ArticleGenome biology2025
Cell type-specific inference from bulk RNA-sequencing data by integrating single-cell reference profiles via EPIC-unmix.
Article in Genome biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Molecular histomorphometry and the emerging role of transcriptomics in celiac disease diagnosis and therapeutic trials.Annals of medicine · 2026Review
- Utilizing single-cell data for per-cell type eQTL mapping in the human pancreas.bioRxiv : the preprint server for biology · 2026Article
- Buwang Formula Regulates Microglial Metabolic Reprogramming and Modulates the mTOR/HIF-1α Pathway to Reduce Neuroinflammation in Diabetic Mice.Pharmaceuticals (Basel, Switzerland) · 2026Article
- Deciphering the Cellular Effects of Strontium Chloride and Potassium Carbonate on Induced Pluripotent Stem Cells and Their Derivative Cardiomyocytes.Pharmaceuticals (Basel, Switzerland) · 2026Article
- Comparative effects of platelets and plasma-derived mitochondria in early-phase Achilles tendon inflammation.Regenerative biomaterials · 2026Article
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Authors and funding
11 authors.
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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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.