{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T11:11:04Z","timestamp":1769944264685,"version":"3.49.0"},"reference-count":40,"publisher":"Oxford University Press (OUP)","issue":"11","license":[{"start":{"date-parts":[[2022,4,19]],"date-time":"2022-04-19T00:00:00Z","timestamp":1650326400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"name":"University of Pittsburgh Brain Institute"},{"name":"University of Pittsburgh Medical Center Competitive Medical Research Fund"},{"name":"National Institutes of Health\u2019s","award":["UL1TR001857"],"award-info":[{"award-number":["UL1TR001857"]}]},{"name":"National Institutes of Health\u2019s","award":["R01HL117191"],"award-info":[{"award-number":["R01HL117191"]}]},{"name":"National Institutes of Health\u2019s","award":["R21HL150431"],"award-info":[{"award-number":["R21HL150431"]}]},{"name":"National Institutes of Health\u2019s","award":["R37MH057881"],"award-info":[{"award-number":["R37MH057881"]}]},{"name":"National Institutes of Health\u2019s","award":["R37MH057881-22S1"],"award-info":[{"award-number":["R37MH057881-22S1"]}]},{"name":"National Institutes of Health\u2019s","award":["R01MH123184"],"award-info":[{"award-number":["R01MH123184"]}]},{"name":"University of Pittsburgh Center for Research Computing through the resources provided"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,5,26]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Motivation<\/jats:title><jats:p>Tissue-level omics data such as transcriptomics and epigenomics are an average across diverse cell types. To extract cell-type-specific (CTS) signals, dozens of cellular deconvolution methods have been proposed to infer cell-type fractions from tissue-level data. However, these methods produce vastly different results under various real data settings. Simulation-based benchmarking studies showed no universally best deconvolution approaches. There have been attempts of ensemble methods, but they only aggregate multiple single-cell references or reference-free deconvolution methods.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>To achieve a robust estimation of cellular fractions, we proposed EnsDeconv (Ensemble Deconvolution), which adopts CTS robust regression to synthesize the results from 11 single deconvolution methods, 10 reference datasets, 5 marker gene selection procedures, 5 data normalizations and 2 transformations. Unlike most benchmarking studies based on simulations, we compiled four large real datasets of 4937 tissue samples in total with measured cellular fractions and bulk gene expression from different tissues. Comprehensive evaluations demonstrated that EnsDeconv yields more stable, robust and accurate fractions than existing methods. We illustrated that EnsDeconv estimated cellular fractions enable various CTS downstream analyses such as differential fractions associated with clinical variables. We further extended EnsDeconv to analyze bulk DNA methylation data.<\/jats:p><\/jats:sec><jats:sec><jats:title>Availability and implementation<\/jats:title><jats:p>EnsDeconv is freely available as an R-package from https:\/\/github.com\/randel\/EnsDeconv. The RNA microarray data from the TRAUMA study are available and can be accessed in GEO (GSE36809). The demographic and clinical phenotypes can be shared on reasonable request to the corresponding authors. The RNA-seq data from the EVAPR study cannot be shared publicly due to the privacy of individuals that participated in the clinical research in compliance with the IRB approval at the University of Pittsburgh. The RNA microarray data from the FHS study are available from dbGaP (phs000007.v32.p13). The RNA-seq data from ROS study is downloaded from AD Knowledge Portal.<\/jats:p><\/jats:sec><jats:sec><jats:title>Supplementary information<\/jats:title><jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p><\/jats:sec>","DOI":"10.1093\/bioinformatics\/btac279","type":"journal-article","created":{"date-parts":[[2022,4,14]],"date-time":"2022-04-14T11:10:15Z","timestamp":1649934615000},"page":"3004-3010","source":"Crossref","is-referenced-by-count":25,"title":["Robust and accurate estimation of cellular fraction from tissue omics data via ensemble deconvolution"],"prefix":"10.1093","volume":"38","author":[{"given":"Manqi","family":"Cai","sequence":"first","affiliation":[{"name":"Department of Biostatistics, University of Pittsburgh , Pittsburgh, PA 15261, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Molin","family":"Yue","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, University of Pittsburgh , Pittsburgh, PA 15261, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tianmeng","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Surgery, University of Pittsburgh , Pittsburgh, PA 15213, USA"},{"name":"Department of Pathology, University of Pittsburgh School of Medicine , Pittsburgh, PA 15213, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinling","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Engineering Management and Systems Engineering, Missouri University of Science and Technology , Rolla, MO 65409, USA"},{"name":"Department of Biological Sciences, Missouri University of Science and Technology , Rolla, MO 65409, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Erick","family":"Forno","sequence":"additional","affiliation":[{"name":"Department of Pediatrics, University of Pittsburgh Medical Center Children\u2019s Hospital of Pittsburgh , Pittsburgh, PA 15224, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8599-2269","authenticated-orcid":false,"given":"Xinghua","family":"Lu","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, University of Pittsburgh , Pittsburgh, PA 15206, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Timothy","family":"Billiar","sequence":"additional","affiliation":[{"name":"Department of Surgery, University of Pittsburgh , Pittsburgh, PA 15213, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6139-5320","authenticated-orcid":false,"given":"Juan","family":"Celed\u00f3n","sequence":"additional","affiliation":[{"name":"Department of Pediatrics, University of Pittsburgh Medical Center Children\u2019s Hospital of Pittsburgh , Pittsburgh, PA 15224, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chris","family":"McKennan","sequence":"additional","affiliation":[{"name":"Department of Statistics, University of Pittsburgh , Pittsburgh, PA 15213, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7196-8703","authenticated-orcid":false,"given":"Wei","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Pediatrics, University of Pittsburgh Medical Center Children\u2019s Hospital of Pittsburgh , Pittsburgh, PA 15224, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3828-4400","authenticated-orcid":false,"given":"Jiebiao","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, University of Pittsburgh , Pittsburgh, PA 15261, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2022,4,19]]},"reference":[{"key":"2023041403082283000_","doi-asserted-by":"crossref","first-page":"720","DOI":"10.1002\/msb.134947","article-title":"Digital cell quantification identifies global immune cell dynamics during influenza infection","volume":"10","author":"Altboum","year":"2014","journal-title":"Mol. 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