SEPAL: Spatial Gene Expression Prediction from Local Graphs
This work addresses a bottleneck in spatial transcriptomics for disease understanding, though it appears incremental as it builds on existing methods by refining spatial context integration.
The authors tackled the problem of predicting spatial gene expression from histopathology images by introducing SEPAL, a model that uses local visual context and graph neural networks to outperform previous state-of-the-art methods in human breast cancer datasets.
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.