Dispersion estimation and its effect on test performance in RNA-seq data analysis: a simulation-based comparison of methods

PloS One
William Michael Landau, Peng Liu

Abstract

A central goal of RNA sequencing (RNA-seq) experiments is to detect differentially expressed genes. In the ubiquitous negative binomial model for RNA-seq data, each gene is given a dispersion parameter, and correctly estimating these dispersion parameters is vital to detecting differential expression. Since the dispersions control the variances of the gene counts, underestimation may lead to false discovery, while overestimation may lower the rate of true detection. After briefly reviewing several popular dispersion estimation methods, this article describes a simulation study that compares them in terms of point estimation and the effect on the performance of tests for differential expression. The methods that maximize the test performance are the ones that use a moderate degree of dispersion shrinkage: the DSS, Tagwise wqCML, and Tagwise APL. In practical RNA-seq data analysis, we recommend using one of these moderate-shrinkage methods with the QLShrink test in QuasiSeq R package.

References

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Citations

Jul 16, 2015·BioMed Research International·J Zyprych-WalczakI Siatkowski
Jun 4, 2015·PloS One·Nysia I GeorgeChing-Wei Chang
Mar 11, 2017·Genome Medicine·William R SwindellNicole L Ward
Dec 19, 2018·Frontiers in Genetics·Zhihua GaoWenqiang Tang
Jul 30, 2019·Journal of the American Statistical Association·Will LandauDan Nettleton
Jan 12, 2016·Quantitative Biology·Junhai JiangMomiao Xiong
Sep 25, 2019·Evolutionary Applications·Deiene Rodriguez-BarretoCarlos Garcia de Leaniz
Sep 15, 2016·BMC Bioinformatics·Kai DongXiang Wan
Mar 20, 2018·Brain, Behavior, and Immunity·Annesa FlentjeBradley E Aouizerat

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Datasets Mentioned

BETA
GSE19480
GSE20895

Methods Mentioned

BETA
RNA-seq

Software Mentioned

DSS
AMAP
Seq R
edgeR R package
R package
ClusterGeneration R package
DESeq R
Seq
DESeq
R

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