{"doi":"10.1101/2024.05.23.595514","title":"Cell-type specific inference from bulk RNA-sequencing data by integrating single cell reference profiles via EPIC-unmix","abstract":"Cell type specific (CTS) analysis is essential to reveal biological insights obscured in bulk tissue data. However, single-cell (sc) or single-nuclei (sn) resolution data are still cost-prohibitive for large-scale samples. Thus, computational methods to perform deconvolution from bulk tissue data are highly valuable. We here present EPIC-unmix, a novel two-step empirical Bayesian method integrating reference sc/sn RNA-seq data and bulk RNA-seq data from target samples to enhance the accuracy of CTS inference. We demonstrate through comprehensive simulations across three tissues that EPIC-unmix achieved 4.6% - 109.8% higher accuracy compared to alternative methods. By applying EPIC-unmix to human bulk brain RNA-seq data from the ROSMAP and MSBB cohorts, we identified multiple genes differentially expressed between Alzheimer's disease (AD) cases versus controls in a CTS manner, including 57.4% novel genes not identified using similar sample size sc/snRNA-seq data, indicating the power of our in-silico approach. Among the 6-69% overlapping, 83%-100% are in consistent direction with those from sc/snRNA-seq data, supporting the reliability of our findings. EPIC-unmix inferred CTS expression profiles similarly empowers CTS eQTL analysis. Among the novel eQTLs, we highlight a microglia eQTL for AD risk gene AP3B2, obscured in bulk and missed by sc/snRNA-seq based eQTL analysis. The variant resides in a microglia-specific cCRE, forming chromatin loop with AP3B2 promoter region in microglia. Taken together, we believe EPIC-unmix will be a valuable tool to enable more powerful CTS analysis","journal":"bioRxiv (Cold Spring Harbor Laboratory)","year":2024,"id":497898,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9501,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2024-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":11263,"name":"Quan Sun","orcid":"0000-0001-8324-2803","position":1,"is_corresponding":false},{"id":1344859,"name":"Xinyue Zeng","orcid":null,"position":2,"is_corresponding":false},{"id":1021665,"name":"Xiaoyu Yang","orcid":"0009-0001-0035-9289","position":3,"is_corresponding":false},{"id":648391,"name":"Fei Liu","orcid":"0009-0005-0576-440X","position":4,"is_corresponding":false},{"id":564457,"name":"Jinying Zhao","orcid":"0000-0003-3243-2660","position":5,"is_corresponding":false},{"id":29911,"name":"Yin Shen","orcid":"0000-0001-9901-5613","position":6,"is_corresponding":false},{"id":230575,"name":"Boxiang Liu","orcid":"0000-0002-2595-4463","position":7,"is_corresponding":false},{"id":177741,"name":"Jia Wen","orcid":null,"position":8,"is_corresponding":false},{"id":24805,"name":"Yun Li","orcid":"0000-0002-9275-4189","position":10,"is_corresponding":false},{"id":1344621,"name":"Chenwei Tang","orcid":"0000-0002-1749-986X","position":0,"is_corresponding":true}],"reference_count":62,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-19T02:09:38.543544Z","pmid":"38826297","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}