Bayesian estimation of cell-type-specific gene expression per bulk sample with prior derived from single-cell data

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, however, scRNA-seq data are known to be noisy. Moreover, 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 detecting CTS differentially expressed genes (DEGs) and eQTLs. Through simulations, we demonstrate that bMIND improves the accuracy of sample-level CTS expression estimates and power to discover CTS-DEGs when compared to existing methods. To further our understanding of two complex phenotypes, autism spectrum disorder and Alzheimers 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 in those cell types. Finally, we calculate CTS-eQTLs for eleven brain regions by analyzing GTEx V8 data, creating a new resource for biological insights.