Cell-type-specific co-expression inference from single cell RNA-sequencing data
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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Aug 10, 2023
- Source ID
- 10.1038/s41467-023-40503-7
Entities
People
- Biao Cai
- Chang Su
- Hongyu Zhao
- Jingfei Zhang
- Xinning Shan
- Zichun Xu
Organizations
- National Institute of General Medical Sciences
- National Institute on Aging
- National Science Foundation
- United States Army Medical Command
- United States Department of Health and Human Services