SARTools: A DESeq2- and EdgeR-Based R Pipeline for Comprehensive Differential Analysis of RNA-Seq Data

TLDR

Multiple R packages exist for detecting differentially expressed genes from RNA‑Seq data, yet their pipelines differ in normalization, dispersion estimation, and differential testing, and the absence of mandatory quality control or awareness of non‑exchangeable normalization methods can produce spurious results. The authors therefore develop a dedicated pipeline that systematically incorporates quality control steps to prevent errors from misusing these methods.

Abstract

Several R packages exist for the detection of differentially expressed genes from RNA-Seq data. The analysis process includes three main steps, namely normalization, dispersion estimation and test for differential expression. Quality control steps along this process are recommended but not mandatory, and failing to check the characteristics of the dataset may lead to spurious results. In addition, normalization methods and statistical models are not exchangeable across the packages without adequate transformations the users are often not aware of. Thus, dedicated analysis pipelines are needed to include systematic quality control steps and prevent errors from misusing the proposed methods.