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

Deconvolution01:20

Deconvolution

416
Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
416

You might also read

Related Articles

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

Sort by
Same author

scDeepAPA: a deep learning framework for single-cell alternative polyadenylation identification.

Briefings in bioinformatics·2026
Same author

VIRSE: a variational Bayesian framework for RNA structural ensemble inference.

Briefings in bioinformatics·2026
Same author

Socioeconomic and Clinical Determinants Driving Access to BRCA Genetic Testing in Cancer : A Case-Control Study Using Observational Electronic Health Records Across Multiple Sites.

medRxiv : the preprint server for health sciences·2026
Same author

SpaGene: A Deep Adversarial Framework for Spatial Gene Imputation.

Computational and structural biotechnology journal·2026
Same author

ShapeRNA: an integrated web server for RNA secondary structure, ensemble, and functional analysis.

Nucleic acids research·2026
Same author

GatorST: A Versatile Contrastive Meta-Learning Framework for Spatial Transcriptomic Data Analysis.

Small methods·2026
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

Related Experiment Video

Updated: Nov 20, 2025

Mining Spatial Transcriptomics Datasets using DeepSpaceDB
10:16

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Published on: September 5, 2025

404

DSTG: deconvoluting spatial transcriptomics data through graph-based artificial intelligence.

Qianqian Song1,2, Jing Su3,4

  • 1Center for Cancer Genomics and Precision Oncology, Wake Forest Baptist Comprehensive Cancer Center, Wake Forest Baptist Medical Center, Winston Salem, NC 27157, USA.

Briefings in Bioinformatics
|January 22, 2021
PubMed
Summary
This summary is machine-generated.

Spatial transcriptomics (ST) data reveals cell mixtures in tissue spots. Our new method, deconvoluting spatial transcriptomics data through graph-based convolutional networks (DSTG), accurately disentangles these mixtures to identify cell types and their spatial organization.

Keywords:
deconvolutiongraph-based artificial intelligencesingle-cell RNA-seqspatial transcriptomics

More Related Videos

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

1.1K
Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.5K

Related Experiment Videos

Last Updated: Nov 20, 2025

Mining Spatial Transcriptomics Datasets using DeepSpaceDB
10:16

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Published on: September 5, 2025

404
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

1.1K
Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.5K

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Spatial transcriptomics (ST) enables mapping RNA abundance to tissue locations.
  • ST data spots often contain mixed cell types, complicating analysis.
  • Understanding cellular composition within spatial spots is crucial for tissue function studies.

Purpose of the Study:

  • To develop a novel method for deconvoluting mixed cell signals in ST data.
  • To accurately identify cell compositions within each spatial spot.
  • To reveal the spatial architecture of cellular heterogeneity in tissues.

Main Methods:

  • Propose deconvoluting spatial transcriptomics data through graph-based convolutional networks (DSTG).
  • DSTG utilizes graph-based convolutional networks to disentangle gene expression data.
  • The method is evaluated on synthetic and real-world ST datasets.

Main Results:

  • DSTG accurately deconvolutes gene expressions from mixed cell populations in ST data.
  • Superior performance demonstrated on synthetic data across different protocols.
  • Successfully identified spatial cell compositions in mouse cortex, hippocampus, and pancreatic tumors.

Conclusions:

  • DSTG effectively uncovers cell states and subpopulations based on spatial localization.
  • The method provides precise interrogation of spatial organizations and cellular functions within tissues.
  • DSTG is released as an open-source software for broader research application.