Cited by 5,195Gene expression and cancer classificationGenomics and Phylogenetic StudiesSingle-cell and spatial transcriptomics

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Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation

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McCarthy, Davis J., Chen, Yunshun, Smyth, Gordon K.·2012·Nucleic Acids Research

DOI 10.1093/nar/gks042PMC PMC3378882

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Abstract

A flexible statistical framework is developed for the analysis of read counts from RNA-Seq gene expression studies. It provides the ability to analyse complex experiments involving multiple treatment conditions and blocking variables while still taking full account of biological variation. Biological variation between RNA samples is estimated separately from the technical variation associated with sequencing technologies. Novel empirical Bayes methods allow each gene to have its own specific variability, even when there are relatively few biological replicates from which to estimate such variability. The pipeline is implemented in the edgeR package of the Bioconductor project. A case study analysis of carcinoma data demonstrates the ability of generalized linear model methods (GLMs) to detect differential expression in a paired design, and even to detect tumour-specific expression changes. The case study demonstrates the need to allow for gene-specific variability, rather than assuming a common dispersion across genes or a fixed relationship between abundance and variability. Genewise dispersions de-prioritize genes with inconsistent results and allow the main analysis to focus on changes that are consistent between biological replicates. Parallel computational approaches are developed to make non-linear model fitting faster and more reliable, making the application of GLMs to genomic data more convenient and practical. Simulations demonstrate the ability of adjusted profile likelihood estimators to return accurate estimators of biological variability in complex situations. When variation is gene-specific, empirical Bayes estimators provide an advantageous compromise between the extremes of assuming common dispersion or separate genewise dispersion. The methods developed here can also be applied to count data arising from DNA-Seq applications, including ChIP-Seq for epigenetic marks and DNA methylation analyses.

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Figure 1. Multidimensional scaling plot of the squamous cell carcinoma profiles in which distances correspond to BCV between pairs of samples. Pairwise BCVs were computed from the 500 most heterogeneous genes. Samples are labelled with patient number and either ‘T’ for tumour or ‘N’ or normal. The first plot dimension roughly corresponds to tissue source (normal or tumour) and the second to patient differences. The tumour samples are more heterogeneous than the normals.CC BY-NC · PubMed Central

Figure 3. Boxplots of common BCV estimates from 100 simulated data sets. The left panel shows results for the one group case, with three replicate samples in the group. The right panel shows results for a paired-design with two groups and three blocks. The horizontal lines indicate the true common BCV of 0.4, chosen to match with the carcinoma case study. Conditional maximum likelihood (qCML) is the most accurate in the former case. For generalized linear models, Cox–Reid APL is the best performer.CC BY-NC · PubMed Central

Figure 4. Mean-square error with which empirical Bayes genewise dispersions estimate the true dispersion (BCV2), when true dispersions are randomly generated. In this case, the optimal prior weight is 10–12 prior genes, equivalent to 20–24 prior degrees of freedom. The common BCV estimator is equivalent to using infinite weight for the prior. Boxplots show results for 10 simulations.CC BY-NC · PubMed Central

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Authors: McCarthy, D. J., Chen, Y., & Smyth, G. K.
Affiliations: The University of Melbourne (AU)
Corresponding author: Smyth, Gordon K.
ORCID: McCarthy, Davis J.
Chen, Yunshun
Smyth, Gordon K.
Venue: Nucleic Acids Research
Publisher: Oxford University Press (OUP)
Published: 2012-01-28
Volume: 40
Issue: 10
Pages: 4288-4297
ISSN: 0305-1048 (ISSN-L)
1362-4962
DOI: 10.1093/nar/gks042
PMC: PMC3378882
OpenAlex: W2130116522
Cited by: 5,195
References: 70
Citations by year: 2026: 203 · 2025: 339 · 2024: 496 · 2023: 527 · 2022: 680 · 2021: 828 · 2020: 640 · 2019: 463 · 2018: 354 · 2017: 237 · 2016: 157 · 2015: 110 · 2014: 92 · 2013: 47 · 2012: 18
FWCI: 34.30
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by-nc
creativecommons.org/licenses/by-nc/3.0 (vor, 1746 d)
Locations: Nucleic Acids Research (publishedVersion, cc-by-nc)
PubMed Central (publishedVersion, cc-by-nc)
PubMed (publishedVersion)
Minerva Access (University of Melbourne) (submittedVersion, cc-by-nc)
HighWire Press Open Archive (submittedVersion)
Funders: Medical Research Council
National Health and Medical Research Council
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 read counts, biological variation, technical variability, empirical Bayes, edgeR, generalized linear models, paired design, carcinoma, profile likelihood, count data analysis, DNA sequencing, ChIP-seq, DNA methylation
MeSH terms: Algorithms, Bayes Theorem, Carcinoma, Squamous Cell, Mouth Neoplasms, Genetic Variation, Linear Models, Sequence Analysis, RNA, Gene Expression Profiling, High-Throughput Nucleotide Sequencing

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Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation

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