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

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

4,744 citations recorded by the source corpus.

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.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/571c3ea8cabd16ab0ae7a1a3495d3f3aca918e23

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Citations in the corpus

Robustly detecting differential expression in RNA sequencing data using observation weights (2013, methodology): “The adjusted profile likelihood (APL) introduced by Cox and Reid (21) has shown good performance for dispersion estimation in the context of genome-scale count data (8,22).”
Why weight? Modelling sample and observational level variability improves power in RNA-seq analyses (2015, methodology): “In each analysis, the linear modelling (Equation (1)) was carried out on the normalized log2(CPM).”
From reads to genes to pathways: differential expression analysis of RNA-Seq experiments using Rsubread and the edgeR quasi-likelihood pipeline (2016, methodology): “The dispersion estimates can be visualized with plotBCV: > plotBCV(y) (see Figure 4).”
ReactomeGSA - Efficient Multi-Omics Comparative Pathway Analysis (2020, methodology): “For Camera and PADOG, discrete values are normalised using edgeR’s (17) calcNormFactors function.”
SingleCellSignalR: inference of intercellular networks from single-cell transcriptomics (2019, methodology): “For annotation purposes, differentially expressed genes between each cluster and all the other clusters pooled, are successively searched with edgeR functions glmFit and glmRT (38).”
Leaf Responses to Mild Drought Stress in Natural Variants of Arabidopsis1[OPEN] (2015, positive): “Data were normalized using TMM, implemented in the edgeR package (version 3.5.27) in R (version 3.1.0; Robinson et al., 2010; McCarthy et al., 2012).”
CyTOF workflow: differential discovery in high-throughput high-dimensional cytometry datasets (2017, neutral): “UTF-8 LC_NAME=C ## [9] LC_ADDRESS=C LC_TELEPHONE=C ## [11] LC_MEASUREMENT=en_US.”
Monocyte-derived alveolar macrophages drive lung fibrosis and persist in the lung over the life span (2017, methodology): “, 2013), and differential gene expression was assessed using edgeR (Robinson et al., 2010; McCarthy et al., 2012) R/Bioconductor packages.”
voom: precision weights unlock linear model analysis tools for RNA-seq read counts (2014, methodology): “While several previous methods fit a semiparametric trend to the variances or to the NB dispersions [13, 19, 34, 23], the trend has always been used to estimate gene-level model parameters.”
A Unified Model for the Function of YTHDF Proteins in Regulating m6A-Modified mRNA (2020, methodology): “For each DF paralog silencing experiment, TMM normalization, empirical Bayes estimate of the negative binomial dispersion, and measurement of the level of change in translation (log2 Fold Change) compared to the control condition was performed using edgeR (McCarthy et al., 2012).”
A Bayesian hierarchical model to detect differentially methylated loci from single nucleotide resolution sequencing data (2014, methodology): “To improve the performance of our method when the number of replicates is low, we employ a shrinkage approach; this strategy has previously been shown to improve detection of differential expression in microarray and RNA-seq studies (24,28,30,31).”
Pooling across cells to normalize single-cell RNA sequencing data with many zero counts (2016, methodology): “A GLM was fitted for each gene using a one-way layout with the two groups of interest [23].”