Love Michael I, Soneson Charlotte, Patro Rob
Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27516, USA.
Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27516, USA.
F1000Res. 2018 Jun 27;7:952. doi: 10.12688/f1000research.15398.3. eCollection 2018.
Detection of differential transcript usage (DTU) from RNA-seq data is an important bioinformatic analysis that complements differential gene expression analysis. Here we present a simple workflow using a set of existing R/Bioconductor packages for analysis of DTU. We show how these packages can be used downstream of RNA-seq quantification using the Salmon software package. The entire pipeline is fast, benefiting from inference steps by Salmon to quantify expression at the transcript level. The workflow includes live, runnable code chunks for analysis using DRIMSeq and DEXSeq, as well as for performing two-stage testing of DTU using the stageR package, a statistical framework to screen at the gene level and then confirm which transcripts within the significant genes show evidence of DTU. We evaluate these packages and other related packages on a simulated dataset with parameters estimated from real data.
从RNA测序数据中检测差异转录本使用情况（DTU）是一项重要的生物信息学分析，它是对差异基因表达分析的补充。在此，我们展示了一个使用一组现有R/Bioconductor软件包进行DTU分析的简单工作流程。我们展示了如何在使用Salmon软件包进行RNA测序定量分析之后使用这些软件包。整个流程速度很快，受益于Salmon的推理步骤，可以在转录本水平上对表达进行定量。该工作流程包括用于使用DRIMSeq和DEXSeq进行分析的实时可运行代码块，以及用于使用stageR软件包进行DTU两阶段测试的代码块，stageR是一个统计框架，用于在基因水平上进行筛选，然后确认显著基因中的哪些转录本显示出DTU的证据。我们在一个模拟数据集上评估了这些软件包和其他相关软件包，该数据集的参数是根据实际数据估计的。
