify putative candidate genes and affected cells associated with cardiovascular traits. Collectively, our workflow allows for the identification of cell-specific targets relevant for atherosclerosis and can be universally applied to other complex genetic diseases and traits.

Dataset Information

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

Publications

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

Similar Datasets

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