Abstract
BackgroundRNA-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 (Annals Appl Stat 5:2493–2518, 2011) proposed a Poisson linear discriminant analysis for RNA-Seq data. The Poisson assumption may not be as appropriate as the negative binomial distribution when biological replicates are available and in the presence of overdispersion (i.e., when the variance is larger than or equal to the mean). However, it is more complicated to model negative binomial variables because they involve a dispersion parameter that needs to be estimated.ResultsIn 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 two real RNA-Seq data sets to demonstrate the advantages of our method in real-world applications.ConclusionsWe have developed a new classifier using the negative binomial model for RNA-seq data classification. Our simulation results show that our proposed classifier has a better performance than existing works. The proposed classifier can serve as an effective tool for classifying RNA-seq data. Based on the comparison results, we have provided some guidelines for scientists to decide which method should be used in the discriminant analysis of RNA-Seq data. R code is available at http://www.comp.hkbu.edu.hk/~xwan/NBLDA.Ror https://github.com/yangchadam/NBLDA
Highlights
RNA-sequencing (RNA-Seq) has become a powerful technology to characterize gene expression profiles because it is more accurate and comprehensive than microarrays
Simulation design We generate the data from the following negative binomial distribution: Xig |yi = k ∼ NB(siλg dkg, φ)
We have further explored the relationship between negative binomial linear discriminant analysis (NBLDA) and Poisson linear discriminant analysis (PLDA), and investigated the impact of dispersion on the discriminant score of NBLDA by conducting a numerical comparison
Summary
RNA-sequencing (RNA-Seq) has become a powerful technology to characterize gene expression profiles because it is more accurate and comprehensive than microarrays. The Poisson distribution and negative binomial distribution are commonly used to model count data. Witten (Annals Appl Stat 5:2493–2518, 2011) proposed a Poisson linear discriminant analysis for RNA-Seq data. RNA-sequencing (RNA-Seq) is a revolutionary technology that uses the capabilities of next-generation sequencing to infer gene expression levels [1,2,3]. RNA-Seq has many advantages including the detection of novel transcripts, low background signal, and the increased specificity and sensitivity. Due to reduced sequencing cost, RNA-Seq has been widely used in biomedical research in recent years [4]. RNA-seq usually produces millions of short reads, between 25 and 300 base-pairs in length. The reads are mapped to genomic or transcriptomic regions of interest
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