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Related Experiment Video

Updated: Sep 1, 2025

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

Analyzing Multifactorial RNA-Seq Experiments with DicoExpress.

Kevin Baudry¹, Christine Paysant-Le Roux², Stefano Colella³

¹Université 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.

Journal of Visualized Experiments : Jove

|August 15, 2022

Summary

This summary is machine-generated.

DiCoExpress offers a user-friendly R pipeline for RNA-Seq analysis, enabling beginners to perform differential and co-expression analyses. This tool simplifies complex statistical modeling for enhanced biological interpretation.

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Last Updated: Sep 1, 2025

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

Published on: September 18, 2021

Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples

Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples

Published on: June 8, 2020

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Next-generation sequencing (NGS) data analysis, particularly RNA-Seq, demands advanced statistical expertise.
Generalized linear models (GLMs) are increasingly favored for RNA-Seq differential analysis, while mixture models are beneficial for co-expression analysis.

Purpose of the Study:

To develop DiCoExpress, a standardized R pipeline simplifying RNA-Seq analysis for users with limited statistical or R programming knowledge.
To enable comprehensive RNA-Seq analysis, including quality control, differential expression, and co-expression analysis, through an accessible platform.

Main Methods:

Implementation of a standardized R pipeline within DiCoExpress.
Integration of generalized linear models for differential expression analysis using contrasts.
Inclusion of mixture models for co-expression analysis.
Development of an enrichment analysis module for differentially expressed and co-expressed gene sets.

Main Results:

DiCoExpress provides a managed environment for performing complex RNA-Seq statistical analyses.
The pipeline facilitates complete RNA-Seq analysis from quality control to enrichment analysis.
Users can conduct differential and co-expression analyses without deep statistical or R programming expertise.

Conclusions:

DiCoExpress empowers researchers, especially beginners, to effectively analyze RNA-Seq data and interpret biological findings.
The tool democratizes advanced statistical modeling techniques for RNA-Seq data analysis.
A step-by-step video tutorial enhances user adoption and maximizes the utility of DiCoExpress.