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Journal Articles Journal of visualized experiments : JoVE Year : 2022

Analyzing Multifactorial RNA-Seq Experiments with DicoExpress

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Abstract

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.
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Dates and versions

hal-03838630 , version 1 (03-11-2022)

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Cite

Kevin Baudry, Christine Paysant-Le Roux, Stefano Colella, Benoît Castandet, Martin-Magniette Marie-Laure. Analyzing Multifactorial RNA-Seq Experiments with DicoExpress. Journal of visualized experiments : JoVE, 2022, 185, ⟨10.3791/62566⟩. ⟨hal-03838630⟩
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