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Updated: Dec 24, 2025

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

ORdensity: user-friendly R package to identify differentially expressed genes.

José María Martínez-Otzeta¹, Itziar Irigoien¹, Basilio Sierra¹

¹Department of Computation Science and Artificial Intelligence, University of the Basque Country UPV/EHU, Donostia, Spain.

BMC Bioinformatics

|April 9, 2020

Summary

The ORdensity R package efficiently identifies differentially expressed genes from microarray data. This robust tool simplifies gene expression analysis for researchers, even those new to programming.

Keywords:

Differentially expressed gene Multivariate statistics Outlier Parallel implementation Quantile R package

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Last Updated: Dec 24, 2025

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Published on: September 18, 2021

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Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization

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Published on: October 3, 2025

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Microarray technology enables large-scale gene expression profiling.
Identifying differentially expressed genes is crucial for biological insight and disease understanding.
Efficient software is needed to analyze the growing volume of biomedical data.

Purpose of the Study:

To introduce ORdensity, an R package for identifying differentially expressed genes.
To implement a recent statistical methodology for gene expression analysis.
To discuss the advantages of parallel implementation in the package.

Main Methods:

Utilizes a recent methodology for identifying differentially expressed genes.
Implements parallel processing for improved computational efficiency.
The ORdensity package provides a user-friendly interface for analysis.

Main Results:

ORdensity successfully identifies differentially expressed genes in an accessible format.
Parallel execution significantly reduces run-time on standard hardware.
The package demonstrates robustness and suitability for gene expression analysis using simulated and real data.

Conclusions:

ORdensity offers a straightforward and user-friendly approach to identifying differentially expressed genes.
The package is particularly beneficial for users without extensive programming experience.
The methodology implemented is robust and suitable for practical applications in gene expression studies.