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A masked generative graph representation learning framework empowering precise spatial domain identification
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DOI：10.1093/bioinformatics/btag333.png)
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Abstract
En 中文
Spatial transcriptomics (ST) enables the measurement of gene expression while preserving the spatial context of tissues. However, the sparsity of ST data leads to poor usage of gene expression and spatial information, resulting in the embeddings that are not well represented and challenging for downstream analyses.
Keywords:
Spatial transcriptomics
Gene expression
Graph representation learning
Spatial domain identification
Data sparsity
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Cited Papers
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