Count-based differential expression analysis of RNA sequencing data using R and Bioconductor

By Simon Anders, Davis J. McCarthy, Yunsheng Chen, M. Okoniewski, Gordon K. Smyth, Wolfgang Huber, Mark D. Robinson. Nature Protocols, 2013

1,122 citations recorded by the source corpus.

RNA sequencing (RNA-seq) has been rapidly adopted for the profiling of transcriptomes in many areas of biology, including studies into gene regulation, development and disease. Of particular interest is the discovery of differentially expressed genes across different conditions (e.g., tissues, perturbations) while optionally adjusting for other systematic factors that affect the data-collection process. There are a number of subtle yet crucial aspects of these analyses, such as read counting, appropriate treatment of biological variability, quality control checks and appropriate setup of statistical modeling. Several variations have been presented in the literature, and there is a need for guidance on current best practices. This protocol presents a state-of-the-art computational and statistical RNA-seq differential expression analysis workflow largely based on the free open-source R language and Bioconductor software and, in particular, on two widely used tools, DESeq and edgeR. Hands-on time for typical small experiments (e.g., 4–10 samples) can be <1 h, with computation time <1 d using a standard desktop PC.

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Citations in the corpus

Classification of low quality cells from single-cell RNA-seq data (2016, methodology): “In addition, we determined differentially expressed genes using both the DESeq [32] and Piano [51] package available on Bioconductor.”
Unifying the analysis of high-throughput sequencing datasets: characterizing RNA-seq, 16S rRNA gene sequencing and selective growth experiments by compositional data analysis (2014, methodology): “That DESeq, DESeq2, SAMseq and baySeq identify features that become less abundant makes sense if the data are counts, but do not make sense if the data are compositional.”
featureCounts: an efficient general purpose program for assigning sequence reads to genomic features (2013, methodology): “Another common approach is to summarize counts at the gene-level, by counting all reads that overlap any exon for each gene (Anders et al. , 2013; Bhattacharyya et al. , 2013; Man et al. , 2013).”
Data quality aware analysis of differential expression in RNA-seq with NOISeq R/Bioc package (2015, background): “The estimation of gene expression is less reliable for low-count genes, which therefore represent a source of noise that negatively affects sensitivity and specificity in most DE analysis methods (20), hence the removal of low-count genes before further analysis is undertaken is recommended (40).”
It's DE-licious: A Recipe for Differential Expression Analyses of RNA-seq Experiments Using Quasi-Likelihood Methods in edgeR (2016, background): “We focus here on the quasi-likelihood features of edgeR rather than exact tests [8, 9] or likelihood ratio tests [6], because the latter have been described previously [10] and because the quasi-likelihood functions provide more robust and reliable error rate control when the number of replicates is”
Robustly detecting differential expression in RNA sequencing data using observation weights (2013, methodology): “It is clear that the most successful methods implement some form of ‘information sharing’ across the whole dataset to improve DE inference (2), and this becomes an intricate exercise to tradeoff power, false discovery control and protection against outliers.”
Qualimap 2: advanced multi-sample quality control for high-throughput sequencing data (2015, unknown): “The public repository of Qualimap is hosted at bitbucket.org/kokonech/qualimap .”
Modelling dropouts for feature selection in scRNASeq experiments (2016, methodology): “The ground truth differentially expressed genes were obtained for each dataset using three standard analysis methods: DESeq2 [43], edgeR [44], and limma-voom [45].”
SARTools: A DESeq2- and EdgeR-Based R Pipeline for Comprehensive Differential Analysis of RNA-Seq Data (2015, background): “Although these steps are strongly recommended by the authors of the packages [7], they can be skipped by the users without stopping the analysis process.”
RNA Sequencing and Analysis (2015, unknown): “Alternative approaches, such as HTSeq, can quantify expression without assembling transcripts by counting the number of reads that map to an exon (Anders et al. 2013).”
An Integrated Metabolic Atlas of Clear Cell Renal Cell Carcinoma (2016, unknown): “Separately, a crosscancer study of metabolic gene expression profiles further characterized ccRCC with concerted downregulation of most metabolic genes in comparison with other malignancies (Anders et al., 2013; Gatto et al., 2014).”
Fusobacterium nucleatum Promotes Chemoresistance to Colorectal Cancer by Modulating Autophagy (2017, methodology): “The RNA-seq data analysis was performed according to the TopHat- HTSeq-DeSeq2 frame (Anders et al., 2013).”