Université Paris-Saclay, CNRS, INRAE, Univ Evry, Institute of Plant Sciences Paris-Saclay (IPS2), Orsay, France; Université de Paris, CNRS, INRAE, Institute of Plant Sciences Paris Saclay (IPS2), Orsay, France; Université Paris-Saclay, INRAE, CNRS, AgroParisTech, GQE - Le Moulon, Gif-sur-Yvette, France.
Université Paris-Saclay, CNRS, INRAE, Univ Evry, Institute of Plant Sciences Paris-Saclay (IPS2), Orsay, France; Université de Paris, CNRS, INRAE, Institute of Plant Sciences Paris Saclay (IPS2), Orsay, France.
J Vis Exp. 2022 Jul 29(185). doi: 10.3791/62566.
The proper use of statistical modeling in NGS data analysis requires an advanced level of expertise. There has recently been a growing consensus on using generalized linear models for differential analysis of RNA-Seq data and the advantage of mixture models to perform co-expression analysis. To offer a managed setting to use these modeling approaches, we developed DiCoExpress that provides a standardized R pipeline to perform an RNA-Seq analysis. Without any particular knowledge in statistics or R programming, beginners can perform a complete RNA-Seq analysis from quality controls to co-expression through differential analysis based on contrasts inside a generalized linear model. An enrichment analysis is proposed both on the lists of differentially expressed genes, and the co-expressed gene clusters. This video tutorial is conceived as a step-by-step protocol to help users take full advantage of DiCoExpress and its potential in empowering the biological interpretation of an RNA-Seq experiment.
正确使用统计建模进行 NGS 数据分析需要高级别的专业知识。最近，人们越来越一致地认为，广义线性模型可用于 RNA-Seq 数据的差异分析，混合模型则可用于共表达分析。为了提供使用这些建模方法的管理环境，我们开发了 DiCoExpress，它提供了一个标准化的 R 管道，可执行 RNA-Seq 分析。无需任何统计学或 R 编程方面的特定知识，初学者就可以通过广义线性模型中的对比，从质量控制到共表达，再到差异分析，完成完整的 RNA-Seq 分析。还对差异表达基因列表和共表达基因簇进行了富集分析。本视频教程旨在提供一个逐步的方案，帮助用户充分利用 DiCoExpress 及其在增强 RNA-Seq 实验的生物学解释方面的潜力。
