GATCL: An Adaptive Contrastive Learning Framework Based on MHGAT for Spatial Domain Identification in Spatial Transcriptomics
Shilin Zhang, Weiliang Huo, Qingchen Zhang, Xiulong Liu
Abstract
Recent advances in spatial transcriptomics have enabled the simultaneous measurement of gene expression profiles and spatial location information, offering a more comprehensive and in-depth view for studying the tissue microenvironment. Spatial domain identification is a crucial step in analyzing spatial transcriptomics. However, current methods have poor accuracy and visualization because they lack self-adaptability to different tissue data, and moreover, they cannot effectively extract spatial location information. To address these issues, we propose an adaptive graph contrastive learning framework based on multi-head graph attention networks (GATCL) for spatial domain identification. Specifically, we design a data augmentation module to mask and shuffle the pre-processed gene expression data to generate more differentiated negative samples. In addition, we construct the multi-head graph attention networks (MHGAT) to encode gene expression profiles and spatial location information. More importantly, we design an adaptive graph contrastive learning model that works both with positive and negative samples from spatial transcriptomics. We introduce the attention pooling mechanism to dynamically and adaptively aggregate the spots
BibTeX
@inproceedings{aaai2026_gatclanadaptivec,
title = {GATCL: An Adaptive Contrastive Learning Framework Based on MHGAT for Spatial Domain Identification in Spatial Transcriptomics},
author = {Shilin Zhang and Weiliang Huo and Qingchen Zhang and Xiulong Liu},
booktitle = {AAAI 2026},
year = {2026}
}