Multi-view gene panel characterization for spatially resolved omics.

Abstract

Spatially resolved transcriptomics has revolutionized the study of complex tissues by enabling cellular and subcellular resolution. However, targeted spatial technologies depend on pre-selected gene panels, which are typically curated based on prior biological knowledge or specific research hypotheses. While existing methods often focus on optimizing for cell type identification, we argue that effective panel design should also account for transcriptional variation, pathway-level coverage, and minimal gene redundancy. To meet these broader criteria, we developed a two-part framework: (i) panelScope, a gene panel characterization platform that characterizes panels from multiple perspectives, allowing for holistic comparisons of gene panels for custom panel design; and (ii) panelScope-OA, a genetic algorithm that integrates these characterization metrics into a multi-loss function to automate panel optimization. We applied panelScope and panelScope-OA to characterize nine panels across four datasets. Notably, computationally constructed gene panels performed competitively in capturing major cell types when compared to our in-house manually curated panel. However, refined manual curation offered distinct advantages, particularly in capturing minor cell types. Our results demonstrate the utility of panelScope and panelScope-OA by offering quantitative and multi-dimensional insights to support the design of panels tailored to diverse research needs.