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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: Sep 10, 2025

Single-cell RNA-Seq of Defined Subsets of Retinal Ganglion Cells

Single-cell RNA-Seq of Defined Subsets of Retinal Ganglion Cells

Published on: May 22, 2017

Deep clustering of single-cell RNA-seq using adversarial graph contrastive learning.

Le Van Vinh¹, Tran Nhat Quang², Lai Hoang Hiep¹

¹Faculty of Information Technology, HCMC University of Technology and Education, Vo Van Ngan Street, Ho Chi Minh City 700000, Vietnam.

Briefings in Bioinformatics

|August 21, 2025

Summary

This study introduces scAGCL, a novel method for single-cell RNA sequencing (scRNA-seq) data clustering. scAGCL uses adversarial graph contrastive learning to improve cell type classification and identify marker genes, outperforming existing algorithms.

Keywords:

adversarial attack clustering contrastive learning graph neural network single-cell RNA-seq

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Last Updated: Sep 10, 2025

Single-cell RNA-Seq of Defined Subsets of Retinal Ganglion Cells

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Published on: May 22, 2017

Reconstruction of Single-Cell Innate Fluorescence Signatures by Confocal Microscopy

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Published on: May 27, 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:

Computational Biology
Genomics
Bioinformatics

Background:

Single-cell technologies provide cellular resolution for biological insights.
Accurate cell type classification is crucial for analyzing single-cell RNA sequencing (scRNA-seq) data.
Existing scRNA-seq clustering methods face computational challenges due to data characteristics.

Purpose of the Study:

To propose a novel method for scRNA-seq data clustering using adversarial graph contrastive learning.
To enhance cell type classification accuracy and scalability in scRNA-seq data analysis.
To develop a method that can also support marker gene identification for cell types.

Main Methods:

The proposed method, scAGCL, constructs a cell-cell graph.
It employs adversarial graph contrastive learning on graph structures and node features for representation learning.
A subgraph sampling technique is incorporated to improve scalability.

Main Results:

scAGCL demonstrates superior performance compared to seven state-of-the-art algorithms on real scRNA-seq datasets.
The method effectively clusters cells into distinct types.
scAGCL aids in the identification of marker genes crucial for defining cell types.

Conclusions:

scAGCL offers an effective and scalable approach for scRNA-seq data clustering.
The adversarial graph contrastive learning framework enhances cell type classification.
The method contributes to a deeper understanding of cellular heterogeneity and gene expression patterns.