Scalable visual selection and comparison of spatially variable genes

Develop visualization methods that support scalable selection and comparison of spatially informative genes while mitigating manual feature selection and the color-blending limitations of dense small-multiple tissue maps.

Background

Spatial transcriptomics figures commonly display one tissue map per selected gene, using small multiples to compare expression patterns. The surveyed literature frequently relies on manually curated marker-gene sets, while dense small multiples and blended color encodings become difficult to interpret when many genes or variables must be shown simultaneously. The authors identify the combination of manual selection and color-blending limits as an unresolved visualization challenge, especially for discovery at larger feature scales.