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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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Related Experiment Video

Updated: Jul 6, 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

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

Jeroen Gilis^1,2,3,4, Laura Perin^1,5, Milan Malfait²

¹These authors contributed equally.

Biorxiv : the Preprint Server for Biology

|January 8, 2024

Summary

This summary is machine-generated.

This study introduces differential detection (DD) analysis for single-cell transcriptomics, complementing traditional differential gene expression (DE) analysis. DD identifies genes with altered detection rates, offering new insights into gene regulation and cell function.

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Related Experiment Videos

Last Updated: Jul 6, 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

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

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

Published on: May 16, 2020

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

Area of Science:

Single-cell transcriptomics
Computational biology
Genomics

Background:

Differential gene expression (DE) analysis is standard in single-cell transcriptomics, focusing on average expression differences.
Single-cell data also offers potential to identify genes with altered expression distributions, not just averages.

Conclusions:

Differential detection analysis is a valuable addition to single-cell transcriptomics.
Combining DE and DD provides a more comprehensive view of gene expression patterns.
This approach improves the functional interpretation of single-cell data.