Preserving tissue structure through density-based spatial analysis with scider

In spatial transcriptomics, most existing approaches for spatial domain detection analyze slide-wide gene expression patterns while integrating information from local neighborhoods or nearest neighbors. However, such global strategies can overlook tissue organization and obscure regional heterogeneity. To address this, we present scider, a framework for spatial data analysis to preserve tissue structures. scider defines spatial domains from local cellular organization and distribution, instead of relying on global clustering of gene expression and neighborhood profiles. Kernel density estimation (KDE) is used to characterize the spatial distribution of cells, enabling the robust and unbiased identification of regions of interest (ROIs) beyond histological annotations. We show that ROIs make multi-sample analysis possible and reveal spatial patterns that are undetectable in single-sample analysis. Additionally, KDE-derived contour lines define regions of similar cell density, supporting cell type composition and differential expression analyses along continuous spatial gradients.