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...

Cell Specific Gene Expression

Cell Specific Gene Expression

Multicellular organisms contain a variety of structurally and functionally distinct cell types, but the DNA in all the cells originated from the same parent cells. The differences in the cells can be attributed to the differential gene expression. Liver cells, whose functions include detoxification of blood, production of bile to metabolize fats, and synthesis of proteins essential for metabolism, must express a specific set of genes to perform their functions. Gene expression also varies with...

Cell Specific Gene Expression

Cell Specific Gene Expression

You might also read

Related Articles

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

Sort by

Same author

Deep Unsupervised Domain Adaptation for Translating Cancer Dependency Maps From Cell Lines to Breast Cancer Tumor Genomics.

Genetic epidemiology·2026

Same author

Heterozygous Nonsense Mutation in the Nuclear Transport Factor <i>KPNA7</i>, a Maternal Factor Active in Embryonic Tissues, Causes Autosomal Dominant Otosclerosis.

International journal of molecular sciences·2026

Same author

Systemic and Local Adiposity in the Bone Marrow Microenvironment Associated With Improved Prognosis in Hodgkin Lymphoma: Imaging and Molecular Analysis.

International journal of cancer·2026

Same author

Generative AI for the Design of Molecules: Advances and Challenges.

Journal of chemical information and modeling·2025

Same author

MolGraph-xLSTM as a graph-based dual-level xLSTM framework for enhanced molecular representation and interpretability.

Communications chemistry·2025

Same author

Integrative analysis of taste genetics and the dental plaque microbiome in early childhood caries.

Cell reports·2025

Search research articles

Related Experiment Video

Updated: Apr 7, 2026

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

A copula-infused graph neural network for cell type classification in single-cell RNA sequencing data.

Shijie Min¹, Leann Lac^2,3, Pingzhao Hu^1,2,4,5

¹Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada.

Computational and Structural Biotechnology Journal

|April 6, 2026

Summary

This summary is machine-generated.

We developed scCopulaGNN, a novel graph neural network method for cell type classification using single-cell RNA sequencing data. This approach effectively handles complex gene dependencies and cell relationships for improved biological insights.

Keywords:

Cell type classification Copula theory Graph neural networks ScCopulaGNN Single-cell RNA sequencing

More Related Videos

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

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: Apr 7, 2026

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

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

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
Genomics
Bioinformatics

Background:

Single-cell RNA sequencing (scRNA-seq) enables the study of cellular heterogeneity.
scRNA-seq data presents challenges due to high dimensionality, sparsity, and noise.
Accurate cell-type classification is crucial for understanding biological mechanisms.

Purpose of the Study:

To introduce scCopulaGNN, a novel copula-infused graph neural network for single-cell type classification.
To address the computational and statistical challenges in scRNA-seq data analysis.
To improve the accuracy and efficiency of cell type classification.

Main Methods:

Developed scCopulaGNN, integrating copula theory with graph neural networks (GNNs).
Copula framework captures complex gene dependencies.
GNN models structural relationships among cells.
Evaluated on real and simulated high-dimensional scRNA-seq datasets.

Main Results:

scCopulaGNN demonstrates robust performance on high-dimensional scRNA-seq data.
The model effectively handles complex gene dependencies and cell relationships.
Comparative analysis shows superior performance against existing methods for cell type classification.

Conclusions:

scCopulaGNN is a powerful tool for cell type classification in single-cell transcriptomics.
The method enhances understanding of cellular diversity and function.
This approach offers a significant advancement in analyzing complex biological data.