Analyzing Multifactorial RNA-Seq Experiments with DicoExpress

J Vis Exp. 2022 Jul 29:(185). doi: 10.3791/62566.

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.

Publication types

  • Video-Audio Media
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Exome Sequencing
  • Gene Expression Profiling* / methods
  • High-Throughput Nucleotide Sequencing* / methods
  • RNA-Seq
  • Sequence Analysis, RNA / methods
  • Software