整合定量不确定性的 RNA-seq 差异分析。
Differential analysis of RNA-seq incorporating quantification uncertainty.
机构信息
Department of Computer Science, University of California, Berkeley, Berkeley, California, USA.
Innovative Genomics Institute and Department of Molecular &Cell Biology, University of California, Berkeley, Berkeley, California, USA.
出版信息
Nat Methods. 2017 Jul;14(7):687-690. doi: 10.1038/nmeth.4324. Epub 2017 Jun 5.
We describe sleuth (http://pachterlab.github.io/sleuth), a method for the differential analysis of gene expression data that utilizes bootstrapping in conjunction with response error linear modeling to decouple biological variance from inferential variance. sleuth is implemented in an interactive shiny app that utilizes kallisto quantifications and bootstraps for fast and accurate analysis of data from RNA-seq experiments.
我们介绍了 sleuth（http://pachterlab.github.io/sleuth），这是一种用于基因表达数据分析的差异分析方法，它利用引导法结合响应误差线性建模来分离生物学方差和推断方差。sleuth 是在一个交互式 shiny 应用程序中实现的，该应用程序利用 kallisto 定量和引导来快速准确地分析 RNA-seq 实验数据。
Zotero 插件