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

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