Document Type

Journal Article

Department/Unit

Department of Mathematics

Language

English

Abstract

Background

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 (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.

Results

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 two real RNA-Seq data sets to demonstrate the advantages of our method in real-world applications.

Conclusions

We 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

Keywords

RNA-Seq, Negative binomial distribution, Linear discriminant analysis

Publication Date

9-13-2016

Source Publication Title

BMC Bioinformatics

Volume

17

Start Page

369

Publisher

BioMed Central

Peer Reviewed

1

Copyright

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Funder

Hongyu Zhao’s research was supported by the National Institutes of Health grant R01 GM59507. Xiang Wan’s research was supported by the Hong Kong RGC grant HKBU12202114, the Hong Kong Baptist University grant FRG2/14-15/077, and Hong Kong Baptist University Strategic Development Fund. Tiejun Tong’s research was supported in part by Hong Kong Baptist University FRG grants FRG1/14-15/084, FRG2/15-16/019 and FRG2/15-16/038, and the National Natural Science Foundation of China grant (No. 11671338).

DOI

10.1186/s12859-016-1208-1

Link to Publisher's Edition

http://dx.doi.org/10.1186/s12859-016-1208-1

ISSN (print)

14712105

Included in

Mathematics Commons

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