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RNA-seq

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases.
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...

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Updated: Oct 18, 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

scDEA: differential expression analysis in single-cell RNA-sequencing data via ensemble learning.

Hui-Sheng Li¹, Le Ou-Yang², Yuan Zhu³

¹School of Mathematics and Statistics, Central China Normal University, China.

Briefings in Bioinformatics

|September 27, 2021

Summary

This summary is machine-generated.

We developed scDEA, an ensemble learning method for single-cell RNA sequencing (scRNA-seq) data analysis. It integrates multiple methods to identify differentially expressed genes more accurately and stably.

Keywords:

differential expression analysis ensemble learning scRNA-seq

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Last Updated: Oct 18, 2025

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

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Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

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Published on: July 29, 2022

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Differential gene expression analysis is vital for single-cell RNA sequencing (scRNA-seq) data interpretation.
Existing methods yield diverse results and their performance varies with data structure.

Purpose of the Study:

To introduce scDEA, a novel ensemble learning approach for robust differential gene expression detection in scRNA-seq data.
To enhance the stability and accuracy of identifying differentially expressed genes.

Main Methods:

scDEA employs an ensemble learning strategy.
It integrates P-values from 12 individual differential expression analysis tools using a P-value combination technique.

Main Results:

scDEA demonstrated superior performance compared to state-of-the-art individual methods.
Experimental results confirmed scDEA's effectiveness across various settings and metrics.

Conclusions:

scDEA offers a more stable and accurate solution for differential gene expression analysis in scRNA-seq.
The method is beneficial for biologists, bioinformaticians, and data scientists.