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

Chenwei Tang; Quan Sun; Xinyue Zeng; Gang Li; Xiaoyu Yang; Fei Liu; Jinying Zhao; Yin Shen; Boxiang Liu; Jia Wen; Yun Li

doi:10.1186/s13059-025-03847-5

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

Genome Biol. 2025 Nov 20;26(1):395. doi: 10.1186/s13059-025-03847-5.

Authors

Chenwei Tang^#¹, Quan Sun^#^{1

2

3}, Xinyue Zeng¹, Gang Li^{4

5}, Xiaoyu Yang⁶, Fei Liu⁷, Jinying Zhao⁸, Yin Shen⁶, Boxiang Liu^{7

9}, Jia Wen¹⁰, Yun Li^{11

12}

Affiliations

¹ Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
² Center for Computational and Genomic Medicine and Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, PA, USA.
³ Department of Pathology and Laboratory Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
⁴ Department of Genome Sciences, University of Washington, Seattle, USA, WA.
⁵ eScience Institute, University of Washington, WRF Data Science Studio, University of Washington, Seattle, WA, USA.
⁶ Institute for Human Genetics, University of California, San Francisco, CA, USA.
⁷ Department of Pharmacy and Pharmaceutical Sciences, Faculty of Science, National University of Singapore, Singapore, Singapore.
⁸ Department of Epidemiology, College of Public Health & Health Professions and College of Medicine, University of Florida, Center for Genetic Epidemiology and Bioinformatics, Gainesville, FL, USA.
⁹ Department of Biomedical Informatics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
¹⁰ Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. jia_wen@med.unc.edu.
¹¹ Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. yun_li@med.unc.edu.
¹² Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. yun_li@med.unc.edu.

^# Contributed equally.

Abstract

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.

Supplementary Information: The online version contains supplementary material available at 10.1186/s13059-025-03847-5.

Abstract

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