voomDDA: discovery of diagnostic biomarkers and classification of RNA-seq data

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dc.authoridZararsız, Gökmen/0000-0001-5801-1835
dc.authoridZARARSIZ, GOKMEN/0000-0001-5801-1835
dc.authoridOzturk, Ahmet/0000-0002-7130-5624
dc.authoridGOKSULUK, DINCER/0000-0002-2752-7668
dc.authoridKorkmaz, Selçuk/0000-0003-4632-6850
dc.authorwosidZararsız, Gökmen/E-8818-2013
dc.authorwosidZARARSIZ, GOKMEN/ABH-7959-2020
dc.authorwosidOzturk, Ahmet/M-6564-2014
dc.authorwosidKarabulut, Erdem/E-9242-2013
dc.authorwosidELDEM, VAHAP/A-9160-2018
dc.authorwosidGOKSULUK, DINCER/E-9175-2013
dc.authorwosidKorkmaz, Selçuk/AAU-4677-2020
dc.contributor.authorZararsiz, Gokmen
dc.contributor.authorGoksuluk, Dincer
dc.contributor.authorKlaus, Bernd
dc.contributor.authorKorkmaz, Selcuk
dc.contributor.authorEldem, Vahap
dc.contributor.authorKarabulut, Erdem
dc.contributor.authorOzturk, Ahmet
dc.date.accessioned2024-06-12T10:59:29Z
dc.date.available2024-06-12T10:59:29Z
dc.date.issued2017
dc.departmentTrakya Üniversitesien_US
dc.description.abstractRNA-Seq is a recent and efficient technique that uses the capabilities of next-generation sequencing technology for characterizing and quantifying transcriptomes. One important task using gene-expression data is to identify a small subset of genes that can be used to build diagnostic classifiers particularly for cancer diseases. Microarray based classifiers are not directly applicable to RNA-Seq data due to its discrete nature. Overdispersion is another problem that requires careful modeling of rnean and variance relationship of the RNA-Seq data. In this study, we present voomDDA classifiers: variance modeling at the observational level (voom) extensions of the nearest shrunken centroids (NSC) and the diagonal discriminant classifiers. VoomNSC is one of these classifiers and brings voom and NSC approaches together for the purpose of gene-expression based classification. For this purpose, we propose weighted statistics and put these weighted statistics into the NSC algorithm. The VoomNSC is a sparse classifier that models the mean-variance relationship using the voom method and incorporates voom's precision weights into the NSC classifier via weighted statistics. A comprehensive simulation study was designed and four real datasets are used for performance assessment. The overall results indicate that voomNSC performs as the sparsest classifier. It also provides the most accurate results together with power-transformed Poissan linear discriminant analysis, rlog transformed support vector machines and random forests algorithms. In addition to prediction purposes, the voomNSC classifier can be used to identify the potential diagnostic biomarkers for a condition of interest. Through this work, statistical learning methods proposed for can be reused for RNA-Seq data.en_US
dc.description.sponsorshipResearch Fund of Erciyes University [TDK-2015-5468]en_US
dc.description.sponsorshipThis work was supported by the Research Fund of Erciyes University [TDK-2015-5468]. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.en_US
dc.identifier.doi10.7717/peerj.3890
dc.identifier.issn2167-8359
dc.identifier.pmid29018623en_US
dc.identifier.scopus2-s2.0-85030664036en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.7717/peerj.3890
dc.identifier.urihttps://hdl.handle.net/20.500.14551/20469
dc.identifier.volume5en_US
dc.identifier.wosWOS:000412520500008en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherPeerj Incen_US
dc.relation.ispartofPeerjen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectDiagnostic Biomarker Discoveryen_US
dc.subjectDiagonal Discriminant Analysisen_US
dc.subjectMachine Learningen_US
dc.subjectGene-Expression Based Classificationen_US
dc.subjectVoom Transformationen_US
dc.subjectNearest Shrunken Centroidsen_US
dc.subjectShrunken Centroidsen_US
dc.subjectTumorsen_US
dc.titlevoomDDA: discovery of diagnostic biomarkers and classification of RNA-seq dataen_US
dc.typeArticleen_US

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