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Updated: Jun 19, 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

Assessing RNA-Seq Workflow Methodologies Using Shannon Entropy.

Nicolas Carels¹

¹Laboratory of Biological System Modeling, Center of Technological Development in Health (CDTS), Oswaldo Cruz Foundation (Fiocruz), Rio de Janeiro 21040-900, RJ, Brazil.

|July 26, 2024

Summary

This study identifies optimal RNA-sequencing (RNA-seq) workflows for preserving biological data. Combining TPM, RLE, and TMM normalization with log2 fold change best preserves biological facts in RNA-seq analysis.

Keywords:

5-year OS PPI network RPKM benchmarking cancer entropy median normalization

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Last Updated: Jun 19, 2025

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

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Published on: September 18, 2021

Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations

Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations

Published on: August 4, 2016

RNA Secondary Structure Prediction Using High-throughput SHAPE

RNA Secondary Structure Prediction Using High-throughput SHAPE

Published on: May 31, 2013

Area of Science:

Bioinformatics
Genomics
Computational Biology

Background:

RNA-sequencing (RNA-seq) data processing lacks standardized workflows, hindering reliable biological interpretation.
Existing methods vary in their ability to accurately capture biological signals from RNA-seq data.

Purpose of the Study:

To evaluate and identify the most effective RNA-seq workflow for preserving biological facts.
To assess the utility of Shannon entropy as a metric for optimizing RNA-seq analysis pipelines.

Main Methods:

Shannon entropy was employed to measure the biological status of RNA-seq data.
Multiple RNA-seq workflow approaches, including DESeq2 and edgeR, were analyzed.
Nine normalization methods combined with log2 fold change were tested on TCGA RNA-seq datasets from 515 patients across 12 cancer types.

Main Results:

TPM, RLE, and TMM normalization methods, when used with a log2 fold change threshold of ≥1 for differential gene expression, demonstrated superior performance.
These optimal combinations effectively preserved biological information within the RNA-seq datasets.

Conclusions:

Shannon entropy provides an objective metric for assessing and optimizing RNA-seq workflows.
The identified workflow (TPM, RLE, or TMM normalization with log2 fold change ≥1) enhances the accuracy of RNA-seq data analysis and mRNA sequencing technologies.