From reads to genes to pathways: differential expression analysis of RNA-Seq experiments using Rsubread and the edgeR quasi-likelihood pipeline

By Yunshun Chen, Aaron T. L. Lun, Gordon K. Smyth. F1000Research, 2016

1,215 citations recorded by the source corpus.

In recent years, RNA sequencing (RNA-seq) has become a very widely used technology for profiling gene expression. One of the most common aims of RNA-seq profiling is to identify genes or molecular pathways that are differentially expressed (DE) between two or more biological conditions. This article demonstrates a computational workflow for the detection of DE genes and pathways from RNA-seq data by providing a complete analysis of an RNA-seq experiment profiling epithelial cell subsets in the mouse mammary gland. The workflow uses R software packages from the open-source Bioconductor project and covers all steps of the analysis pipeline, including alignment of read sequences, data exploration, differential expression analysis, visualization and pathway analysis. Read alignment and count quantification is conducted using the Rsubread package and the statistical analyses are performed using the edgeR package. The differential expression analysis uses the quasi-likelihood functionality of edgeR.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/7fa6888009b435efa79b1ce5f3b2d803f129702a

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Citations in the corpus

The R package Rsubread is easier, faster, cheaper and better for alignment and quantification of RNA sequencing reads (2018, methodology): “Rsubread allows RNA-seq data analyses, from raw sequence reads to scientific results, to be conducted entirely in R [21, 22].”
Heavy-tailed prior distributions for sequence count data: removing the noise and preserving large differences (2018, methodology): “The authors of edgeR [7] and limma-voom [12] suggest using a ﬁltering rule that removes genes with low scaled counts before statistical analysis [14].”
RNA Sequencing Data: Hitchhiker's Guide to Expression Analysis (2018, methodology): “Implementations differ; for example, DESeq2 replaces the composite null with a simple null hypothesis at the boundary of the parameter space [128] ; edgeR uses a modified likelihood ratio test or a quasilikelihood Ftest against a threshold [156] .”
edgeR v4: powerful differential analysis of sequencing data with expanded functionality and improved support for small counts and larger datasets (2024, neutral): “Panels (b), (c) and (d) display a BCV plot, a quasi-dispersion plot and a mean-difference (MD) plot, respectively, from the RNA-seq differential expression analysis described by Chen et al. [ 10 ].”