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

Abstract

BackgroundSeveral 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.ResultsSARTools is an R pipeline for differential analysis of RNA-Seq count data. It can handle designs involving two or more conditions of a single biological factor with or without a blocking factor (such as a batch effect or a sample pairing). It is based on DESeq2 and edgeR and is composed of an R package and two R script templates (for DESeq2 and edgeR respectively). Tuning a small number of parameters and executing one of the R scripts, users have access to the full results of the analysis, including lists of differentially expressed genes and a HTML report that (i) displays diagnostic plots for quality control and model hypotheses checking and (ii) keeps track of the whole analysis process, parameter values and versions of the R packages used.ConclusionsSARTools provides systematic quality controls of the dataset as well as diagnostic plots that help to tune the model parameters. It gives access to the main parameters of DESeq2 and edgeR and prevents untrained users from misusing some functionalities of both packages. By keeping track of all the parameters of the analysis process it fits the requirements of reproducible research.

Identification of key differentially expressed MicroRNAs in cancer patients through pan-cancer analysis

“…[38]. However, as more stable and mature tools, DESeq2 [28] and edgeR [26] have been prevalent in expression research [39]. In this study, we utilized both DESeq2 and edgeR to perform miRNA differential expression analysis in 14 cancer types, applying statistical tests to minimize potential false positive errors resulting from the differences among individuals and samples.…”

The other white‐nose syndrome transcriptome: Tolerant and susceptible hosts respond differently to the pathogen Pseudogymnoascus destructans

“…We used the Trinity contig_ExN50_statistic perl script to calculate the ExN50 statistic. We identified differentially expressed transcripts between the control and exposed sample groups using SARTools v.1.3.0 (Varet, Coppée, & Dillies, 2016), which streamlined the DESeq2 v.1.12.3 (Love, Huber, & Anders, 2014) and edgeR v3.14.0 (Robinson, McCarthy, & Smyth, 2010) analyses. The SARTools‐based DESeq2 settings included cooksCutoff = TRUE (perform outliers detection), independentFiltering = TRUE, alpha = 0.05 (threshold of statistical significance), pAdjustMethod = BH (benjamini hochberg p ‐value adjustment method; Benjamini and Hochberg, 1995), and locfunc = median (estimate size factors).…”

Adaptation of Propionibacterium freudenreichii to long-term survival under gradual nutritional shortage

“…Finaly, non coding RNA validated by Rfam and CDS were counted with htseq-count [40] using the ABIMS Roscoff platform. A list of differentially expressed genes was generated using an R software package: SARtools [41], embedded Deseq2 [42] and EdgeR [43] (modified t -test adjusted, Pvalues < 0.05). A comparison of the results obtained with either Deseq2 or EdgeR produced a more exhaustive list using EdgeR.…”