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https://hdl.handle.net/20.500.14365/2737
Title: | Qtqda: Quantile Transformed Quadratic Discriminant Analysis for High-Dimensional Rna-Seq Data | Authors: | Kochan, Necla Tütüncü, Gözde Yazgı Smyth, Gordon K. Gandoffo, Luke C. Giner, Goeknur |
Keywords: | Classification Gene expression RNA-seq Dependent count data Negative binomial distribution Quadratic discriminant analysis Covariance-Matrix Classification |
Publisher: | Peerj Inc | Abstract: | Classification on the basis of gene expression data derived from RNA-seq promises to become an important part of modern medicine. We propose a new classification method based on a model where the data is marginally negative binomial but dependent, thereby incorporating the dependence known to be present between measurements from different genes. The method, called qtQDA, works by first performing a quantile transformation (qt) then applying Gaussian quadratic discriminant analysis (QDA) using regularized covariance matrix estimates. We show that qtQDA has excellent performance when applied to real data sets and has advantages over some existing approaches. An R package implementing the method is also available on https://github.com/goknurginer/qtQDA. | URI: | https://doi.org/10.7717/peerj.8260 https://hdl.handle.net/20.500.14365/2737 |
ISSN: | 2167-8359 |
Appears in Collections: | PubMed İndeksli Yayınlar Koleksiyonu / PubMed Indexed Publications Collection WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection |
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