Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

DNA Microarrays02:34

DNA Microarrays

17.2K
Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...
17.2K
Neural Circuits01:25

Neural Circuits

1.1K
Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
1.1K

You might also read

Related Articles

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

Sort by
Same author

Development of a CRISPRi system in <i>Fusarium fujikuroi</i> and its application in gibberellic acid production.

Engineering microbiology·2026
Same author

Zero-Fluoroscopy Cryoballoon Ablation for Paroxysmal Atrial Fibrillation in a Patient With Dextrocardia: A Case Report.

Pacing and clinical electrophysiology : PACE·2026
Same author

An Assisting Contact Electrification Strategy for Achieving Self-Recoverable Mechanoluminescence.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026
Same author

A Machine Learning Approach to Prioritize Place-Based Prevention to Address Cardiovascular Disease Burden in New York City.

American journal of preventive medicine·2026
Same author

Atomic-scale ordering enables intrinsic bioactivity and rapid osseointegration in medium-entropy alloys.

Bioactive materials·2026
Same author

Concave and Convex Molecular Curvature Modulates Spatial Electronic Environments for Controlled Electrocatalysis.

Journal of the American Chemical Society·2026
Same journal

Multi-omics analysis identifies loci associated with pyrethroid resistance across sister species in the Anopheles gambiae species complex.

BMC genomics·2026
Same journal

Comparative and population genomics analyses of eared pheasants inhabiting highly varying altitudes.

BMC genomics·2026
Same journal

Identification of differentially expressed lncRNAs in different daily weight gains of Jiangquan black pigs and functional analysis of LOC100518120.

BMC genomics·2026
Same journal

A self-attention-based deep learning model for identifying key genes in insect pupal metamorphosis.

BMC genomics·2026
Same journal

Multiple genomic approaches reveal geographic structure and local selection signals in invasive Anopheles stephensi from the Horn of Africa and Yemen.

BMC genomics·2026
Same journal

Integration of bulk RNA-seq and scRNA-seq reveals cell subsets and gene signatures associated with Glaesserella parasuis infection.

BMC genomics·2026
See all related articles

Related Experiment Video

Updated: Jun 6, 2025

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection
09:19

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Published on: July 6, 2022

4.8K

Deciphering spatial domains from spatially resolved transcriptomics through spatially regularized deep graph

Daoliang Zhang1, Na Yu1, Xue Sun1

  • 1Center of Intelligent Medicine, School of Control Science and Engineering, Shandong University, Jinan, Shandong, 250061, China.

BMC Genomics
|November 29, 2024
PubMed
Summary
This summary is machine-generated.

Spatially regularized deep graph networks (SR-DGN) improve spatial domain identification in spatial transcriptomic data by integrating gene expression with spatial information. This method effectively addresses gene dropouts and enhances tissue heterogeneity analysis.

Keywords:
Cross-entropy lossGraph attention networkSpatial domainsSpatial regularization constraintSpatial resolved transcriptomics

More Related Videos

Spatially Compact Arrangement of Larval Zebrafish Sections for Spatial Transcriptomic Analysis
07:40

Spatially Compact Arrangement of Larval Zebrafish Sections for Spatial Transcriptomic Analysis

Published on: May 16, 2025

99
Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

641

Related Experiment Videos

Last Updated: Jun 6, 2025

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection
09:19

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Published on: July 6, 2022

4.8K
Spatially Compact Arrangement of Larval Zebrafish Sections for Spatial Transcriptomic Analysis
07:40

Spatially Compact Arrangement of Larval Zebrafish Sections for Spatial Transcriptomic Analysis

Published on: May 16, 2025

99
Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

641

Area of Science:

  • Genomics
  • Computational Biology
  • Bioinformatics

Background:

  • Spatially resolved transcriptomics (SRT) enables gene expression analysis within tissue contexts.
  • Identifying spatial domains is crucial for understanding tissue heterogeneity and function.
  • Existing methods struggle with spatial consistency and gene dropouts in SRT data.

Purpose of the Study:

  • To develop a novel framework for accurate spatial domain detection in SRT data.
  • To integrate gene expression profiles with spatial information for robust spot representation.
  • To overcome limitations of existing methods, including gene dropout and spatial inconsistency.

Main Methods:

  • Introduction of spatially regularized deep graph networks (SR-DGN).
  • Utilizing graph attention networks (GAT) to aggregate neighboring spot expression data.
  • Implementing spatial regularization for consistent neighborhood relationships.
  • Employing cross-entropy (CE) loss to mitigate gene dropout effects.

Main Results:

  • SR-DGN demonstrates superior performance in spatial domain identification compared to state-of-the-art methods.
  • The framework effectively analyzes SRT data from diverse sequencing platforms.
  • SR-DGN accurately recovers microanatomical structures and improves visualization.

Conclusions:

  • SR-DGN offers a significant advancement in spatial domain identification for SRT data.
  • The method provides more accurate spatial trajectory inferences.
  • SR-DGN enhances the understanding of tissue heterogeneity and biological functions through improved spatial analysis.