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

Abstract

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. 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. The dataset and code are available in GitHub (https://github.com/JinxianHu/DualCellChat) and Zenodo (DOI: 10.5281/zenodo.18512678).