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

Deconvolution

Deconvolution

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...

You might also read

Related Articles

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

Sort by

Same author

Comparison of Electroconvulsive Therapy Seizure Outcomes When Using Methohexital Versus Propofol: A Brief Retrospective Report.

The journal of ECT·2026

Same author

Hippocampal volume for prediction of working memory performance in patients with infantile hydrocephalus.

Journal of neurosurgery. Pediatrics·2026

Same author

Accurate delineation of cellular niches via integrated spatial transcriptomics and histological imaging with SYMOL.

Genome research·2026

Same author

Integrative transcriptome and microbiome analysis reveals ferroptosis-driven duodenal damage caused by Ochratoxin A in mice.

Frontiers in immunology·2026

Same author

High-order correlation and consistency-aware multi-view clustering via anchor graph learning.

Neural networks : the official journal of the International Neural Network Society·2026

Same author

Sulfur vacancy-confined Co-Mo sites in MoS<sub>2</sub> for high-efficiency CO<sub>2</sub> hydrogenation to formate.

Nature communications·2026

Same journal

Predicting piRNA-Disease Associations Based on Dual-View Learning and Multi-head Self-Attention Mechanism Fusion.

Interdisciplinary sciences, computational life sciences·2026

Same journal

DTANet+: Dual Interaction and Kernel-Diverse Network for Drug-Target Affinity Prediction.

Interdisciplinary sciences, computational life sciences·2026

Same journal

STNMAE: Identifying Spatial Domains from Spatial Transcriptomics Data with Neighbor-Aware Multi-view Masked Graph Autoencoder.

Interdisciplinary sciences, computational life sciences·2026

Same journal

Diagnosis and Prediction of Alzheimer's Disease via a High-Level Convolutional Block Attention Module-Residual Network.

Interdisciplinary sciences, computational life sciences·2026

Same journal

Deep3D-DTA: A Tri-Modal Deep Learning Framework for Binding Affinity Prediction Leveraging 3D Structural Representations of Drugs and Targets.

Interdisciplinary sciences, computational life sciences·2026

Same journal

ST-LDAW: A Topic-Model and Damped Weighted Least-Squares Method for Integrative Deconvolution of Single-Cell and Spatial Transcriptomics.

Interdisciplinary sciences, computational life sciences·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Sep 18, 2025

Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps

Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps

Published on: October 28, 2018

stGNN: Spatially Informed Cell-Type Deconvolution Based on Deep Graph Learning and Statistical Modeling.

Juntong Zhu¹, Daoyuan Wang², Siqi Chen²

¹School of Information Science and Engineering, Shandong Normal University, Jinan, 250014, China.

Interdisciplinary Sciences, Computational Life Sciences

|June 26, 2025

Summary

This summary is machine-generated.

stGNN, a new spatial graph learning method, accurately resolves cell types in spatial transcriptomics data by integrating spatial and gene expression information. It outperforms existing methods, enabling high-resolution analysis of complex tissue structures.

Keywords:

Cell type deconvolution Deep graph learning Single-cell RNA sequencing reference Spatial transcriptomics Statistical modeling

More Related Videos

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

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

Related Experiment Videos

Last Updated: Sep 18, 2025

Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps

Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps

Published on: October 28, 2018

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

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

Area of Science:

Computational Biology
Genomics
Bioinformatics

Background:

Spatial transcriptomics (ST) revolutionizes tissue analysis but lacks single-cell resolution.
Existing methods often ignore spatial context or fail to align reference data, limiting cell type mapping accuracy.

Purpose of the Study:

To develop stGNN, a novel spatially-informed graph learning framework for accurate cell type deconvolution in ST data.
To improve cell type resolution and mapping by integrating spatial context and statistical modeling.

Main Methods:

Developed a dual encoding module using graph convolutional networks (GCN) and auto-encoders for spatial and non-spatial representations.
Implemented an adaptive attention mechanism to integrate multi-scale spatial structures.
Utilized a negative log-likelihood loss function to align ST and single-cell RNA sequencing (scRNA-seq) data distributions.

Main Results:

stGNN consistently outperformed seven state-of-the-art methods across six diverse ST datasets (10x Visium, Slide-seqV2, Visium HD).
Successfully resolved distinct cortical layers in mouse brain tissue at high resolution.
Demonstrated effective performance across varying ST resolutions.

Conclusions:

stGNN offers a powerful, accurate framework for analyzing cell type composition and spatial distribution in complex tissues.
The method enhances deconvolution accuracy by effectively leveraging spatial information and aligning reference datasets.