{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T18:59:57Z","timestamp":1762541997553},"reference-count":22,"publisher":"Oxford University Press (OUP)","issue":"19","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2011,10,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Motivation: A number of penalization and shrinkage approaches have been proposed for the analysis of microarray gene expression data. Similar techniques are now routinely applied to RNA sequence transcriptional count data, although the value of such shrinkage has not been conclusively established. If penalization is desired, the explicit modeling of mean\u2013variance relationships provides a flexible testing regimen that \u2018borrows\u2019 information across genes, while easily incorporating design effects and additional covariates.<\/jats:p>\n               <jats:p>Results: We describe BBSeq, which incorporates two approaches: (i) a simple beta-binomial generalized linear model, which has not been extensively tested for RNA-Seq data and (ii) an extension of an expression mean\u2013variance modeling approach to RNA-Seq data, involving modeling of the overdispersion as a function of the mean. Our approaches are flexible, allowing for general handling of discrete experimental factors and continuous covariates. We report comparisons with other alternate methods to handle RNA-Seq data. Although penalized methods have advantages for very small sample sizes, the beta-binomial generalized linear model, combined with simple outlier detection and testing approaches, appears to have favorable characteristics in power and flexibility.<\/jats:p>\n               <jats:p>Availability: An R package containing examples and sample datasets is available at http:\/\/www.bios.unc.edu\/research\/genomic_software\/BBSeq<\/jats:p>\n               <jats:p>Contact: \u00a0yzhou@bios.unc.edu; fwright@bios.unc.edu<\/jats:p>\n               <jats:p>Supplementary information: \u00a0Supplementary data are available at Bioinformatics online.<\/jats:p>","DOI":"10.1093\/bioinformatics\/btr449","type":"journal-article","created":{"date-parts":[[2011,8,3]],"date-time":"2011-08-03T02:01:04Z","timestamp":1312336864000},"page":"2672-2678","source":"Crossref","is-referenced-by-count":101,"title":["A powerful and flexible approach to the analysis of RNA sequence count data"],"prefix":"10.1093","volume":"27","author":[{"given":"Yi-Hui","family":"Zhou","sequence":"first","affiliation":[{"name":"Department of Biostatistics, University of North Carolina, Chapel Hill, NC 27599, USA"}]},{"given":"Kai","family":"Xia","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, University of North Carolina, Chapel Hill, NC 27599, USA"}]},{"given":"Fred A.","family":"Wright","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, University of North Carolina, Chapel Hill, NC 27599, USA"}]}],"member":"286","published-online":{"date-parts":[[2011,8,2]]},"reference":[{"key":"2023012512004065700_B1","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":"2023012512004065700_B2","doi-asserted-by":"crossref","first-page":"e1435","DOI":"10.1371\/journal.pone.0001435","article-title":"Sex specific gene regulation and expression QTLs in mouse macrophages from a strain intercross","volume":"3","author":"Bhasin","year":"2008","journal-title":"PLoS One"},{"key":"2023012512004065700_B3","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1101\/gr.099226.109","article-title":"Sex- specific and lineage-specific alternative splicing in primates","volume":"20","author":"Blekhman","year":"2010","journal-title":"Genome Res."},{"key":"2023012512004065700_B4","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1186\/1471-2105-11-94","article-title":"Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments","volume":"11","author":"Bullard","year":"2010","journal-title":"BMC Bioinformatics"},{"key":"2023012512004065700_B5","doi-asserted-by":"crossref","first-page":"400","DOI":"10.1038\/nature03479","article-title":"X-inactivation profile reveals extensive variability in X-linked gene expression in females","volume":"434","author":"Carrel","year":"2005","journal-title":"Nature"},{"key":"2023012512004065700_B6","doi-asserted-by":"crossref","first-page":"422","DOI":"10.1186\/1471-2105-11-422","article-title":"baySeq: empirical Bayesian methods for identifying differential expression in sequence count data","volume":"11","author":"Hardcastle","year":"2010","journal-title":"BMC Bioinformatics"},{"key":"2023012512004065700_B7","doi-asserted-by":"crossref","first-page":"272","DOI":"10.1038\/386272a0","article-title":"Xist has properties of the X-chromosome inactivation centre","volume":"386","author":"Herzing","year":"1997","journal-title":"Nature"},{"key":"2023012512004065700_B8","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1111\/j.1541-0420.2006.00675.x","article-title":"Assessing differential gene expression with small sample sizes in oligonucleotide arrays using a mean-variance model","volume":"63","author":"Hu","year":"2007","journal-title":"Biometrics"},{"key":"2023012512004065700_B9","doi-asserted-by":"crossref","first-page":"9758","DOI":"10.1073\/pnas.0703736104","article-title":"A genome-wide approach to identify genetic variants that contribute to etoposide-induced cytotoxicity","volume":"104","author":"Huang","year":"2007","journal-title":"Proc. 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