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NBLDA: Negative Binomial Linear Discriminant Analysis for RNA-Seq Data
RNA-sequencing (RNA-Seq) has become a powerful technology to characterize
gene expression profiles because it is more accurate and comprehensive than
microarrays. Although statistical methods that have been developed for
microarray data can be applied to RNA-Seq data, they are not ideal due to the
discrete nature of RNA-Seq data. The Poisson distribution and negative binomial
distribution are commonly used to model count data. Recently, Witten (2011)
proposed a Poisson linear discriminant analysis for RNA-Seq data. The Poisson
assumption may not be as appropriate as negative binomial distribution when
biological replicates are available and in the presence of overdispersion
(i.e., when the variance is larger than the mean). However, it is more
complicated to model negative binomial variables because they involve a
dispersion parameter that needs to be estimated. In this paper, we propose a
negative binomial linear discriminant analysis for RNA-Seq data. By Bayes'
rule, we construct the classifier by fitting a negative binomial model, and
propose some plug-in rules to estimate the unknown parameters in the
classifier. The relationship between the negative binomial classifier and the
Poisson classifier is explored, with a numerical investigation of the impact of
dispersion on the discriminant score. Simulation results show the superiority
of our proposed method. We also analyze four real RNA-Seq data sets to
demonstrate the advantage of our method in real-world applications
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