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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: Jan 15, 2026

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

Differential detection workflows for multi-sample single-cell RNA-seq data.

Jeroen Gilis^1,2, Laura Perin³, Milan Malfait¹

¹Department of Mathematics, Computer science and Statistics, Ghent University, Krijgslaan 281, Ghent, 9000, Belgium.

|October 6, 2025

Summary

This summary is machine-generated.

This study introduces differential detection (DD) workflows for single-cell RNA sequencing (scRNA-seq) to analyze gene expression fractions. Joint analysis of DE and DD offers complementary insights for gene discovery and functional interpretation.

Keywords:

benchmarking pseudobulk aggregation scRNAseq data analysis Presence/Absence

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Last Updated: Jan 15, 2026

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Characterization of In Vitro Differentiation of Human Primary Keratinocytes by RNA-Seq Analysis

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Published on: May 16, 2020

Area of Science:

Computational Biology
Genomics
Bioinformatics

Background:

Single-cell RNA sequencing (scRNA-seq) enables gene expression analysis at a cellular level.
Current differential expression (DE) tools focus on average expression, potentially missing other distributional differences.

Purpose of the Study:

To develop and evaluate workflows for differential detection (DD) in scRNA-seq data.
To create a unified approach for jointly analyzing DE and DD.
To assess the complementary information provided by joint DE and DD analyses.

Main Methods:

Benchmarking of eight differential detection (DD) strategies.
Development of a unified workflow for joint DE and DD analysis.
Validation using simulations and two case studies.

Main Results:

Differential detection (DD) infers differences in the fraction of cells with detected expression.
Joint DE and DD analyses provide complementary information beyond average expression.
The unified workflow enhances gene discovery and functional interpretation.

Conclusions:

The developed DD workflows offer novel insights into scRNA-seq data.
Joint analysis of DE and DD is crucial for a comprehensive understanding of gene expression.
This approach improves the interpretation of scRNA-seq experiments.