Integrating cellular graph embeddings with tumor morphological features to predict in-silico spatial transcriptomics from H&E images
Vignesh Prabhakar,
Elisa Warner,
Kai Liu
Abstract: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 obtaining the tumor morphological features. Using a dataset of 30,612 spatially resolved gene expression… Show more
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