A comparison of methods for differential expression analysis of RNA-seq data
Open accessAbstract
BACKGROUND: Finding 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. RESULTS: We 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. CONCLUSIONS: Very 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.
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Methods for transforming normalized counts of RNA-seq reads while learning the variance structure of the data have been shown to perform well in comparison to discrete distribution approaches.
“Methods for transforming normalized counts of RNA-seq reads while learning the variance structure of the data have been shown to perform well in comparison to the discrete distribution approaches described above [55, 56].”
From A survey of best practices for RNA-seq data analysis (2016) · cites this paper as [56]
Recent independent comparison studies have demonstrated that the choice of the method (or even the version of a software package) can markedly affect the outcome of the analysis and that no single method is likely to perform favorably for all datasets.
“Recent independent comparison studies have demonstrated that the choice of the method (or even the version of a software package) can markedly affect the outcome of the analysis and that no single method is likely to perform favorably for all datasets [56, 63, 64]”
From A survey of best practices for RNA-seq data analysis (2016) · cites this paper as [56]
Most comparison studies of differential expression methods have used simulated datasets or spike-in samples with known quantities.
“Most comparison studies have focused on simulated datasets [56, 208, 209] or on samples to which exogenous RNA (‘spike-in’) has been added in known quantities [63, 196].”
From A survey of best practices for RNA-seq data analysis (2016) · cites this paper as [56]
Cuffdiff and Cuffdiff2 performed surprisingly poorly in the comparisons.
“Cuffdiff and Cuffdiff2 have performed surprisingly poorly in the comparisons [56, 63].”
From A survey of best practices for RNA-seq data analysis (2016) · cites this paper as [56]
limma performs well under many circumstances and is the fastest to run among the tools.
“Among the tools, limma has been shown to perform well under many circumstances and it is also the fastest to run [56, 63, 64].”
From A survey of best practices for RNA-seq data analysis (2016) · cites this paper as [56]
Appropriately transformed and normalized sequencing data can be treated similarly to microarray data for statistical modelling and multiple corrections.
“Appropriately transformed and normalized sequencing data can be treated similarly to microarray data as far as statistical modelling and multiple corrections are concerned175,220.”
From Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders (2015) · cites this paper as [220]
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- Authors
- Soneson, C., & Delorenzi, M.
- Affiliations
- SIB Swiss Institute of Bioinformatics (CH)
- Corresponding author
- Soneson, Charlotte
- Venue
- BMC Bioinformatics
- Published
- 2013-03-09
- Volume
- 14
- Issue
- 1
- Pages
- 91-91
- ISSN
- 1471-2105 (ISSN-L)
- PMC
- PMC3608160
- OpenAlex
- W2074414424
- Cited by
- 887
- References
- 34
- Citations by year
- 2026: 24 · 2025: 21 · 2024: 46 · 2023: 45 · 2022: 50 · 2021: 84 · 2020: 66 · 2019: 82 · 2018: 72 · 2017: 83 · 2016: 91 · 2015: 97 · 2014: 101 · 2013: 24
- FWCI
- 46.70
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- BMC Bioinformatics (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- IRIS (submittedVersion)
- SERVAL (Université de Lausanne) (submittedVersion, other-oa)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Keywords
- differential expression analysis, RNA-seq, method comparison, small sample size, variance stabilizing transformation, limma, count data, R package, simulated data
- MeSH terms
- Animals, Mice, Inbred C57BL, Mice, Inbred DBA, RNA, Messenger, Software, Genome, Sequence Analysis, RNA, DNA, Complementary, Gene Expression Profiling, Genomics, Mice
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