Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

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...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Study on the canopy structure and light distribution of <i>Hippophae rhamnoides</i> at different ages.

Frontiers in plant science·2026

Same author

A Diffusion-Based Time-Frequency Dual-Stream Contrastive Learning Model for Multivariate Time Series Anomaly Detection.

Entropy (Basel, Switzerland)·2026

Same author

Time-resolved transcriptomic analysis reveals key regulatory genes and auxin-responsive networks underlying axillary bud branching in <i>Hippophae rhamnoides</i>.

Frontiers in plant science·2026

Same author

Radioactivity levels of radiocesium and <sup>90</sup>Sr in the surface water of the low-latitude western-central North Pacific Ocean in 2017.

Marine pollution bulletin·2026

Same author

scDGCL: A Dual-Level and Graph-Constrained Contrastive Learning Method for Single-Cell RNA Sequencing Data Clustering.

IEEE transactions on computational biology and bioinformatics·2026

Same author

Trophic Transfer and Radiation Dose Assessment of Radionuclides in Biota from Southeastern Coastal China.

Journal of hazardous materials·2025

Same journal

STED: flexible cross-modal topic modeling infers cell-type-specific regulatory landscapes from bulk epigenomics.

Briefings in bioinformatics·2026

Same journal

A knowledge-guided deep learning framework for quantitative nucleic acid testing.

Briefings in bioinformatics·2026

Same journal

Optimal transport for label transfer in single-cell multi-omics integration.

Briefings in bioinformatics·2026

Same journal

Continuous multi-omics pathway enrichment analysis resolves hidden functional heterogeneity.

Briefings in bioinformatics·2026

Same journal

Evaluating completeness, coherence, and consistency of genome-scale function annotations.

Briefings in bioinformatics·2026

Same journal

Transformers for single-cell RNA sequencing: a survey.

Briefings in bioinformatics·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 8, 2025

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

Multi-level multi-view network based on structural contrastive learning for scRNA-seq data clustering.

Zhenqiu Shu¹, Min Xia¹, Kaiwen Tan¹

¹Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Chenggong, 650500, Yunnan, China.

Briefings in Bioinformatics

|November 4, 2024

Summary

This summary is machine-generated.

This study introduces a novel multi-level multi-view network (scMMN) for accurate single-cell RNA sequencing (scRNA-seq) data clustering. The method enhances clustering by using contrastive learning on multi-view data representations.

Keywords:

contrastive clustering deep views graph laplacian filter multi-level multi-view shallow views

More Related Videos

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

Published on: March 12, 2021

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Related Experiment Videos

Last Updated: Jun 8, 2025

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

Published on: March 12, 2021

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Area of Science:

Computational Biology
Bioinformatics
Genomics

Background:

Clustering is vital for analyzing single-cell RNA sequencing (scRNA-seq) data, aiding in cellular distribution studies.
High dimensionality and complexity of scRNA-seq data present significant challenges for accurate, singular-perspective clustering.

Purpose of the Study:

To propose a novel approach, multi-level multi-view network based on structural consistency contrastive learning (scMMN), for improved scRNA-seq data clustering.
To address the limitations of existing methods in handling complex, high-dimensional scRNA-seq data.

Main Methods:

Construct shallow views using k-nearest neighbor (kNN) and diffusion mapping (DM).
Generate deep views via graph Laplacian filters for representation learning.
Employ contrastive learning, including group contrastive loss and structural consistency contrastive loss, to enhance network discrimination.

Main Results:

The scMMN method demonstrated superior performance compared to state-of-the-art methods across eight real-world scRNA-seq datasets.
The proposed approach effectively handles the high dimensionality and complexity inherent in scRNA-seq data.

Conclusions:

The scMMN method offers a robust and effective solution for scRNA-seq data clustering.
The integration of multi-level multi-view learning and contrastive learning significantly improves clustering accuracy and discrimination ability.