QueST: Querying Functional and Structural Niches on Spatial Transcriptomics data via Contrastive Subgraph Embedding

Mo Chen, Minsheng Hao, Lei Wei, Xuegong Zhang

Keywords: Graph neural networks; Subgraph contrastive learning; Spatial niche query; Spatial transcriptomics; Batch removal

TL;DR: We define a spatial niche query problem in spatial transcriptomics and propose QueST, a novel subgraph representation learning model for solving this problem.

Abstract: The functional or structural spatial regions within tissues, referred to as spatial niches, are elements for illustrating the spatial contexts of multicellular organisms. A key challenge is querying shared niches across diverse tissues, which is crucial for achieving a comprehensive understanding of the organization and phenotypes of cell populations. However, current data analysis methods predominantly focus on creating spatial-aware embeddings for cells, neglecting the development of niche-level representations for effective querying. To address this gap, we introduce QueST, a novel niche representation learning model designed for querying spatial niches across multiple samples. QueST utilizes a novel subgraph contrastive learning approach to explicitly capture niche-level characteristics and incorporates adversarial training to mitigate batch effects. We evaluate QueST on established benchmarks using human and mouse datasets, demonstrating its superiority over state-of-the-art graph representation learning methods in accurate niche queries. Overall, QueST offers a specialized model for spatial niche queries, paving the way for deeper insights into the patterns and mechanisms of cell spatial organization across tissues.

Primary Area: applications to physical sciences (physics, chemistry, biology, etc.)

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Submission Number: 9318