{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T15:35:55Z","timestamp":1764603355998},"reference-count":35,"publisher":"Oxford University Press (OUP)","issue":"5","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2016,3,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Motivation: DNA methylation is a key epigenetic modification that can modulate gene expression. Over the past decade, a lot of studies have focused on profiling DNA methylation and investigating its alterations in complex diseases such as cancer. While early studies were mostly restricted to CpG islands or promoter regions, recent findings indicate that many of important DNA methylation changes can occur in other regions and DNA methylation needs to be examined on a genome-wide scale. In this article, we apply the wavelet-based functional mixed model methodology to analyze the high-throughput methylation data for identifying differentially methylated loci across the genome. Contrary to many commonly-used methods that model probes independently, this framework accommodates spatial correlations across the genome through basis function modeling as well as correlations between samples through functional random effects, which allows it to be applied to many different settings and potentially leads to more power in detection of differential methylation.<\/jats:p>\n               <jats:p>Results: We applied this framework to three different high-dimensional methylation data sets (CpG Shore data, THREE data and NIH Roadmap Epigenomics data), studied previously in other works. A simulation study based on CpG Shore data suggested that in terms of detection of differentially methylated loci, this modeling approach using wavelets outperforms analogous approaches modeling the loci as independent. For the THREE data, the method suggests newly detected regions of differential methylation, which were not reported in the original study.<\/jats:p>\n               <jats:p>Availability and implementation: Automated software called WFMM is available at https:\/\/biostatistics.mdanderson.org\/SoftwareDownload. CpG Shore data is available at http:\/\/rafalab.dfci.harvard.edu. NIH Roadmap Epigenomics data is available at http:\/\/compbio.mit.edu\/roadmap.<\/jats:p>\n               <jats:p>Supplementary information: \u00a0Supplementary data are available at Bioinformatics online.<\/jats:p>\n               <jats:p>Contact: \u00a0jefmorris@mdanderson.org<\/jats:p>","DOI":"10.1093\/bioinformatics\/btv659","type":"journal-article","created":{"date-parts":[[2015,11,12]],"date-time":"2015-11-12T02:10:31Z","timestamp":1447294231000},"page":"664-672","source":"Crossref","is-referenced-by-count":25,"title":["Identification of differentially methylated loci using wavelet-based functional mixed models"],"prefix":"10.1093","volume":"32","author":[{"given":"Wonyul","family":"Lee","sequence":"first","affiliation":[{"name":"Department of Biostatistics, The University of Texas M.D. Anderson Cancer Center, Houston, TX, USA"}]},{"given":"Jeffrey S.","family":"Morris","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, The University of Texas M.D. Anderson Cancer Center, Houston, TX, USA"}]}],"member":"286","published-online":{"date-parts":[[2015,11,11]]},"reference":[{"key":"2023020110353199000_btv659-B1","volume-title":"The Statistical Analysis of Time Series","author":"Anderson","year":"1971"},{"key":"2023020110353199000_btv659-B2","doi-asserted-by":"crossref","first-page":"3891","DOI":"10.1021\/es0700911","article-title":"Determinants of fetal exposure to polyfluoroalkyl compounds in Baltimore, Maryland","volume":"41","author":"Apelberg","year":"2007","journal-title":"Environ. Sci. Technol."},{"key":"2023020110353199000_btv659-B3","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1093\/biostatistics\/kxq055","article-title":"Accurate genome-scale percentage DNA methylation estimates from microarray data","volume":"12","author":"Aryee","year":"2011","journal-title":"Biostatistics"},{"key":"2023020110353199000_btv659-B4","doi-asserted-by":"crossref","first-page":"1280","DOI":"10.1093\/bioinformatics\/bts124","article-title":"CpGassoc: an R function for analysis of DNA methylation microarray data","volume":"28","author":"Barfield","year":"2012","journal-title":"Bioinformatics"},{"key":"2023020110353199000_btv659-B5","doi-asserted-by":"crossref","first-page":"288","DOI":"10.1016\/j.ygeno.2011.07.007","article-title":"High density DNA methylation array with single CpG site resolution","volume":"98","author":"Bibikova","year":"2011","journal-title":"Genomics"},{"key":"2023020110353199000_btv659-B6","doi-asserted-by":"crossref","first-page":"999","DOI":"10.1002\/j.1460-2075.1987.tb04851.x","article-title":"Non-methylated CpG-rich islands at the human alpha-globin locus: implications for evolution of the alpha-globin pseudogene","volume":"6","author":"Bird","year":"1987","journal-title":"EMBO J."},{"key":"2023020110353199000_btv659-B7","doi-asserted-by":"crossref","first-page":"829","DOI":"10.1080\/01621459.1979.10481038","article-title":"Robust locally weighted regression and smoothing scatterplots","volume":"74","author":"Cleveland","year":"1979","journal-title":"J. Am. Stat. Assoc."},{"key":"2023020110353199000_btv659-B8","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1093\/biostatistics\/kxi004","article-title":"Denoising array-based comparative genomic hybridization data using wavelets","volume":"6","author":"Hsu","year":"2005","journal-title":"Biostatistics"},{"key":"2023020110353199000_btv659-B9","doi-asserted-by":"crossref","first-page":"780","DOI":"10.1101\/gr.7301508","article-title":"Comprehensive high-throughput arrays for relative methylation (CHARM)","volume":"18","author":"Irizarry","year":"2008","journal-title":"Genome Res."},{"key":"2023020110353199000_btv659-B10","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1038\/ng.298","article-title":"The human colon cancer methylome shows similar hypo- and hypermethylation at conserved tissue-specific CpG island shores","volume":"41","author":"Irizarry","year":"2009","journal-title":"Nat. Genet."},{"key":"2023020110353199000_btv659-B11","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1093\/ije\/dyr238","article-title":"Bump hunting to identify differentially methylated regions in epigenetic epidemiology studies","volume":"41","author":"Jaffe","year":"2012","journal-title":"Int. J. Epidemiol."},{"key":"2023020110353199000_btv659-B12","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1038\/nature14248","article-title":"Integrative analysis of 111 reference human epigenomes","volume":"518","author":"Kundaje","year":"2015","journal-title":"Nature"},{"key":"2023020110353199000_btv659-B13","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1038\/nrg2732","article-title":"Principles and challenges of genome-wide DNA methylation analysis","volume":"11","author":"Laird","year":"2010","journal-title":"Nat. Rev. Genet."},{"key":"2023020110353199000_btv659-B14","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1093\/ije\/dyr237","article-title":"DNA methylation shows genome-wide association of NFIX, RAPGEF2 and MSRB3 with gestational age at birth","volume":"41","author":"Lee","year":"2012","journal-title":"Int. J. Epidemiol."},{"key":"2023020110353199000_btv659-B15","doi-asserted-by":"crossref","first-page":"733","DOI":"10.1038\/nrg2825","article-title":"Tackling the widespread and critical impact of batch effects in high-throughput data","volume":"11","author":"Leek","year":"2010","journal-title":"Nat. Rev. Genet."},{"key":"2023020110353199000_btv659-B16","doi-asserted-by":"crossref","first-page":"315","DOI":"10.1038\/nature08514","article-title":"Human DNA methylomes at base resolution show widespread epigenomic differences","volume":"462","author":"Lister","year":"2009","journal-title":"Nature"},{"key":"2023020110353199000_btv659-B150","doi-asserted-by":"crossref","first-page":"514","DOI":"10.1080\/01621459.2013.793118","article-title":"A study of Mexican free-tailed bat syllables: Bayesian functional mixed models for nonstationary acoustic time series","volume":"108","author":"Martinez","year":"2013","journal-title":"Journal of the American Statistical Association"},{"key":"2023020110353199000_btv659-B17","doi-asserted-by":"crossref","first-page":"563","DOI":"10.1111\/biom.12299","article-title":"Bayesian function-on-function regression for multi-level functional data","volume":"71","author":"Meyer","year":"2015","journal-title":"Biometrics"},{"key":"2023020110353199000_btv659-B18","doi-asserted-by":"crossref","first-page":"e45486","DOI":"10.1371\/journal.pone.0045486","article-title":"Waveseq: A novel data-driven method of detecting histone modification enrichments using wavelets","volume":"7","author":"Mitra","year":"2012","journal-title":"PLoS One"},{"key":"2023020110353199000_btv659-B19","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1111\/j.1467-9868.2006.00539.x","article-title":"Wavelet-based functional mixed models","volume":"68","author":"Morris","year":"2006","journal-title":"J. R. Stat. Soc. Ser. B (Stat. Methodol.)"},{"key":"2023020110353199000_btv659-B20","doi-asserted-by":"crossref","first-page":"1352","DOI":"10.1198\/016214506000000465","article-title":"Using wavelet-based functional mixed models to characterize population heterogeneity in accelerometer profiles: a case study","volume":"101","author":"Morris","year":"2006","journal-title":"J. Am. Stat. Assoc."},{"key":"2023020110353199000_btv659-B21","doi-asserted-by":"crossref","first-page":"479","DOI":"10.1111\/j.1541-0420.2007.00895.x","article-title":"Bayesian analysis of mass spectrometry proteomics data using wavelet based functional mixed models","volume":"64","author":"Morris","year":"2008","journal-title":"Biometrics"},{"key":"2023020110353199000_btv659-B22","doi-asserted-by":"crossref","first-page":"894","DOI":"10.1214\/10-AOAS407","article-title":"Automated analysis of quantitative image data using isomorphic functional mixed models, with application to proteomics data","volume":"5","author":"Morris","year":"2011","journal-title":"Ann. Appl. Stat."},{"key":"2023020110353199000_btv659-B23","article-title":"A wavelet-based method to exploit epigenomic language in the regulatory region","author":"Nguyen","year":"2013","journal-title":"Bioinformatics"},{"key":"2023020110353199000_btv659-B24","doi-asserted-by":"crossref","DOI":"10.1017\/CBO9780511755453","volume-title":"Semiparametric Regression","author":"Ruppert","year":"2003"},{"key":"2023020110353199000_btv659-B25","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1023\/A:1008818328241","article-title":"Wavelet shrinkage for unequally spaced data","volume":"9","author":"Sardy","year":"1999","journal-title":"Stat. Comput."},{"key":"2023020110353199000_btv659-B26","doi-asserted-by":"crossref","first-page":"665","DOI":"10.1214\/14-AOAS776","article-title":"Wavelet-based genetic association analysis of functional phenotypes arising from high-throughput sequencing assays","volume":"9","author":"Shim","year":"2015","journal-title":"Ann. Appl. Stat."},{"key":"2023020110353199000_btv659-B27","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1006\/acha.1996.0015","article-title":"The lifting scheme: a custom-design construction of biorthogonal wavelets","volume":"3","author":"Sweldens","year":"1996","journal-title":"Appl. Comput. Harmonic Anal."},{"key":"2023020110353199000_btv659-B28","doi-asserted-by":"crossref","first-page":"325","DOI":"10.2217\/epi.12.21","article-title":"Complete pipeline for infinium human methylation 450\u2009K BeadChip data processing using subset quantile normalization for accurate DNA methylation estimation","volume":"4","author":"Touleimat","year":"2012","journal-title":"Epigenomics"},{"key":"2023020110353199000_btv659-B29","doi-asserted-by":"crossref","first-page":"729","DOI":"10.1093\/bioinformatics\/bts013","article-title":"IMA: An R package for high-throughput analysis of illumina 450\u2009K infinium methylation data","volume":"28","author":"Wang","year":"2012","journal-title":"Bioinformatics"},{"key":"2023020110353199000_btv659-B30","doi-asserted-by":"crossref","first-page":"3705","DOI":"10.1093\/bioinformatics\/bth449","article-title":"limmaGUI: a graphical user interface for linear modeling of microarray data","volume":"20","author":"Wettenhall","year":"2004","journal-title":"Bioinformatics"},{"key":"2023020110353199000_btv659-B31","doi-asserted-by":"crossref","first-page":"1394","DOI":"10.1038\/bjc.2013.496","article-title":"Review of processing and analysis methods for DNA methylation array data","volume":"109","author":"Wilhelm-Benartzi","year":"2013","journal-title":"Br. J. Cancer"},{"key":"2023020110353199000_btv659-B32","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1186\/1756-0500-3-337","article-title":"MethVisual-visualization and exploratory statistical analysis of DNA methylation profiles from bisulfite sequencing","volume":"3","author":"Zackay","year":"2010","journal-title":"BMC Res. Notes"},{"key":"2023020110353199000_btv659-B33","doi-asserted-by":"crossref","first-page":"1167","DOI":"10.1198\/jasa.2011.tm10370","article-title":"Robust, adaptive functional regression in functional mixed model framework","volume":"106","author":"Zhu","year":"2011","journal-title":"J. Am. Stat. Assoc."},{"key":"2023020110353199000_btv659-B34","doi-asserted-by":"crossref","first-page":"1260","DOI":"10.1111\/j.1541-0420.2012.01765.x","article-title":"Robust classification of functional and quantitative image data using functional mixed models","volume":"68","author":"Zhu","year":"2012","journal-title":"Biometrics"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/32\/5\/664\/49017511\/bioinformatics_32_5_664.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/32\/5\/664\/49017511\/bioinformatics_32_5_664.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,1]],"date-time":"2023-02-01T21:59:16Z","timestamp":1675288756000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/32\/5\/664\/1744565"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2015,11,11]]},"references-count":35,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2016,3,1]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btv659","relation":{},"ISSN":["1367-4811","1367-4803"],"issn-type":[{"value":"1367-4811","type":"electronic"},{"value":"1367-4803","type":"print"}],"subject":[],"published-other":{"date-parts":[[2016,3,1]]},"published":{"date-parts":[[2015,11,11]]}}}