RNA-seq 数据差异基因表达结果的解读：综述与整合。
Interpretation of differential gene expression results of RNA-seq data: review and integration.
机构信息
Department of Mathematics and Statistics, South Dakota State University, Brookings, SD, USA.
Department of Biology and Microbiology, South Dakota State University, SD, USA.
出版信息
Brief Bioinform. 2019 Nov 27;20(6):2044-2054. doi: 10.1093/bib/bby067.
Abstract
Differential gene expression (DGE) analysis is one of the most common applications of RNA-sequencing (RNA-seq) data. This process allows for the elucidation of differentially expressed genes across two or more conditions and is widely used in many applications of RNA-seq data analysis. Interpretation of the DGE results can be nonintuitive and time consuming due to the variety of formats based on the tool of choice and the numerous pieces of information provided in these results files. Here we reviewed DGE results analysis from a functional point of view for various visualizations. We also provide an R/Bioconductor package, Visualization of Differential Gene Expression Results using R, which generates information-rich visualizations for the interpretation of DGE results from three widely used tools, Cuffdiff, DESeq2 and edgeR. The implemented functions are also tested on five real-world data sets, consisting of one human, one Malus domestica and three Vitis riparia data sets.
摘要
差异基因表达（DGE）分析是 RNA 测序（RNA-seq）数据最常见的应用之一。该过程可阐明两个或更多条件下差异表达的基因，广泛应用于 RNA-seq 数据分析的许多应用中。由于基于所选工具的格式的多样性以及这些结果文件中提供的大量信息，DGE 结果的解释可能是非直观且耗时的。在这里，我们从各种可视化的功能角度回顾了 DGE 结果分析。我们还提供了一个 R/Bioconductor 包，即使用 R 可视化差异基因表达结果，它为三个广泛使用的工具（Cuffdiff、DESeq2 和 edgeR）的 DGE 结果解释生成信息丰富的可视化。所实现的功能还在五个真实数据集上进行了测试，其中包括一个人类、一个苹果和三个河岸葡萄数据集。
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