{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T21:27:30Z","timestamp":1777930050477,"version":"3.51.4"},"reference-count":23,"publisher":"Oxford University Press (OUP)","issue":"6","license":[{"start":{"date-parts":[[2022,10,7]],"date-time":"2022-10-07T00:00:00Z","timestamp":1665100800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/100000053","name":"National Eye Institute","doi-asserted-by":"publisher","award":["R21EY031877"],"award-info":[{"award-number":["R21EY031877"]}],"id":[{"id":"10.13039\/100000053","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000053","name":"National Eye Institute","doi-asserted-by":"publisher","award":["R01EY030192"],"award-info":[{"award-number":["R01EY030192"]}],"id":[{"id":"10.13039\/100000053","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000050","name":"National Heart, Lung, and Blood Institute","doi-asserted-by":"publisher","award":["R21HL156234"],"award-info":[{"award-number":["R21HL156234"]}],"id":[{"id":"10.13039\/100000050","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000050","name":"National Heart, Lung, and Blood Institute","doi-asserted-by":"publisher","award":["R01HL150359"],"award-info":[{"award-number":["R01HL150359"]}],"id":[{"id":"10.13039\/100000050","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,11,19]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Cell-type composition of intact bulk tissues can vary across samples. Deciphering cell-type composition and its changes during disease progression is an important step toward understanding disease pathogenesis. To infer cell-type composition, existing cell-type deconvolution methods for bulk RNA sequencing (RNA-seq) data often require matched single-cell RNA-seq (scRNA-seq) data, generated from samples with similar clinical conditions, as reference. However, due to the difficulty of obtaining scRNA-seq data in diseased samples, only limited scRNA-seq data in matched disease conditions are available. Using scRNA-seq reference to deconvolve bulk RNA-seq data from samples with different disease conditions may lead to a biased estimation of cell-type proportions. To overcome this limitation, we propose an iterative estimation procedure, MuSiC2, which is an extension of MuSiC, to perform deconvolution analysis of bulk RNA-seq data generated from samples with multiple clinical conditions where at least one condition is different from that of the scRNA-seq reference. Extensive benchmark evaluations indicated that MuSiC2 improved the accuracy of cell-type proportion estimates of bulk RNA-seq samples under different conditions as compared with the traditional MuSiC deconvolution. MuSiC2 was applied to two bulk RNA-seq datasets for deconvolution analysis, including one from human pancreatic islets and the other from human retina. We show that MuSiC2 improves current deconvolution methods and provides more accurate cell-type proportion estimates when the bulk and single-cell reference differ in clinical conditions. We believe the condition-specific cell-type composition estimates from MuSiC2 will facilitate the downstream analysis and help identify cellular targets of human diseases.<\/jats:p>","DOI":"10.1093\/bib\/bbac430","type":"journal-article","created":{"date-parts":[[2022,9,15]],"date-time":"2022-09-15T17:03:26Z","timestamp":1663261406000},"source":"Crossref","is-referenced-by-count":57,"title":["MuSiC2: cell-type deconvolution for multi-condition bulk RNA-seq data"],"prefix":"10.1093","volume":"23","author":[{"given":"Jiaxin","family":"Fan","sequence":"first","affiliation":[{"name":"Department of Biostatistics , Epidemiology and Informatics, , Philadelphia, PA 19104 , USA"},{"name":"University of Pennsylvania Perelman School of Medicine , Epidemiology and Informatics, , Philadelphia, PA 19104 , USA"}]},{"given":"Yafei","family":"Lyu","sequence":"additional","affiliation":[{"name":"Department of Biostatistics , Epidemiology and Informatics, , Philadelphia, PA 19104 , USA"},{"name":"University of Pennsylvania Perelman School of Medicine , Epidemiology and Informatics, , Philadelphia, PA 19104 , USA"}]},{"given":"Qihuang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Epidemiology , Biostatistics and Occupational Health, , Montreal, QC, H3A 1G1 , Canada"},{"name":"McGill University , Biostatistics and Occupational Health, , Montreal, QC, H3A 1G1 , Canada"}]},{"given":"Xuran","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Statistics and Data Science, Carnegie Mellon University , Pittsburgh, PA 15213 , USA"}]},{"given":"Mingyao","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Biostatistics , Epidemiology and Informatics, , Philadelphia, PA 19104 , USA"},{"name":"University of Pennsylvania Perelman School of Medicine , Epidemiology and Informatics, , Philadelphia, PA 19104 , USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2727-8171","authenticated-orcid":false,"given":"Rui","family":"Xiao","sequence":"additional","affiliation":[{"name":"Department of Biostatistics , Epidemiology and Informatics, , Philadelphia, PA 19104 , USA"},{"name":"University of Pennsylvania Perelman School of Medicine , Epidemiology and Informatics, , Philadelphia, PA 19104 , USA"}]}],"member":"286","published-online":{"date-parts":[[2022,10,7]]},"reference":[{"key":"2022112111123780700_ref1","doi-asserted-by":"crossref","DOI":"10.1172\/jci.insight.88843","article-title":"Cell-type deconvolution with immune pathways identifies gene networks of host defense and immunopathology in leprosy","volume":"1","author":"Inkeles","year":"2016","journal-title":"JCI Insight"},{"key":"2022112111123780700_ref2","doi-asserted-by":"crossref","first-page":"773","DOI":"10.1038\/s41587-019-0114-2","article-title":"Determining cell type abundance and expression from bulk tissues with digital 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