Efficient experimental design and analysis strategies for the detection of differential expression using RNA-Sequencing
Open accessAbstract
Background: RNA sequencing (RNA-Seq) has emerged as a powerful approach for the detection of differential gene expression with both high-throughput and high resolution capabilities possible depending upon the experimental design chosen. Multiplex experimental designs are now readily available, these can be utilised to increase the numbers of samples or replicates profiled at the cost of decreased sequencing depth generated per sample. These strategies impact on the power of the approach to accurately identify differential expression. This study presents a detailed analysis of the power to detect differential expression in a range of scenarios including simulated null and differential expression distributions with varying numbers of biological or technical replicates, sequencing depths and analysis methods. Results: Differential and non-differential expression datasets were simulated using a combination of negative binomial and exponential distributions derived from real RNA-Seq data. These datasets were used to evaluate the performance of three commonly used differential expression analysis algorithms and to quantify the changes in power with respect to true and false positive rates when simulating variations in sequencing depth, biological replication and multiplex experimental design choices. Conclusions: This work quantitatively explores comparisons between contemporary analysis tools and experimental design choices for the detection of differential expression using RNA-Seq. We found that the DESeq algorithm performs more conservatively than edgeR and NBPSeq. With regard to testing of various experimental designs, this work strongly suggests that greater power is gained through the use of biological replicates relative to library (technical) replicates and sequencing depth. Strikingly, sequencing depth could be reduced as low as 15% without substantial impacts on false positive or true positive rates.
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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 [209]
DESeq and edgeR rank genes similarly but DESeq is often relatively conservative and edgeR too liberal in controlling FDR.
“DESeq and edgeR perform similarly in ranking genes but are often relatively conservative or too liberal, respectively, in controlling FDR [63, 209, 210].”
From A survey of best practices for RNA-seq data analysis (2016) · cites this paper as [209]
All differential expression methods require caution when the number of replicates is very small or for very lowly expressed genes.
“First, specific caution is needed with all the methods when the number of replicate samples is very small or for genes that are expressed at very low levels [55, 64, 209].”
From A survey of best practices for RNA-seq data analysis (2016) · cites this paper as [209]
SAMseq controls FDR well but has acceptable sensitivity only when the number of replicates is relatively high, at least 10.
“SAMseq performs well in terms of FDR but presents an acceptable sensitivity when the number of replicates is relatively high, at least 10 [20, 55, 209].”
From A survey of best practices for RNA-seq data analysis (2016) · cites this paper as [209]
In most experiments, biological variability is greater than technical variability, so biological replicates are of greater value than technical replicates.
“In most experiments, biological variability is greater than technical variability, so biological replicates are of greater value than technical replicates174,175,219.”
From Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders (2015) · cites this paper as [174]
Many guidelines for DGE and network analysis are based on theoretical models and empirical assessments, but most use data lacking the spatial, temporal, or disease-relevant complexities of CNS or post-mortem tissue.
“For DGE and network analysis, there are many studies with guidelines that are based on theoretical models and empirical assessments40,173–176, but most studies use data from experiments that do not have the spatial, temporal or disease-relevant complexities that occur in studies of the CNS or post-mortem tissue.”
From Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders (2015) · cites this paper as [174]
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- Authors
- Robles, J. A., Qureshi, S. E., Stephen, S. J., Wilson, S. R., Burden, C. J., & Taylor, J. M.
- Affiliations
- Australian National University (AU)
- Commonwealth Scientific and Industrial Research Organisation (AU)
- Corresponding author
- Taylor, Jennifer M
- Venue
- BMC Genomics
- Published
- 2012-09-17
- Volume
- 13
- Issue
- 1
- Pages
- 484-484
- ISSN
- 1471-2164 (ISSN-L)
- PMC
- PMC3560154
- OpenAlex
- W2068614693
- Cited by
- 198
- References
- 44
- Citations by year
- 2026: 2 · 2025: 6 · 2024: 3 · 2023: 11 · 2022: 13 · 2021: 11 · 2020: 13 · 2019: 5 · 2018: 18 · 2017: 16 · 2016: 24 · 2015: 25 · 2014: 36 · 2013: 14 · 2012: 1
- FWCI
- 9.78
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- BMC Genomics (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- ANU Open Research (Australian National University) (submittedVersion, cc-by)
- Funders
- Australian Research Council (DP1094699)
- 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 gene expression, RNA-seq, experimental design, sequencing depth, biological replicates, technical replicates, multiplexed sequencing, statistical power, false positive rate, diagnostic sensitivity, DESeq, edgeR, negative binomial distribution
- MeSH terms
- Algorithms, Statistics as Topic, Sequence Analysis, RNA, Gene Expression Profiling
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