Cell type-specific inference from bulk RNA-sequencing data by integrating single-cell reference profiles via EPIC-unmix.

Cell type-specific analysis is crucial for uncovering biological insights hidden in bulk tissue data, yet single-cell or single-nuclei approaches are often cost-prohibitive for large samples. We introduce EPIC-unmix, a novel two-step empirical Bayesian method combining reference single-cell/single-nuclei and bulk RNA-seq data to improve cell type-specific inference, accounting for the difference between reference and target datasets. Under comprehensive simulations, we demonstrate that EPIC-unmix outperforms alternative methods in accuracy. Applied to Alzheimer’s disease brain RNA-seq data, EPIC-unmix identifies multiple differentially expressed genes in a cell type-specific manner, and empowers cell type-specific eQTL analysis.