Kok Siong Ang

Abstract With the advent of spatial multi-omics, mosaic integration of diverse datasets with partially overlapping modalities enables the construction of comprehensive multimodal spatial atlases from heterogeneous sources. Here we present SpaMosaic, a tool that uses contrastive learning and graph neural networks to build a modality-agnostic, batch-corrected latent space for spatial domain identification and missing-modality imputation.

Abstract With the advent of spatial multi-omics, we can mosaic integrate diverse datasets with partially overlapping modalities to construct consensus multi-modal spatial atlases of the source tissue. SpaMosaic is a spatial multi-omics mosaic integration tool that employs contrastive learning and graph neural networks to construct a modality-agnostic and batch-corrected latent space suited for analyses like spatial domain identification and imputing missing omes.

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