Reconstructing cell-cell interaction network in single-cell spatial transcriptomics via directed heterogeneous graph autoencoder

Jin-Xian Hu

doi:10.1093/bioinformatics/btag130

Reconstructing cell-cell interaction network in single-cell spatial transcriptomics via directed heterogeneous graph autoencoder

2026-04-17

0

OA

AI

DOI：10.1093/bioinformatics/btag130

Original

Original request for help

Save

Abstract

En 中文

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.

Keywords:

cell-cell interaction

spatial transcriptomics

directed graph

heterogeneous graph autoencoder

ligand-receptor pairs

Reconstructing cell-cell interaction network in single-cell spatial transcriptomics via directed heterogeneous graph autoencoder

Abstract

Journal

Organization