Connecting Gene Expression and Tissue Morphology with Conditional Generative Models

Inferring tissue morphology from gene expression remains widely unexplored. We present a two-stage conditional generative framework that leverages for the first time spatial transcriptomics data from the Visium HD platform to demonstrate this inference is feasible. Starting from near-whole-transcriptome profiles, the model synthesizes histology-like images that are plausible, as validated by FID scores and expert review. Model interpretation further reveals biologically meaningful links between specific genes and morphological patterns.