While existing methods for analysing spatial transcriptomics data primarily focus on identifying gene patterns within a single tissue slice, few address identifying genes with differential spatial expression patterns (DSEPs) across multiple conditions. Here the authors introduce DSEP gene prioritization as a new analytical task and present River, an interpretable deep learning framework that identifies genes exhibiting condition-relevant spatial changes.