{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,8]],"date-time":"2026-02-08T04:50:12Z","timestamp":1770526212755,"version":"3.49.0"},"reference-count":23,"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: The identification of DNA copy number changes provides insights that may advance our understanding of initiation and progression of cancer. Array-based comparative genomic hybridization (array-CGH) has emerged as a technique allowing high-throughput genome-wide scanning for chromosomal aberrations. A number of statistical methods have been proposed for the analysis of array-CGH data. In this article, we consider a fused quantile regression model based on three motivations: (1) quantile regression may provide a more comprehensive picture for the ratio profile of copy numbers than the standard mean regression approach; (2) for simplicity, most available methods assume uniform spacing between neighboring clones, while incorporating the information of physical locations of clones may be helpful and (3) most current methods have a set of tuning parameters that must be carefully tuned, which introduces complexity to the implementation.<\/jats:p><jats:p>Results: We formulate the detection of regions of gains and losses in a fused regularized quantile regression framework, incorporating physical locations of clones. We derive an efficient algorithm that computes the entire solution path for the resulting optimization problem, and we propose a simple estimate for the complexity of the fitted model, which leads to convenient selection of the tuning parameter. Three published array-CGH datasets are used to demonstrate our approach.<\/jats:p><jats:p>Availability: R code are available at http:\/\/www.stat.lsa.umich.edu\/~jizhu\/code\/cgh\/<\/jats:p><jats:p>Contact: \u00a0jizhu@umich.edu<\/jats:p><jats:p>Supplementary information: Supplementary data are available at Bioinformatics online.<\/jats:p>","DOI":"10.1093\/bioinformatics\/btm364","type":"journal-article","created":{"date-parts":[[2007,7,21]],"date-time":"2007-07-21T00:34:25Z","timestamp":1184978065000},"page":"2470-2476","source":"Crossref","is-referenced-by-count":30,"title":["Analysis of array CGH data for cancer studies using fused quantile regression"],"prefix":"10.1093","volume":"23","author":[{"given":"Youjuan","family":"Li","sequence":"first","affiliation":[{"name":"Department of Statistics, University of Michigan, Michigan, USA"}]},{"given":"Ji","family":"Zhu","sequence":"additional","affiliation":[{"name":"Department of Statistics, University of Michigan, Michigan, USA"}]}],"member":"286","published-online":{"date-parts":[[2007,7,20]]},"reference":[{"key":"2023041106230538000_","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/S1476-5586(03)80017-9","article-title":"Chromosomal localization of DNA amplifications in neuroblastoma tumors using cDNA microarray comparative genomic hybridization","volume":"5","author":"Beheshti","year":"2003","journal-title":"Neoplasia"},{"key":"2023041106230538000_","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":"2004","journal-title":"Bioinformatics"},{"key":"2023041106230538000_","doi-asserted-by":"crossref","first-page":"461","DOI":"10.1080\/01621459.1986.10478291","article-title":"How biased is the apparent error rate of a prediction rule?","volume":"81","author":"Efron","year":"1986","journal-title":"J. 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