{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T14:58:29Z","timestamp":1761058709559,"version":"3.33.0"},"reference-count":26,"publisher":"Oxford University Press (OUP)","issue":"18","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2007,9,15]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Motivation: Array comparative genomic hybridization (aCGH) provides a genome-wide technique to screen for copy number alteration. The existing segmentation approaches for analyzing aCGH data are based on modeling data as a series of discrete segments with unknown boundaries and unknown heights. Although the biological process of copy number alteration is discrete, in reality a variety of biological and experimental factors can cause the signal to deviate from a stepwise function. To take this into account, we propose a smooth segmentation (smoothseg) approach.<\/jats:p><jats:p>Methods: To achieve a robust segmentation, we use a doubly heavy-tailed random-effect model. The first heavy-tailed structure on the errors deals with outliers in the observations, and the second deals with possible jumps in the underlying pattern associated with different segments. We develop a fast and reliable computational procedure based on the iterative weighted least-squares algorithm with band-limited matrix inversion.<\/jats:p><jats:p>Results: Using simulated and real data sets, we demonstrate how smoothseg can aid in identification of regions with genomic alteration and in classification of samples. For the real data sets, smoothseg leads to smaller false discovery rate and classification error rate than the circular binary segmentation (CBS) algorithm. In a realistic simulation setting, smoothseg is better than wavelet smoothing and CBS in identification of regions with genomic alterations and better than CBS in classification of samples. For comparative analyses, we demonstrate that segmenting the t-statistics performs better than segmenting the data.<\/jats:p><jats:p>Availability: The R package smoothseg to perform smooth segmentation is available from http:\/\/www.meb.ki.se\/~yudpaw<\/jats:p><jats:p>Contact: \u00a0yudi.pawitan@ki.se<\/jats:p>","DOI":"10.1093\/bioinformatics\/btm359","type":"journal-article","created":{"date-parts":[[2007,7,28]],"date-time":"2007-07-28T00:29:50Z","timestamp":1185582590000},"page":"2463-2469","source":"Crossref","is-referenced-by-count":33,"title":["Robust smooth segmentation approach for array CGH data analysis"],"prefix":"10.1093","volume":"23","author":[{"given":"Jian","family":"Huang","sequence":"first","affiliation":[{"name":"1 Statistical Laboratory, Department of Statistics, University College Cork, Ireland, 2MRC Biostatistics Unit, Institute of Public Health, Cambridge CB2 2SR, UK, 3Department of Oncology, University Hospital, SE-221 85 Lund and 4Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden"}]},{"given":"Arief","family":"Gusnanto","sequence":"additional","affiliation":[{"name":"1 Statistical Laboratory, Department of Statistics, University College Cork, Ireland, 2MRC Biostatistics Unit, Institute of Public Health, Cambridge CB2 2SR, UK, 3Department of Oncology, University Hospital, SE-221 85 Lund and 4Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden"}]},{"given":"Kathleen","family":"O'Sullivan","sequence":"additional","affiliation":[{"name":"1 Statistical Laboratory, Department of Statistics, University College Cork, Ireland, 2MRC Biostatistics Unit, Institute of Public Health, Cambridge CB2 2SR, UK, 3Department of Oncology, University Hospital, SE-221 85 Lund and 4Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden"}]},{"given":"Johan","family":"Staaf","sequence":"additional","affiliation":[{"name":"1 Statistical Laboratory, Department of Statistics, University College Cork, Ireland, 2MRC Biostatistics Unit, Institute of Public Health, Cambridge CB2 2SR, UK, 3Department of Oncology, University Hospital, SE-221 85 Lund and 4Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden"}]},{"given":"\u00c5ke","family":"Borg","sequence":"additional","affiliation":[{"name":"1 Statistical Laboratory, Department of Statistics, University College Cork, Ireland, 2MRC Biostatistics Unit, Institute of Public Health, Cambridge CB2 2SR, UK, 3Department of Oncology, University Hospital, SE-221 85 Lund and 4Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden"}]},{"given":"Yudi","family":"Pawitan","sequence":"additional","affiliation":[{"name":"1 Statistical Laboratory, Department of Statistics, University College Cork, Ireland, 2MRC Biostatistics Unit, Institute of Public Health, Cambridge CB2 2SR, UK, 3Department of Oncology, University Hospital, SE-221 85 Lund and 4Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden"}]}],"member":"286","published-online":{"date-parts":[[2007,7,27]]},"reference":[{"key":"2023041106230556800_","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1111\/j.2517-6161.1995.tb02031.x","article-title":"Controlling the false discovery rate: a practical and powerful approach to multiple testing","volume":"57","author":"Benjamini","year":"1995","journal-title":"J. R. Stat. Soc. Ser. B"},{"key":"2023041106230556800_","doi-asserted-by":"crossref","DOI":"10.1137\/1.9781611971811","volume-title":"LINPACK Users' Guide.","author":"Dongarra","year":"1979"},{"key":"2023041106230556800_","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1198\/016214502753479248","article-title":"Comparison of discrimination methods for the classification of tumors using gene expression data","volume":"97","author":"Dudoit","year":"2002","journal-title":"Journal of the American Statistical Association"},{"key":"2023041106230556800_","doi-asserted-by":"crossref","first-page":"1146","DOI":"10.1093\/bioinformatics\/bti148","article-title":"Quantile smoothing of array CGH data","volume":"21","author":"Eilers","year":"2003","journal-title":"Bioinformatics"},{"key":"2023041106230556800_","first-page":"339","article-title":"A pseudolikelihood approach for simultaneous analysis of array comparative genomic hybridiztions","volume":"7","author":"Engler","year":"2006","journal-title":"Bioinformatics"},{"key":"2023041106230556800_","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1016\/j.jmva.2004.02.008","article-title":"Hidden markov models approach to the analysis of array CGH data","volume":"90","author":"Fridlyand","year":"2004","journal-title":"J. Multivar. Anal"},{"key":"2023041106230556800_","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":"2023041106230556800_","doi-asserted-by":"crossref","first-page":"3413","DOI":"10.1093\/bioinformatics\/bth418","article-title":"Analysis of array CGH data: from signal ratio to gain and loss of DNA regions","volume":"20","author":"Hupe","year":"2004","journal-title":"Bioinformatics"},{"key":"2023041106230556800_","doi-asserted-by":"crossref","first-page":"7612","DOI":"10.1158\/0008-5472.CAN-05-0570","article-title":"Distinct genomic profiles in hereditary breast tumors identified by array-based comparative genomic hybridization","volume":"65","author":"Jonsson","year":"2005","journal-title":"Cancer Res"},{"key":"2023041106230556800_","doi-asserted-by":"crossref","first-page":"904","DOI":"10.1158\/0008-5472.CAN-03-2451","article-title":"Improved grading of breast adenocarcinomas based on genomic instability","volume":"64","author":"Kronenwett","year":"2004","journal-title":"Cancer Res"},{"key":"2023041106230556800_","doi-asserted-by":"crossref","first-page":"643","DOI":"10.1038\/25292","article-title":"Genetic instabilities in human cancers","volume":"396","author":"Lengauer","year":"1998","journal-title":"Nature"},{"key":"2023041106230556800_","doi-asserted-by":"crossref","first-page":"3763","DOI":"10.1093\/bioinformatics\/bti611","article-title":"Comparative analysis of algorithms for identifying amplifications and deletions in array CGH data","volume":"21","author":"Lai","year":"2005","journal-title":"Bioinformatics"},{"key":"2023041106230556800_","doi-asserted-by":"crossref","DOI":"10.1201\/9781420011340","article-title":"Generalized Linear Models with Random Effects","author":"Lee","year":"2006"},{"key":"2023041106230556800_","doi-asserted-by":"crossref","first-page":"3301","DOI":"10.1093\/bioinformatics\/bti499","article-title":"Prediction error estimation: a comparison of resampling methods","volume":"21","author":"Molinaro","year":"2005","journal-title":"Bioinformatics"},{"key":"2023041106230556800_","doi-asserted-by":"crossref","first-page":"557","DOI":"10.1093\/biostatistics\/kxh008","article-title":"Circular binary segmentation for the analysis of array-based DNA copy number data","volume":"5","author":"Olshen","year":"2004","journal-title":"Biostatistics"},{"key":"2023041106230556800_","doi-asserted-by":"crossref","first-page":"419","DOI":"10.1093\/biomet\/83.2.419","article-title":"Automatic estimation of coherence of bivariate time series","volume":"83","author":"Pawitan","year":"1996","journal-title":"Biometrika"},{"key":"2023041106230556800_","doi-asserted-by":"crossref","DOI":"10.1093\/oso\/9780198507659.001.0001","volume-title":"In All Likelihood: Statistical Modelling and Inference Using Likelihood.","author":"Pawitan","year":"2001"},{"key":"2023041106230556800_","doi-asserted-by":"crossref","first-page":"3017","DOI":"10.1093\/bioinformatics\/bti448","article-title":"FDR, sensitivity and sample size for microarray studies","volume":"21","author":"Pawitan","year":"2005","journal-title":"Bioinformatics"},{"key":"2023041106230556800_","first-page":"6","article-title":"A statistical approach for array CGH data analysis","volume":"21","author":"Picard","year":"2005","journal-title":"BMC Bioinformatics"},{"key":"2023041106230556800_","doi-asserted-by":"crossref","first-page":"12963","DOI":"10.1073\/pnas.162471999","article-title":"Microarray analysis reveals a major direct role of DNA copy number alteration in the transcriptional program of human breast tumors","volume":"99","author":"Pollack","year":"2002","journal-title":"Proc. Natl Acad. Sci. USA"},{"key":"2023041106230556800_","doi-asserted-by":"crossref","DOI":"10.1017\/CBO9780511755453","volume-title":"Semiparametric Regression.","author":"Ruppert","year":"2003"},{"key":"2023041106230556800_","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1038\/ng754","article-title":"Assembly of microarrays for genome-wide measurement of DNA copy number","volume":"29","author":"Snijders","year":"2001","journal-title":"Nat. Genet"},{"key":"2023041106230556800_","doi-asserted-by":"crossref","first-page":"479","DOI":"10.1111\/1467-9868.00346","article-title":"A direct approach to false discovery rates","volume":"64","author":"Storey","year":"2002","journal-title":"J. R. Stat. Soc. Ser. B"},{"key":"2023041106230556800_","doi-asserted-by":"crossref","first-page":"822","DOI":"10.1158\/0008-5472.822.65.3","article-title":"Comparative genomic hybridization profiles in human BRCA1 and BRCA2 breast tumors highlight differential sets of genomic aberrations","volume":"65","author":"van Beers","year":"2005","journal-title":"Cancer Res"},{"key":"2023041106230556800_","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1093\/biostatistics\/kxh017","article-title":"A method for calling gains and losses in array CGH data","volume":"61","author":"Wang","year":"2005","journal-title":"Biostatistics"},{"key":"2023041106230556800_","doi-asserted-by":"crossref","first-page":"4084","DOI":"10.1093\/bioinformatics\/bti677","article-title":"A comparison study: applying segmentation to array CGH data for downstream analyses","volume":"21","author":"Willenbrock","year":"2005","journal-title":"Bioinformatics"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/23\/18\/2463\/49817847\/bioinformatics_23_18_2463.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/23\/18\/2463\/49817847\/bioinformatics_23_18_2463.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,19]],"date-time":"2025-01-19T23:49:04Z","timestamp":1737330544000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/23\/18\/2463\/237992"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2007,7,27]]},"references-count":26,"journal-issue":{"issue":"18","published-print":{"date-parts":[[2007,9,15]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btm359","relation":{},"ISSN":["1367-4811","1367-4803"],"issn-type":[{"type":"electronic","value":"1367-4811"},{"type":"print","value":"1367-4803"}],"subject":[],"published-other":{"date-parts":[[2007,9,15]]},"published":{"date-parts":[[2007,7,27]]}}}