Cell-type-specific co-expression inference from single cell RNA-sequencing data

Chang Su, Zichun Xu, Xinning Shan, Biao Cai, Hongyu Zhao () and Jingfei Zhang ()
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Chang Su: Yale University
Zichun Xu: Yale University
Xinning Shan: Yale University
Biao Cai: Yale University
Hongyu Zhao: Yale University
Jingfei Zhang: Emory University

Nature Communications, 2023, vol. 14, issue 1, 1-12

Abstract: Abstract The advancement of single cell RNA-sequencing (scRNA-seq) technology has enabled the direct inference of co-expressions in specific cell types, facilitating our understanding of cell-type-specific biological functions. For this task, the high sequencing depth variations and measurement errors in scRNA-seq data present two significant challenges, and they have not been adequately addressed by existing methods. We propose a statistical approach, CS-CORE, for estimating and testing cell-type-specific co-expressions, that explicitly models sequencing depth variations and measurement errors in scRNA-seq data. Systematic evaluations show that most existing methods suffered from inflated false positives as well as biased co-expression estimates and clustering analysis, whereas CS-CORE gave accurate estimates in these experiments. When applied to scRNA-seq data from postmortem brain samples from Alzheimer’s disease patients/controls and blood samples from COVID-19 patients/controls, CS-CORE identified cell-type-specific co-expressions and differential co-expressions that were more reproducible and/or more enriched for relevant biological pathways than those inferred from existing methods.

Date: 2023
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DOI: 10.1038/s41467-023-40503-7

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