Journal article

qtQDA: quantile transformed quadratic discriminant analysis for high-dimensional RNA-seq data

Necla Kochan, G Yazgi Tutuncu, Gordon K Smyth, Luke C Gandoffo, Goeknur Giner

PEERJ | PEERJ INC | Published : 2019

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 metho..

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Grants

Awarded by Scientific and Technical Research Council of Turkey


Awarded by Australian National Health and Medical Research Council


Funding Acknowledgements

This work was supported by the Scientific and Technical Research Council of Turkey (TUBITAK 2214/A-1059B141601270) and by the Australian National Health and Medical Research Council (Program Grant 1054618 and Fellowship 1154970 to Gordon K. Smyth), the Cancer Therapeutics CRC, Victorian State Government Operational Infrastructure Support and Australian Government NHMRC IRIIS. Funding for the article processing fee was provided by Smyth Lab funds. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.