Search research articles

Related Concept Videos

You might also read

Related Articles

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

Sort by

Same author

AcImpute: a constraint-enhancing smooth-based approach for imputing single-cell RNA sequencing data.

Bioinformatics (Oxford, England)·2025

Same author

Improved YOLOv7 Algorithm for Detecting Bone Marrow Cells.

Sensors (Basel, Switzerland)·2023

Same author

Identifying cell types from single-cell data based on similarities and dissimilarities between cells.

BMC bioinformatics·2021

Same journal

Correction to "Mathematical Modelling of COVID-19 Transmission in Kenya: A Model with Reinfection Transmission Mechanism".

Computational and mathematical methods in medicine·2025

Same journal

RETRACTION: Ligustrazine Inhibits Lung Phosphodiesterase Activity in a Rat Model of Allergic Asthma.

Computational and mathematical methods in medicine·2025

Same journal

RETRACTION: Delivery of miR-224-5p by Exosomes from Cancer-Associated Fibroblasts Potentiates Progression of Clear Cell Renal Cell Carcinoma.

Computational and mathematical methods in medicine·2025

Same journal

RETRACTION: Empirical Analysis of the Nursing Effect of Intelligent Medical Internet of Things in Postoperative Osteoarthritis.

Computational and mathematical methods in medicine·2025

Same journal

RETRACTION: Evaluation and Analysis of the Intervention Effect of Systematic Parent Training Based on Computational Intelligence on Child Autism.

Computational and mathematical methods in medicine·2024

Same journal

RETRACTION: Humanistic Spirit Training of Medical Students Based on Multisource Medical Data Fusion.

Computational and mathematical methods in medicine·2024

See all related 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

Search research articles

Related Experiment Video

Updated: Oct 17, 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

SCMAG: A Semisupervised Single-Cell Clustering Method Based on Matrix Aggregation Graph Convolutional Neural Network.

Haonan Peng¹, Wei Fan¹, Chujie Fang¹

¹School of Mathematics and Physics, Wuhan Institute of Technology, 430205 Wuhan, China.

Computational and Mathematical Methods in Medicine

|October 14, 2021

Summary

This summary is machine-generated.

This study introduces SCMAG, a novel semisupervised single-cell clustering method. SCMAG effectively utilizes prior cell information to improve the accuracy of cell type identification in scRNA-seq data.

More Related Videos

Author Spotlight: Investigating Cellular and Molecular Dynamics During Muscle Regeneration Using Cutting-Edge Single-Cell Technologies

Author Spotlight: Investigating Cellular and Molecular Dynamics During Muscle Regeneration Using Cutting-Edge Single-Cell Technologies

Published on: December 1, 2023

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

Published on: January 16, 2019

Related Experiment Videos

Last Updated: Oct 17, 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

Author Spotlight: Investigating Cellular and Molecular Dynamics During Muscle Regeneration Using Cutting-Edge Single-Cell Technologies

Author Spotlight: Investigating Cellular and Molecular Dynamics During Muscle Regeneration Using Cutting-Edge Single-Cell Technologies

Published on: December 1, 2023

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

Published on: January 16, 2019

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Clustering analysis is crucial for single-cell data mining, aiding in gene sequence division, functional gene identification, and new cell type detection.
Unsupervised clustering methods, while not requiring labels, are sensitive to data distribution and hyperparameter settings, impacting effectiveness.
Leveraging known cell types (prior information) can significantly enhance clustering accuracy.

Purpose of the Study:

To propose SCMAG (semisupervised single-cell clustering method based on a matrix aggregation graph convolutional neural network), a novel approach for single-cell data analysis.
To develop a method that effectively incorporates prior information into the clustering process for improved accuracy.
To evaluate the performance of SCMAG against existing semisupervised clustering algorithms.

Main Methods:

Development of SCMAG, a semisupervised clustering method utilizing a matrix aggregation graph convolutional neural network.
Integration of prior cell information into the graph convolutional neural network framework.
Testing and comparison of SCMAG on diverse single-cell datasets, including real scRNA-seq data.

Main Results:

SCMAG demonstrates superior performance in recognizing cell types compared to current semisupervised clustering algorithms.
The proposed method achieves higher accuracy and significance in cell type identification across various scRNA-seq datasets.
Evaluation confirms the effectiveness of incorporating prior information for enhanced clustering outcomes.

Conclusions:

SCMAG represents a significant advancement in semisupervised single-cell clustering.
The method provides a more accurate and robust approach for cell type identification by leveraging prior biological knowledge.
SCMAG offers a valuable tool for single-cell data mining and biological discovery.