{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T20:34:54Z","timestamp":1772138094577,"version":"3.50.1"},"reference-count":41,"publisher":"Oxford University Press (OUP)","issue":"20","license":[{"start":{"date-parts":[[2019,3,23]],"date-time":"2019-03-23T00:00:00Z","timestamp":1553299200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"name":"National Institute of Health","award":["R01GM122083"],"award-info":[{"award-number":["R01GM122083"]}]},{"name":"National Institute of Health","award":["P50AG025688"],"award-info":[{"award-number":["P50AG025688"]}]},{"name":"National Institute of Health","award":["R01GM122083"],"award-info":[{"award-number":["R01GM122083"]}]},{"name":"National Institute of Health","award":["P20GM103645"],"award-info":[{"award-number":["P20GM103645"]}]},{"name":"National Institute of Health","award":["NS097206"],"award-info":[{"award-number":["NS097206"]}]},{"name":"National Institute of Health","award":["AG052476"],"award-info":[{"award-number":["AG052476"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,10,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Samples from clinical practices are often mixtures of different cell types. The high-throughput data obtained from these samples are thus mixed signals. The cell mixture brings complications to data analysis, and will lead to biased results if not properly accounted for.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We develop a method to model the high-throughput data from mixed, heterogeneous samples, and to detect differential signals. Our method allows flexible statistical inference for detecting a variety of cell-type specific changes. Extensive simulation studies and analyses of two real datasets demonstrate the favorable performance of our proposed method compared with existing ones serving similar purpose.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>The proposed method is implemented as an R package and is freely available on GitHub (https:\/\/github.com\/ziyili20\/TOAST).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Supplementary information<\/jats:title>\n                    <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btz196","type":"journal-article","created":{"date-parts":[[2019,3,20]],"date-time":"2019-03-20T16:23:04Z","timestamp":1553098984000},"page":"3898-3905","source":"Crossref","is-referenced-by-count":42,"title":["Dissecting differential signals in high-throughput data from complex tissues"],"prefix":"10.1093","volume":"35","author":[{"given":"Ziyi","family":"Li","sequence":"first","affiliation":[{"name":"Department of Biostatistics and Bioinformatics, Emory University , Atlanta, GA, USA"}]},{"given":"Zhijin","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, Brown University , Providence, RI, USA"}]},{"given":"Peng","family":"Jin","sequence":"additional","affiliation":[{"name":"Department of Human Genetics, Emory University , Atlanta, GA, USA"}]},{"given":"Hao","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Biostatistics and Bioinformatics, Emory University , Atlanta, GA, USA"}]}],"member":"286","published-online":{"date-parts":[[2019,3,23]]},"reference":[{"key":"2023013108193074100_btz196-B1","doi-asserted-by":"crossref","first-page":"e6098.","DOI":"10.1371\/journal.pone.0006098","article-title":"Deconvolution of blood microarray data identifies cellular activation patterns in systemic lupus erythematosus","volume":"4","author":"Abbas","year":"2009","journal-title":"PLoS One"},{"key":"2023013108193074100_btz196-B2","doi-asserted-by":"crossref","first-page":"R106.","DOI":"10.1186\/gb-2010-11-10-r106","article-title":"Differential expression analysis for sequence count data","volume":"11","author":"Anders","year":"2010","journal-title":"Genome Biol"},{"key":"2023013108193074100_btz196-B3","doi-asserted-by":"crossref","first-page":"1363","DOI":"10.1093\/bioinformatics\/btu049","article-title":"Minfi: a flexible and comprehensive Bioconductor package for the analysis of infinium DNA methylation microarrays","volume":"30","author":"Aryee","year":"2014","journal-title":"Bioinformatics"},{"key":"2023013108193074100_btz196-B4","first-page":"1546","article-title":"Purification of specific cell population by fluorescence activated cell sorting (FACS)","volume":"41","author":"Basu","year":"2010","journal-title":"J. Vis. Exp"},{"key":"2023013108193074100_btz196-B5","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1159\/000087446","article-title":"The rush memory and aging project: study design and baseline characteristics of the study cohort","volume":"25","author":"Bennett","year":"2005","journal-title":"Neuroepidemiology"},{"key":"2023013108193074100_btz196-B6","doi-asserted-by":"crossref","first-page":"4164","DOI":"10.1073\/pnas.0308531101","article-title":"Metagenes and molecular pattern discovery using matrix factorization","volume":"101","author":"Brunet","year":"2004","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"2023013108193074100_btz196-B7","doi-asserted-by":"crossref","first-page":"1043","DOI":"10.1093\/bioinformatics\/btq097","article-title":"Statistical expression deconvolution from mixed tissue samples","volume":"26","author":"Clarke","year":"2010","journal-title":"Bioinformatics"},{"key":"2023013108193074100_btz196-B8","doi-asserted-by":"crossref","first-page":"57.","DOI":"10.1038\/nature11247","article-title":"An integrated encyclopedia of DNA elements in the human genome","volume":"489","year":"2012","journal-title":"Nature"},{"key":"2023013108193074100_btz196-B9","doi-asserted-by":"crossref","first-page":"2571","DOI":"10.1093\/bioinformatics\/btq406","article-title":"Probabilistic analysis of gene expression measurements from heterogeneous tissues","volume":"26","author":"Erkkil\u00e4","year":"2010","journal-title":"Bioinformatics"},{"key":"2023013108193074100_btz196-B10","doi-asserted-by":"crossref","first-page":"e69","DOI":"10.1093\/nar\/gku154","article-title":"A Bayesian hierarchical model to detect differentially methylated loci from single nucleotide resolution sequencing data","volume":"42","author":"Feng","year":"2014","journal-title":"Nucleic Acids Res"},{"key":"2023013108193074100_btz196-B11","first-page":"2211","article-title":"CellMix: a comprehensive toolbox for gene expression deconvolution","volume-title":"Bioinformatics","author":"Gaujoux","year":"2013"},{"key":"2023013108193074100_btz196-B12","doi-asserted-by":"crossref","first-page":"367.","DOI":"10.1186\/1471-2105-11-367","article-title":"A flexible r package for nonnegative matrix factorization","volume":"11","author":"Gaujoux","year":"2010","journal-title":"BMC Bioinformatics"},{"key":"2023013108193074100_btz196-B13","doi-asserted-by":"crossref","first-page":"e27156.","DOI":"10.1371\/journal.pone.0027156","article-title":"Optimal deconvolution of transcriptional profiling data using quadratic programming with application to complex clinical blood samples","volume":"6","author":"Gong","year":"2011","journal-title":"PLoS One"},{"key":"2023013108193074100_btz196-B14","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1016\/j.molcel.2016.03.019","article-title":"Methylome-wide analysis of chronic HIV infection reveals five-year increase in biological age and epigenetic targeting of HLA","volume":"62","author":"Gross","year":"2016","journal-title":"Mol. Cell"},{"key":"2023013108193074100_btz196-B15","doi-asserted-by":"crossref","first-page":"290","DOI":"10.4161\/epi.23924","article-title":"A cell epigenotype specific model for the correction of brain cellular heterogeneity bias and its application to age, brain region and major depression","volume":"8","author":"Guintivano","year":"2013","journal-title":"Epigenetics"},{"key":"2023013108193074100_btz196-B16","doi-asserted-by":"crossref","first-page":"86.","DOI":"10.1186\/1471-2105-13-86","article-title":"DNA methylation arrays as surrogate measures of cell mixture distribution","volume":"13","author":"Houseman","year":"2012","journal-title":"BMC Bioinformatics"},{"key":"2023013108193074100_btz196-B17","doi-asserted-by":"crossref","first-page":"1431","DOI":"10.1093\/bioinformatics\/btu029","article-title":"Reference-free cell mixture adjustments in analysis of DNA methylation data","volume":"30","author":"Houseman","year":"2014","journal-title":"Bioinformatics"},{"key":"2023013108193074100_btz196-B18","doi-asserted-by":"crossref","first-page":"259.","DOI":"10.1186\/s12859-016-1140-4","article-title":"Reference-free deconvolution of DNA methylation data and mediation by cell composition effects","volume":"17","author":"Houseman","year":"2016","journal-title":"BMC Bioinformatics"},{"key":"2023013108193074100_btz196-B19","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1016\/0165-5728(89)90115-X","article-title":"Relationship of microglia and astrocytes to amyloid deposits of Alzheimer disease","volume":"24","author":"Itagaki","year":"1989","journal-title":"J. Neuroimmunol"},{"key":"2023013108193074100_btz196-B20","doi-asserted-by":"crossref","first-page":"R31.","DOI":"10.1186\/gb-2014-15-2-r31","article-title":"Accounting for cellular heterogeneity is critical in epigenome-wide association studies","volume":"15","author":"Jaffe","year":"2014","journal-title":"Genome Biol"},{"key":"2023013108193074100_btz196-B21","doi-asserted-by":"crossref","first-page":"15.","DOI":"10.1097\/00062752-199901000-00004","article-title":"Microglia and Alzheimer\u2019s disease","volume":"6","author":"Kalaria","year":"1999","journal-title":"Curr. Opin. Hematol"},{"key":"2023013108193074100_btz196-B22","doi-asserted-by":"crossref","first-page":"945.","DOI":"10.1038\/nmeth.1710","article-title":"Population-specific expression analysis (PSEA) reveals molecular changes in diseased brain","volume":"8","author":"Kuhn","year":"2011","journal-title":"Nat. Methods"},{"key":"2023013108193074100_btz196-B23","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1038\/nbt.2487","article-title":"Epigenome-wide association data implicate DNA methylation as an intermediary of genetic risk in rheumatoid arthritis","volume":"31","author":"Liu","year":"2013","journal-title":"Nat. Biotechnol"},{"key":"2023013108193074100_btz196-B24","doi-asserted-by":"crossref","first-page":"679.","DOI":"10.1038\/ncpneuro0355","article-title":"Mechanisms of disease: astrocytes in neurodegenerative disease","volume":"2","author":"Maragakis","year":"2006","journal-title":"Nat. Rev. Neurol"},{"key":"2023013108193074100_btz196-B25","doi-asserted-by":"crossref","first-page":"R94.","DOI":"10.1186\/gb-2013-14-8-r94","article-title":"Measuring cell-type specific differential methylation in human brain tissue","volume":"14","author":"Monta\u00f1o","year":"2013","journal-title":"Genome Biol"},{"key":"2023013108193074100_btz196-B26","doi-asserted-by":"crossref","first-page":"453.","DOI":"10.1038\/nmeth.3337","article-title":"Robust enumeration of cell subsets from tissue expression profiles","volume":"12","author":"Newman","year":"2015","journal-title":"Nat. Methods"},{"key":"2023013108193074100_btz196-B27","doi-asserted-by":"crossref","first-page":"27.","DOI":"10.1186\/1471-2105-11-27","article-title":"Biomarker discovery in heterogeneous tissue samples-taking the in-silico deconfounding approach","volume":"11","author":"Repsilber","year":"2010","journal-title":"BMC Bioinformatics"},{"key":"2023013108193074100_btz196-B28","doi-asserted-by":"crossref","first-page":"e47","DOI":"10.1093\/nar\/gkv007","article-title":"limma powers differential expression analyses for RNA-sequencing and microarray studies","volume":"43","author":"Ritchie","year":"2015","journal-title":"Nucleic Acids Res"},{"key":"2023013108193074100_btz196-B29","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1111\/j.1600-0609.1994.tb00095.x","article-title":"Magnetic activated cell sorting (MACS) a new immunomagnetic method for megakaryocytic cell isolation: comparison of different separation techniques","volume":"52","author":"Schmitz","year":"1994","journal-title":"Eur. J. Haematol"},{"key":"2023013108193074100_btz196-B30","doi-asserted-by":"crossref","first-page":"287.","DOI":"10.1038\/nmeth.1439","article-title":"Cell type\u2013specific gene expression differences in complex tissues","volume":"7","author":"Shen-Orr","year":"2010","journal-title":"Nat. Methods"},{"key":"2023013108193074100_btz196-B31","doi-asserted-by":"crossref","first-page":"703","DOI":"10.3233\/JAD-2009-1180","article-title":"Neuropathology in the adult changes in thought study: a review","volume":"18","author":"Sonnen","year":"2009","journal-title":"J. Alzheimers Dis"},{"key":"2023013108193074100_btz196-B32","doi-asserted-by":"crossref","first-page":"105.","DOI":"10.1186\/s12859-017-1511-5","article-title":"A comparison of reference-based algorithms for correcting cell-type heterogeneity in epigenome-wide association studies","volume":"18","author":"Teschendorff","year":"2017","journal-title":"BMC Bioinformatics"},{"key":"2023013108193074100_btz196-B33","doi-asserted-by":"crossref","first-page":"5116","DOI":"10.1073\/pnas.091062498","article-title":"Significance analysis of microarrays applied to the ionizing radiation response","volume":"98","author":"Tusher","year":"2001","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"2023013108193074100_btz196-B34","doi-asserted-by":"crossref","first-page":"981","DOI":"10.1523\/JNEUROSCI.13-03-00981.1993","article-title":"Proton nuclear magnetic resonance spectroscopy unambiguously identifies different neural cell types","volume":"13","author":"Urenjak","year":"1993","journal-title":"J. Neurosci"},{"key":"2023013108193074100_btz196-B35","doi-asserted-by":"crossref","first-page":"399","DOI":"10.1016\/j.nurt.2010.05.017","article-title":"Astrocytes in Alzheimer\u2019s disease","volume":"7","author":"Verkhratsky","year":"2010","journal-title":"Neurotherapeutics"},{"key":"2023013108193074100_btz196-B36","doi-asserted-by":"crossref","first-page":"e1005223.","DOI":"10.1371\/journal.pgen.1005223","article-title":"Cell specific eQTL analysis without sorting cells","volume":"11","author":"Westra","year":"2015","journal-title":"PLoS Genet"},{"key":"2023013108193074100_btz196-B37","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1093\/biostatistics\/kxs033","article-title":"A new shrinkage estimator for dispersion improves differential expression detection in RNA-seq data","volume":"14","author":"Wu","year":"2012","journal-title":"Biostatistics"},{"key":"2023013108193074100_btz196-B38","doi-asserted-by":"crossref","first-page":"1059.","DOI":"10.1038\/s41592-018-0213-x","article-title":"Identification of differentially methylated cell types in epigenome-wide association studies","volume":"15","author":"Zheng","year":"2018","journal-title":"Nat. Methods"},{"key":"2023013108193074100_btz196-B39","doi-asserted-by":"crossref","first-page":"8.","DOI":"10.1038\/nmeth.1830","article-title":"Gene expression deconvolution in linear space","volume":"9","author":"Zhong","year":"2012","journal-title":"Nat. Methods"},{"key":"2023013108193074100_btz196-B40","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1186\/1471-2105-14-89","article-title":"Digital sorting of complex tissues for cell type-specific gene expression profiles","volume":"14","author":"Zhong","year":"2013","journal-title":"BMC Bioinformatics"},{"key":"2023013108193074100_btz196-B41","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1038\/nmeth.2815","article-title":"Epigenome-wide association studies without the need for cell-type composition","volume":"11","author":"Zou","year":"2014","journal-title":"Nat. Methods"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/academic.oup.com\/bioinformatics\/advance-article-pdf\/doi\/10.1093\/bioinformatics\/btz196\/28492028\/btz196.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/35\/20\/3898\/48977335\/bioinformatics_35_20_3898.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/35\/20\/3898\/48977335\/bioinformatics_35_20_3898.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,31]],"date-time":"2023-01-31T10:09:10Z","timestamp":1675159750000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/35\/20\/3898\/5418952"}},"subtitle":[],"editor":[{"given":"John","family":"Hancock","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2019,3,23]]},"references-count":41,"journal-issue":{"issue":"20","published-print":{"date-parts":[[2019,10,15]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btz196","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/402354","asserted-by":"object"}]},"ISSN":["1367-4803","1367-4811"],"issn-type":[{"value":"1367-4803","type":"print"},{"value":"1367-4811","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2019,10,15]]},"published":{"date-parts":[[2019,3,23]]}}}