@inproceedings{
he2024heterogeneous,
title={Heterogeneous Graph Guided Contrastive Learning for Spatially Resolved Transcriptomics Data},
author={Xiao He and Chang Tang and Xinwang Liu and Chuankun Li and Shan An and Zhenglai Li},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=FZCOsdSAKW}
}
Spatial transcriptomics provides revolutionary insights into cellular interactions and disease development mechanisms by combining high-throughput gene sequencing and spatially resolved imaging technologies to analyze genes naturally associated with spatially variable tissue genes. However, existing methods typically map aggregated multi-view features into a unified representation, ignoring the heterogeneity and view independence of genes and spatial information. To this end, we construct a heterogeneous Graph guided Contrastive Learning (stGCL) for aggregating spatial transcriptomics data. The method is guided by the inherent heterogeneity of cellular molecules by dynamically coordinating triple-level node attributes through comparative learning loss distributed across view domains, thus maintaining view independence during the aggregation process. In addition, we introduce a cross-view hierarchical feature alignment module employing a parallel approach to decouple spatial and genetic views on molecular structures while aggregating multi-view features according to information theory, thereby enhancing the integrity of inter- and intra-views. Rigorous experiments demonstrate that stGCL outperforms existing methods in various tasks and related downstream applications.
\end{abstract}
Heterogeneous Graph Guided Contrastive Learning for Spatially Resolved Transcriptomics Data
Xiao He Chang Tang Xinwang Liu Chuankun Li Shan An Zhenglai Li