Abstract
When assessed over a large number of samples, bulk RNA sequencing provides reliable data for gene expression at the tissue level. Single-cell RNA sequencing (scRNA-seq) deepens those analyses by evaluating gene expression at the cellular level. Both data types lend insights into disease etiology. With current technologies, scRNA-seq data are known to be noisy. Constrained by costs, scRNA-seq data are typically generated from a relatively small number of subjects, which limits their utility for some analyses, such as identification of gene expression quantitative trait loci (eQTLs). To address these issues while maintaining the unique advantages of each data type, we develop a Bayesian method (bMIND) to integrate bulk and scRNA-seq data. With a prior derived from scRNA-seq data, we propose to estimate sample-level cell type–specific (CTS) expression from bulk expression data. The CTS expression enables large-scale sample-level downstream analyses, such as detection of CTS differentially expressed genes (DEGs) and eQTLs. Through simulations, we show that bMIND improves the accuracy of sample-level CTS expression estimates and increases the power to discover CTS DEGs when compared to existing methods. To further our understanding of two complex phenotypes, autism spectrum disorder and Alzheimer's disease, we apply bMIND to gene expression data of relevant brain tissue to identify CTS DEGs. Our results complement findings for CTS DEGs obtained from snRNA-seq studies, replicating certain DEGs in specific cell types while nominating other novel genes for those cell types. Finally, we calculate CTS eQTLs for 11 brain regions by analyzing Genotype-Tissue Expression Project data, creating a new resource for biological insights.
Funder
National Institute on Aging
National Institute of Neurological Disorders and Stroke
NINDS
Arizona Department of Health Services
NIA
National Institutes of Health
National Cancer Institute
NCI
National Human Genome Research Institute
NHGRI
National Heart, Lung, and Blood Institute
NHLBI
National Institute on Drug Abuse
NIDA
National Institute of Mental Health
NIMH
NINDS.
NCI\Leidos Biomedical Research, Inc.
University of Miami
University of North Carolina—Chapel Hill
North Carolina State University
Harvard University
Stanford University
Washington University
University of Pennsylvania
Simons Foundation Autism Research Initiative
Publisher
Cold Spring Harbor Laboratory
Subject
Genetics(clinical),Genetics
Cited by
34 articles.
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