Dataset Information

Bayesian Analysis of RNA-Seq Data Using a Family of Negative Binomial Models.

ABSTRACT: The analysis of RNA-Seq data has been focused on three main categories, including gene expression, relative exon usage and transcript expression. Methods have been proposed independently for each category using a negative binomial (NB) model. However, counts following a NB distribution on one feature (e.g., exon) do not guarantee a NB distribution for the other two features (e.g., gene/transcript). In this paper we propose a family of Negative Binomial models, which integrates the gene, exon and transcript analysis under a coherent NB model. The proposed model easily incorporates the uncertainty of assigning reads to transcripts and simplifies substantially the estimation for the relative usage. We developed simple Gibbs sampling algorithms for the posterior inference by exploiting fully tractable closed-forms of computation via suitable conjugate priors. The proposed models were investigated under extensive simulations. Finally, we applied our model to a real data set.

SUBMITTER: Zhao L

PROVIDER: S-EPMC8052637 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature

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Bayesian Analysis of RNA-Seq Data Using a Family of Negative Binomial Models.

Zhao Lili L Wu Weisheng W Feng Dai D Jiang Hui H Nguyen XuanLong X

Bayesian analysis 20170408 2

The analysis of RNA-Seq data has been focused on three main categories, including gene expression, relative exon usage and transcript expression. Methods have been proposed independently for each category using a negative binomial (NB) model. However, counts following a NB distribution on one feature (e.g., exon) do not guarantee a NB distribution for the other two features (e.g., gene/transcript). In this paper we propose a family of Negative Binomial models, which integrates the gene, exon and ...[more]

PMID: 33868546

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Project description: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.R or https://github.com/yangchadam/NBLDA.

Dataset Information

Bayesian Analysis of RNA-Seq Data Using a Family of Negative Binomial Models.

Publications

Bayesian Analysis of RNA-Seq Data Using a Family of Negative Binomial Models.

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