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

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.

SUBMITTER: Landau WM

PROVIDER: S-EPMC3857202 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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

Landau William Michael WM Liu Peng P

PloS one 20131209 12

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 sever ...[more]

PMID: 24349066

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Project description:A significant challenge to the effective application of RNA-seq to the complete transcript analysis of low quantity and/or degraded samples is the amplification of minimal input RNA to enable sequencing library construction. Several strategies have been commercialized in order to facilitate this goal. However, each strategy has its own specific protocols and methodology, and each may introduce unique bias and in some cases show specific preference for a collection of sequences. Our wider investigation of human spermatozoal RNAs was able to reveal their complexity despite being generally characterized by low quantity and high fragmentation. In this study, the following four commercially available RNA-seq amplification and library protocols for the preparation of low quantity/highly fragmented samples, SMARTer™ Ultra Low RNA (SU) for Illumina® Sequencing, SeqPlex RNA Amplification (SP), Ovation® RNA-Seq System V2 (OR), and Ovation® RNA-Seq Formalin Fixed Paraffin Embedded System (FFPES) were assessed using human sperm RNAs. Further investigation analyzed the effects on the end results of two different library preparation methods, Encore NGS Multiplex System I (Enc) and Ovation Ultralow Library Systems (UL), that appeared best suited to this type of RNA, along with other potential confounding factors such as FFPE preservation. Our results indicate that for each library preparation protocol, the differences in the initial amount of input RNA and choice of RNA purification step do not generate marked differences in terms of RNA profiling. However, substantial disparity is introduced by individual amplification methods prior to library construction. These significant differences may be caused by the different priming methods or amplification strategies used in each of the four different protocols examined. The observation of intra-sample variation introduced by the choice of protocol highlights the role that external factors play in planning and subsequent reliable interpretation of results of any RNA-seq experiment.

Dataset Information

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

Publications

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

Similar Datasets

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