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...

Cluster Sampling Method

Cluster Sampling Method

Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...

You might also read

Related Articles

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

Sort by

Same author

Exploring Complex Genetic Mechanisms in Brain Imaging Genetics via a New Multi-task Learning Method.

IEEE transactions on computational biology and bioinformatics·2026

Same author

stDGCN: A dual-augmentation graph convolutional network for identifying spatial domains with attention mechanism.

IEEE journal of biomedical and health informatics·2026

Same author

MVCL: A Contrastive Learning Model with Multi-view Networks for Driver Gene Prediction.

IEEE journal of biomedical and health informatics·2026

Same author

Single-cell transcriptomic atlas-guided NRG3-ERBB4 axis enhances the efficacy of chemo-immunotherapy during induction phase in high-risk neuroblastoma.

Molecular therapy : the journal of the American Society of Gene Therapy·2026

Same author

scDEBGCL: a deep embedding approach based on bipartite graph contrastive learning for single-cell RNA-seq data.

BMC biology·2026

Same author

Integrated analysis identifies a CXCR6/CD45/PD-1-based risk model for melanoma prognosis and intratumoral CD8+/CD69 + T-cell infiltration correlation.

Cellular oncology (Dordrecht, Netherlands)·2026

Same journal

MCFST: Spatial domain identification method based on multi-view graph convolutional network and graph fusion network.

Bioinformatics (Oxford, England)·2026

Same journal

SpaBiT: Enhancing Spatial Transcriptomics Resolution via Bidirectional Attention Transformers.

Bioinformatics (Oxford, England)·2026

Same journal

EDEL: Enhancing Dense Retrievers for Curation of Biomedical Knowledge Bases.

Bioinformatics (Oxford, England)·2026

Same journal

Informative Relational Learning for Adverse Reaction Prediction with Enhanced Generalization to Novel Drugs.

Bioinformatics (Oxford, England)·2026

Same journal

An interpretable deep learning framework uncovers features governing CRISPR-Cas9 genome-editing efficiency.

Bioinformatics (Oxford, England)·2026

Same journal

3DICE: Interpretable 3D Cross-Modal Learning for Drug-Target Interaction Prediction and Large-Scale Drug Discovery.

Bioinformatics (Oxford, England)·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Sep 8, 2025

Author Spotlight: A Computational Pipeline for Analyzing Chimeric Noncoding RNA-Target RNA Interactions in High-Throughput Sequencing Data

Author Spotlight: A Computational Pipeline for Analyzing Chimeric Noncoding RNA-Target RNA Interactions in High-Throughput Sequencing Data

Published on: December 1, 2023

scCNC: a method based on capsule network for clustering scRNA-seq data.

Hai-Yun Wang¹, Jian-Ping Zhao^1,2, Chun-Hou Zheng^1,3

¹College of Mathematics and System Sciences, Xinjiang University, Urumqi 830046, China.

Bioinformatics (Oxford, England)

|June 14, 2022

Summary

This summary is machine-generated.

This study introduces scCNC, a semi-supervised clustering method for single-cell RNA sequencing (scRNA-seq) data. scCNC integrates domain knowledge to improve cell type assignment and downstream analysis, overcoming limitations of unsupervised methods.

More Related Videos

Identification of Circular RNAs using RNA Sequencing

Identification of Circular RNAs using RNA Sequencing

Published on: November 14, 2019

Isolation of Adult Spinal Cord Nuclei for Massively Parallel Single-nucleus RNA Sequencing

Isolation of Adult Spinal Cord Nuclei for Massively Parallel Single-nucleus RNA Sequencing

Published on: October 12, 2018

Related Experiment Videos

Last Updated: Sep 8, 2025

Author Spotlight: A Computational Pipeline for Analyzing Chimeric Noncoding RNA-Target RNA Interactions in High-Throughput Sequencing Data

Author Spotlight: A Computational Pipeline for Analyzing Chimeric Noncoding RNA-Target RNA Interactions in High-Throughput Sequencing Data

Published on: December 1, 2023

Identification of Circular RNAs using RNA Sequencing

Identification of Circular RNAs using RNA Sequencing

Published on: November 14, 2019

Isolation of Adult Spinal Cord Nuclei for Massively Parallel Single-nucleus RNA Sequencing

Isolation of Adult Spinal Cord Nuclei for Massively Parallel Single-nucleus RNA Sequencing

Published on: October 12, 2018

Area of Science:

Computational Biology
Bioinformatics
Genomics

Background:

Clustering is vital for single-cell RNA sequencing (scRNA-seq) analysis.
Unsupervised methods often fail to produce biologically interpretable clusters due to high dimensionality and dropout events in scRNA-seq data.
Existing methods do not leverage gold-standard labels or domain knowledge.

Purpose of the Study:

To propose a novel semi-supervised clustering method for scRNA-seq data.
To integrate domain knowledge into the clustering process for improved cell type assignment.
To enhance the biological interpretability of scRNA-seq clustering.

Main Methods:

Developed scCNC, a semi-supervised clustering method utilizing a capsule network.
Introduced a Semi-supervised Greedy Iterative Training method for network training.
Applied the method to real-world scRNA-seq datasets.

Main Results:

scCNC significantly improves clustering performance on scRNA-seq datasets.
The method facilitates more accurate downstream analyses and cell type assignment.
Integration of domain knowledge enhances biological interpretability.

Conclusions:

scCNC offers a powerful approach to semi-supervised clustering in scRNA-seq analysis.
The proposed method addresses limitations of purely unsupervised techniques.
scCNC improves the biological relevance and utility of scRNA-seq data analysis.