spMosaic: multi-sample integration for scalable spatial domain discovery based on spatial transcriptomics

Spatial transcriptomics enables high-resolution profiling of gene expression within intact tissue architecture, providing new opportunities to study the spatial organization of tissue structures and cell types. However, identifying spatial domains that are reproducible across samples and individuals remains challenging due to batch effects, inter-individual heterogeneity, increasing dataset sizes and the incomparability of spatial coordinates across independently processed tissue sections. We propose spMosaic, a scalable computational framework for multi-sample spatial domain identification. The method integrates two key components: (i) generalized additive model-based spatial smoothing to identify spatially variable genes shared across samples, and (ii) a batch-aware dual-encoder autoencoder that learns low-dimensional representations while encouraging sample-associated variation to be represented separately from domain-relevant biological variation. A subsequent deep embedded clustering refinement step further improves the separation and compactness of inferred domain assignments. We evaluated spMosaic against nine existing methods in both single-individual and multi-individual settings across diverse datasets spanning five technologies. spMosaic consistently achieved strong clustering performance relative to existing methods while maintaining competitive sample integration. In addition, under the computational settings used in our benchmark, spMosaic was the only method among those evaluated that successfully scaled to a large cohort, enabling the analysis of 378 918 spatial spots from 31 frontal cortex samples across 12 mice. These results demonstrate that spMosaic provides an integrative and scalable approach for multi-sample spatial transcriptomics analysis, enabling accurate and interpretable characterization of spatial tissue organization.