Analyzing Multifactorial RNA-Seq Experiments with DicoExpress

被引:4
作者
Baudry, Kevin [1 ,2 ,3 ]
Paysant-Le Roux, Christine [1 ,2 ]
Colella, Stefano [4 ]
Castandet, Benoit [1 ,2 ]
Martin, Marie-Laure [1 ,2 ,5 ]
机构
[1] Univ Paris Saclay, CNRS, INRAE, Univ Evry,Inst Plant Sci Paris Saclay IPS2, Orsay, France
[2] Univ Paris, CNRS, INRAE, Inst Plant Sci Paris Saclay IPS2, Orsay, France
[3] Univ Paris Saclay, INRAE, CNRS, AgroParisTech,GQE Le Moulon, Gif Sur Yvette, France
[4] Univ Montpellier, LSTM, INRAE, IRD,CIRAD,Inst Agro, Montpellier, France
[5] Univ Paris Saclay, INRAE, AgroParisTech, UMR MIA Paris, Paris, France
来源
JOVE-JOURNAL OF VISUALIZED EXPERIMENTS | 2022年 / 185期
关键词
D O I
10.3791/62566
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
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.
引用
收藏
页数:13
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