Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Summary

DiCoExpress is a script-based tool implemented in R to perform an RNA-Seq analysis from quality control to co-expression. DiCoExpress handles complete and unbalanced design up to 2 biological factors. This video tutorial guides the user through the different features of DiCoExpress.

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.

Introduction

Next-generation RNA sequencing (RNA-Seq) technology is now the gold standard of transcriptome analysis¹. Since the early days of the technology, the combined efforts of bioinformaticians and biostatisticians have resulted in the development of numerous methods tackling all the essential steps of transcriptomic analyses, from mapping to transcript quantification². Most of the tools available today to the biologist are developed within the R software environment for statistical computing and graphs³, and many packages for biological data analysis are available in the Bioconductor repository

Protocol

1. DiCoExpress

1. Open a R studio session and set directory to Template_scripts.
2. Open the DiCoExpress_Tutorial.R script in R studio.
3. Load DiCoExpress functions in the R session with the following commands:
   > source("../Sources/Load_Functions.R")
   > Load_Functions()
   > Data_Directory = "../Data"
   > Results_Directory = "../Results/"
4. Load data files in the R session with the following commands:
   > Project_Name =.......

Discussion

Because RNA-Seq has become a ubiquitous method in biological studies, there is a constant need to develop versatile and user-friendly analytical tools. A critical step within most of the analytical workflows is often to identify with confidence the genes differentially expressed between biological conditions and/or treatments¹⁵. The production of reliable results requires proper statistical modeling, which has been the motivation for the development of DiCoExpress.

DiCo.......

Materials

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