Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.14365/4967
Title: | Qtqda: Quantile Transformed Quadratic Discriminant Analysis for High-Dimensional Rna-Seq Data | Authors: | Koçhan, N. Tutuncu, G.Y. Smyth, G.K. Gandolfo, L.C. Giner, G. |
Keywords: | Classification Dependent count data Gene expression Negative binomial distribution Quadratic discriminant analysis RNA-seq article binomial distribution discriminant analysis RNA sequencing |
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. Copyright 2019 Koçhan et al. | URI: | https://doi.org/10.7717/peerj.8260 https://hdl.handle.net/20.500.14365/4967 |
ISSN: | 2167-8359 |
Appears in Collections: | Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection |
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