  Spatial transcriptomics is an emerging technology that aligns histopathology
images with spatially resolved gene expression profiling. It holds the
potential for understanding many diseases but faces significant bottlenecks
such as specialized equipment and domain expertise. In this work, we present
SEPAL, a new model for predicting genetic profiles from visual tissue
appearance. Our method exploits the biological biases of the problem by
directly supervising relative differences with respect to mean expression, and
leverages local visual context at every coordinate to make predictions using a
graph neural network. This approach closes the gap between complete locality
and complete globality in current methods. In addition, we propose a novel
benchmark that aims to better define the task by following current best
practices in transcriptomics and restricting the prediction variables to only
those with clear spatial patterns. Our extensive evaluation in two different
human breast cancer datasets indicates that SEPAL outperforms previous
state-of-the-art methods and other mechanisms of including spatial context.
