Swimming downstream: statistical analysis of differential transcript usage following Salmon quantification.
Swimming downstream: statistical analysis of differential transcript usage following Salmon quantification.
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DOI：
10.12688/f1000research.15398.3
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
Patro R
中科院分区：
文献类型：
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作者：
Love MI;Soneson C;Patro R
从RNA-seq数据检测差异转录物使用（DTU）是补充差异基因表达分析的重要生物信息学分析。在这里，我们提出了一个简单的工作流程，使用一组现有的R/Bioconductor包分析DTU。我们展示了如何使用Salmon软件包在RNA-seq定量的下游使用这些软件包。整个流水线速度很快，得益于Salmon在转录水平上量化表达的推理步骤。该工作流程包括使用DRIMSeq和DEXSeq进行分析的实时可运行代码块，以及使用stageR包执行DTU的两阶段测试，stageR包是一种统计框架，用于在基因水平上进行筛选，然后确认显着基因中的哪些转录本显示DTU的证据。我们评估这些软件包和其他相关的软件包在一个模拟的数据集与参数估计从真实的数据。
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.