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.
Authors
- Jia Wen
- Chenwei Tang (ORCID: https://orcid.org/0000-0002-1749-986X)
- Boxiang Liu (ORCID: https://orcid.org/0000-0002-2595-4463)
- Quan Sun (ORCID: https://orcid.org/0000-0001-8324-2803)
- Xiaoyu Yang (ORCID: https://orcid.org/0000-0003-2508-4805)
- Gang Li (ORCID: https://orcid.org/0000-0003-1725-6611)
- Xinyue Zeng
- Yun Li (ORCID: https://orcid.org/0000-0002-5982-9265)
- Fei Liu
- Jinying Zhao (ORCID: https://orcid.org/0000-0003-4192-5053)
- Yin Shen (ORCID: https://orcid.org/0000-0001-9901-5613)
Institutions
- University of North Carolina at Chapel Hill (US)
- Children's Hospital of Philadelphia (US)
- National University of Singapore (SG)
- University of California, San Francisco (US)
- University of Washington (US)
- University of Pennsylvania (US)
- Florida College (US)
Publication Details
- Journal
- Genome biology
- Published
- 2025-11-20
- DOI
- https://doi.org/10.1186/s13059-025-03847-5
- Primary Topic
- Single-cell and spatial transcriptomics
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Institutes of Health