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Super-resolved spatial transcriptomics by deep data fusion
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DOI：10.1038/s41587-021-01075-3.png)
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摘要
En 中文
The low resolution of spatial transcriptomics is substantially improved by including histology images. Current methods for spatial transcriptomics are limited by low spatial resolution. Here we introduce a method that integrates spatial gene expression data with histological image data from the same tissue section to infer higher-resolution expression maps. Using a deep generative model, our method characterizes the transcriptome of micrometer-scale anatomical features and can predict spatial gene expression from histology images alone.
Keyword:
CELL RNA-SEQ
SINGLE-CELL
GENE-EXPRESSION
TISSUE
VISUALIZATION
期刊
IF:
41.7
论文数:
1.2W
被引数:
10.1W
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