Bioinformatics Seminar
Time:
Venue: Na
17 September 2019
NaqtQDA: Quantile Transformed Quadratic Discriminant Analysis for High-dimensional RNA-seq Data
Goknur GinerWEHI Bioinformatics
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. The paper is available on https://www.biorxiv.org/content/10.1101/751370v1 and an R package implementing the method is also available on https://github.com/goknurginer/qtQDA.