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Updated: Oct 3, 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

Transcriptome analysis method based on differential distribution evaluation.

Yiwei Meng^1,2, Yanhong Huang^3,4, Xiao Chang⁵

¹State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai 200031, China.

Briefings in Bioinformatics

|February 12, 2022

Summary

This summary is machine-generated.

This study introduces Kullback-Leibler divergence-based differential distribution (klDD) to detect subtle gene expression changes. This method enhances understanding of biological mechanisms and disease progression.

Keywords:

cancer subtyping differential distribution genes disease early-warning signals gene expression potential disease modules single-cell clustering

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Last Updated: Oct 3, 2025

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

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Polysome Fractionation and Analysis of Mammalian Translatomes on a Genome-wide Scale

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Published on: May 17, 2014

Area of Science:

Genomics
Computational Biology
Biostatistics

Background:

Differential gene expression analysis is crucial for understanding biological processes and diseases.
Existing methods primarily focus on mean or variance shifts, potentially missing subtle distribution changes.

Purpose of the Study:

To introduce a novel approach, Kullback-Leibler divergence-based differential distribution (klDD), for quantifying gene expression changes.
To provide a flexible framework for detecting alterations in higher-order statistical information of genes.

Main Methods:

Utilized Kullback-Leibler divergence to quantify differences in gene expression distributions.
Developed the klDD method to capture changes beyond simple mean or variance shifts.

Main Results:

The klDD method effectively identifies subtle differences in gene expression distributions.
Demonstrated superior performance compared to methods relying solely on mean or variance shifts.
Validated klDD's ability to detect informational genes based on differential distribution.

Conclusions:

klDD offers a robust framework for analyzing gene expression dynamics.
The method has broad applications in cancer subtyping, single-cell clustering, and early disease detection.
Validated findings across diverse benchmark datasets underscore klDD's utility.