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STMGraph: spatial-context-aware of transcriptomes via a dual-remasked dynamic graph attention model.
Lixian Lin1,2, Haoyu Wang1, Yuxiao Chen1
1Center for Genomics and Biotechnology, Fujian Provincial Key Laboratory of Haixia Applied Plant Systems Biology, Haixia Institute of Science and Technology, Fujian Agriculture and Forestry University, No. 15 Shangxiadian Road, Cangshan District, Fuzhou 350002, China.
STMGraph, a novel deep learning framework, enhances spatial transcriptomics analysis by addressing data dropouts. It improves accuracy in detecting tissue microenvironments, clustering spatial domains, and correcting batch effects for biological discovery.
Area of Science:
- Genomics
- Bioinformatics
- Computational Biology
Background:
- Spatial transcriptomics (ST) technologies offer insights into tissue architecture.
- Integrating local and non-local cellular features is crucial for understanding gene expression patterns.
- Current ST algorithms struggle with data dropouts, impacting analysis accuracy and robustness.
Purpose of the Study:
- To develop a novel framework for enhanced spatial transcriptomics data integration.
- To overcome limitations of existing methods in handling ST data dropouts.
- To improve accuracy in microenvironmental heterogeneity detection, spatial domain clustering, and batch-effects correction.
Main Methods:
- Developed STMGraph, a dual-view dynamic deep learning framework.
- Incorporated a dual-remask mechanism (MASK-REMASK) for feature sharing.
- Utilized a dynamic graph attention model (DGAT) with self-supervision for comprehensive node representation.
Main Results:
- STMGraph demonstrated superior performance over 10 state-of-the-art tools in systematic benchmarking.
- Achieved high accuracy and robustness in spatial domain clustering across diverse ST platforms and resolutions.
- Implicitly corrected batch effects, enabling spatial domain clustering of multi-slice ST data.
Conclusions:
- STMGraph is a universal, platform-independent framework for spatial transcriptomics analysis.
- It excels in spatial-context-aware analysis, improving microenvironmental detection and clustering.
- Presents a desirable novel tool for advancing diverse ST studies and biological discovery.
