  Spatial Transcriptomics is a novel technology that aligns histology images
with spatially resolved gene expression profiles. Although groundbreaking, it
struggles with gene capture yielding high corruption in acquired data. Given
potential applications, recent efforts have focused on predicting
transcriptomic profiles solely from histology images. However, differences in
databases, preprocessing techniques, and training hyperparameters hinder a fair
comparison between methods. To address these challenges, we present a
systematically curated and processed database collected from 26 public sources,
representing an 8.6-fold increase compared to previous works. Additionally, we
propose a state-of-the-art transformer based completion technique for inferring
missing gene expression, which significantly boosts the performance of
transcriptomic profile predictions across all datasets. Altogether, our
contributions constitute the most comprehensive benchmark of gene expression
prediction from histology images to date and a stepping stone for future
research on spatial transcriptomics.
