Abstract
AbstractProperly integrating spatially resolved transcriptomics (SRT) generated from different batches into a unified gene-spatial coordinate system could enable the construction of a comprehensive spatial transcriptome atlas. Here, we propose SPIRAL, consisting of two consecutive modules: SPIRAL-integration, with graph domain adaptation-based data integration, and SPIRAL-alignment, with cluster-aware optimal transport-based coordination alignment. We verify SPIRAL with both synthetic and real SRT datasets. By encoding spatial correlations to gene expressions, SPIRAL-integration surpasses state-of-the-art methods in both batch effect removal and joint spatial domain identification. By aligning spots cluster-wise, SPIRAL-alignment achieves more accurate coordinate alignments than existing methods.
Funder
Scholarship from the China Scholarship Council
Chenguang Program of Shanghai Education Development Foundation and Shanghai Municipal Education Commission
Tencent AI Lab Rhino-Bird Focused Research Program
Natural Science Foundation of China
Shanghai Science and Technology Development Funds
Shanghai Center for Brain Science and Brain-Inspired Technology, and 111 Project
Publisher
Springer Science and Business Media LLC
Cited by
14 articles.
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