Related Experiment Video
Updated: Apr 21, 2026
Using R, Seurat, and CellChat to Analyze a Single-Cell Transcriptomics Dataset of Mouse Skin Wound Healing
Published on: August 1, 2025
Reconstructing cell-cell interaction network in single-cell spatial transcriptomics via directed heterogeneous graph
Jin-Xian Hu1,2,3, Xiaoyong Pan3, Yuan Ye4
1Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
Motivation:
Spatial transcriptome data have both gene expression information and cell spatial location information, offering exceptional prospects for analyzing cell-cell interaction (CCI) network. Most existing statistical and optimal transport-based methods rely only on known ligand-receptor pairs to infer CCI network. Furthermore, most current deep learning frameworks rely on symmetric decoders or undirected graph architectures.
Results:
Taking advantage of spatial transcriptomic data and graph autoencoders, we present a directed heterogeneous graph autoencoder-based approach DualCellChat to reconstruct a complete and accurate CCI network from incomplete single cell spatial transcriptomics. Benchmarked on five single-cell spatial datasets from four different technologies, we demonstrate that DualCellChat outperforms existing deep learning-based methods and can inherently model the direction of cellular interactions. Furthermore, we introduce downstream analysis to infer signature genes involved in cellular interactions from the reconstructed CCI network and infer significant ligand-receptor pairs for specific cell types.
Availability And Implementation:
The dataset and code are available in GitHub (https://github.com/JinxianHu/DualCellChat) and Zenodo (DOI: 10.5281/zenodo.18512678).
