{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T11:02:15Z","timestamp":1776769335492,"version":"3.51.2"},"reference-count":26,"publisher":"Oxford University Press (OUP)","issue":"10","license":[{"start":{"date-parts":[[2016,10,2]],"date-time":"2016-10-02T00:00:00Z","timestamp":1475366400000},"content-version":"vor","delay-in-days":985,"URL":"http:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2014,5,15]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Motivation: Recently there has been increasing interest in the effects of cell mixture on the measurement of DNA methylation, specifically the extent to which small perturbations in cell mixture proportions can register as changes in DNA methylation. A recently published set of statistical methods exploits this association to infer changes in cell mixture proportions, and these methods are presently being applied to adjust for cell mixture effect in the context of epigenome-wide association studies. However, these adjustments require the existence of reference datasets, which may be laborious or expensive to collect. For some tissues such as placenta, saliva, adipose or tumor tissue, the relevant underlying cell types may not be known.<\/jats:p><jats:p>Results: We propose a method for conducting epigenome-wide association studies analysis when a reference dataset is unavailable, including a bootstrap method for estimating standard errors. We demonstrate via simulation study and several real data analyses that our proposed method can perform as well as or better than methods that make explicit use of reference datasets. In particular, it may adjust for detailed cell type differences that may be unavailable even in existing reference datasets.<\/jats:p><jats:p>Availability and implementation: Software is available in the R package RefFreeEWAS. Data for three of four examples were obtained from Gene Expression Omnibus (GEO), accession numbers GSE37008, GSE42861 and GSE30601, while reference data were obtained from GEO accession number GSE39981.<\/jats:p><jats:p>Contact: \u00a0andres.houseman@oregonstate.edu<\/jats:p><jats:p>Supplementary information: \u00a0Supplementary data are available at Bioinformatics online.<\/jats:p>","DOI":"10.1093\/bioinformatics\/btu029","type":"journal-article","created":{"date-parts":[[2014,1,23]],"date-time":"2014-01-23T01:47:51Z","timestamp":1390441671000},"page":"1431-1439","source":"Crossref","is-referenced-by-count":418,"title":["Reference-free cell mixture adjustments in analysis of DNA methylation data"],"prefix":"10.1093","volume":"30","author":[{"given":"Eugene Andres","family":"Houseman","sequence":"first","affiliation":[{"name":"1 School of Biological and Population Health Sciences, College of Public Health and Human Sciences, Oregon State University, Corvallis, OR 97331, USA and 2Section of Biostatistics and Epidemiology, Department of Community and Family Medicine, Geisel School of Medicine at Dartmouth, Hanover, NH 03755, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"John","family":"Molitor","sequence":"additional","affiliation":[{"name":"1 School of Biological and Population Health Sciences, College of Public Health and Human Sciences, Oregon State University, Corvallis, OR 97331, USA and 2Section of Biostatistics and Epidemiology, Department of Community and Family Medicine, Geisel School of Medicine at Dartmouth, Hanover, NH 03755, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Carmen J.","family":"Marsit","sequence":"additional","affiliation":[{"name":"1 School of Biological and Population Health Sciences, College of Public Health and Human Sciences, Oregon State University, Corvallis, OR 97331, USA and 2Section of Biostatistics and Epidemiology, Department of Community and Family Medicine, Geisel School of Medicine at Dartmouth, Hanover, NH 03755, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2014,1,21]]},"reference":[{"key":"2023041302475190300_","doi-asserted-by":"crossref","first-page":"e46705","DOI":"10.1371\/journal.pone.0046705","article-title":"Heterogeneity in white blood cells has potential to confound DNA methylation measurements","volume":"7","author":"Adalsteinsson","year":"2012","journal-title":"PLoS One"},{"key":"2023041302475190300_","doi-asserted-by":"crossref","first-page":"920","DOI":"10.4161\/epi.6.7.16079","article-title":"Infant growth restriction is associated with distinct patterns of DNA methylation in human placentas","volume":"6","author":"Banister","year":"2011","journal-title":"Epigenetics"},{"key":"2023041302475190300_","doi-asserted-by":"crossref","first-page":"55","DOI":"10.4161\/epi.1.1.2643","article-title":"DNA methylation analysis as a tool for cell typing","volume":"1","author":"Baron","year":"2006","journal-title":"Epigenetics"},{"key":"2023041302475190300_","doi-asserted-by":"crossref","first-page":"705","DOI":"10.1038\/nrg3273","article-title":"Analysing and interpreting DNA methylation data","volume":"13","author":"Bock","year":"2012","journal-title":"Nat. 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