Article

Integrating cellular graph embeddings with tumor morphological features to predict in-silico spatial transcriptomics from H&E images

2023-11-03

Abstract excerpt

Spatial transcriptomics allows precise RNA abundance measurement at high spatial resolution, linking cellular morphology with gene expression. We present a novel deep learning algorithm predicting local gene expression from histopathology images. Our approach employs a graph isomorphism neural network capturing cell-to-cell interactions in the tumor microenvironment and a Vision Transformer (CTransPath) for obtain...

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Literature Corpus work: d88f7068-1a34-5bcc-86bb-8711f1767c54
DOI: 10.1101/2023.10.31.565020

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