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

TL;DR

This paper provides a comprehensive workflow for differential expression analysis of RNA-seq data using R and Bioconductor, emphasizing best practices and practical implementation with DESeq and edgeR.

Contribution

It offers a detailed, practical protocol for RNA-seq differential expression analysis, integrating current best practices with open-source tools.

Findings

Efficient analysis workflow for small RNA-seq experiments

Guidance on data quality control and statistical modeling

Implementation using DESeq and edgeR tools

Abstract

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 critical 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, two widely-used tools DESeq and edgeR. Hands-on time for typical small experiments (e.g., 4-10 samples) can be <1 hour, with computation time <1 day using a standard desktop PC.