A comparison of methods for differential expression analysis of RNA-seq data

Charlotte Soneson,Mauro Delorenzi

doi:10.1186/1471-2105-14-91

Abstract

BackgroundFinding genes that are differentially expressed between conditions is an integral part of understanding the molecular basis of phenotypic variation. In the past decades, DNA microarrays have been used extensively to quantify the abundance of mRNA corresponding to different genes, and more recently high-throughput sequencing of cDNA (RNA-seq) has emerged as a powerful competitor. As the cost of sequencing decreases, it is conceivable that the use of RNA-seq for differential expression analysis will increase rapidly. To exploit the possibilities and address the challenges posed by this relatively new type of data, a number of software packages have been developed especially for differential expression analysis of RNA-seq data.ResultsWe conducted an extensive comparison of eleven methods for differential expression analysis of RNA-seq data. All methods are freely available within the R framework and take as input a matrix of counts, i.e. the number of reads mapping to each genomic feature of interest in each of a number of samples. We evaluate the methods based on both simulated data and real RNA-seq data.ConclusionsVery small sample sizes, which are still common in RNA-seq experiments, impose problems for all evaluated methods and any results obtained under such conditions should be interpreted with caution. For larger sample sizes, the methods combining a variance-stabilizing transformation with the ‘limma’ method for differential expression analysis perform well under many different conditions, as does the nonparametric SAMseq method.

Highlights

Finding genes that are differentially expressed between conditions is an integral part of understanding the molecular basis of phenotypic variation
Eleven methods for differential expression analysis of RNA-seq data were evaluated in this study
We evaluated the robustness of the methods against variations in the distribution of the input data, by instead imposing a Poisson distribution for the counts for some of the genes, or including outliers with abnormally high counts

Summary

Introduction

Finding genes that are differentially expressed between conditions is an integral part of understanding the molecular basis of phenotypic variation. RNA-seq uses next-generation sequencing (NGS) methods to sequence cDNA that has been derived from an RNA sample, and produces millions of short reads. These reads are typically mapped to a reference genome and the number of reads mapping within a genomic feature of interest (such as a gene or an exon) is used as a Arguably the most common use of transcriptome profiling is in the search for differentially expressed (DE) genes, that is, genes that show differences in expression level between conditions or in other ways are associated with given predictors or responses. In differential expression analysis, where the genes are tested individually for expression differences between conditions, such ‘within-sample’ biases are usually ignored since they are assumed to affect all samples [3]

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Results

Conclusion