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

• Authors: Chang Su, Emory University; Zichun Xu, Yale University; Xinning Shan, Yale University; Biao Cai, Yale University; Hongyu Zhao, Yale University; Jingfei Zhang, Emory University
• Journal Title: Nature Communications
• Volume: Volume 14
• Publisher: Springer Nature | 2023-08-10, Pages 4846-None
• Type of Work: Article | Final Publisher PDF
• Publisher DOI: http://doi.org/10.1038/s41467-023-40503-7
• Subjects: Biology, Bioinformatics; Biology, Genetics; Biology, Cell; Health Sciences, Immunology; Biology, Neuroscience
• Keywords: Statistical methods; Computational models; Bioinformatics; Data mining; Software

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.