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Updated: Jun 24, 2026

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Published on: September 5, 2025

CAGNet: a structure-aware clustering-alternated graph network for cell-cell interaction inference in spatial

Han Ma¹, Xin Zhang¹, Hang Chen²

¹School of Computer Science and Technology, Changchun University of Science and Technology, Changchun, 130022, Jilin, China.

BMC Bioinformatics

|June 23, 2026

Summary

This summary is machine-generated.

CAGNet enhances cell-cell interaction inference in spatial transcriptomics by dynamically learning cellular relationships. This novel framework improves understanding of tissue organization and cell function.

Keywords:

Cell-cell interaction Clustering Graph neural network Spatial transcriptomics

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An Integrated Workflow to Study the Promoter-Centric Spatio-Temporal Genome Architecture in Scarce Cell Populations

An Integrated Workflow to Study the Promoter-Centric Spatio-Temporal Genome Architecture in Scarce Cell Populations

Published on: April 21, 2023

Area of Science:

Computational biology
Genomics
Bioinformatics

Background:

Understanding cell-cell interactions (CCIs) is vital for spatial transcriptomics.
Current graph-based models struggle with dynamic cellular relationships due to static clustering.
Limitations exist in capturing dynamic cellular relationships in tissue organization.

Purpose of the Study:

To propose CAGNet, a novel two-stage framework for robust CCI inference.
To overcome limitations of static models in spatial transcriptomics analysis.
To improve the understanding of spatial organization and functional heterogeneity in tissues.

Main Methods:

CAGNet employs a two-stage framework for CCI inference.
Stage 1: Graph Attention Network encoder for structure-aware node embeddings.
Stage 2: Alternating optimization for iterative refinement of cluster centers and node embeddings.

Main Results:

CAGNet consistently outperforms six CCI inference baselines on three 10x Genomics Visium datasets.
Achieved the highest Adjusted Rand Index for spatial domain identification, indicating biologically relevant embeddings.
Information-theoretic analysis confirms superior mutual information retention in learned embeddings.

Conclusions:

CAGNet provides a powerful new approach for cell-cell interaction inference.
The framework effectively captures dynamic cellular relationships and spatial organization.
CAGNet enhances the analysis of spatial transcriptomics data for biological discovery.