Cross‐Modal Denoising and Integration of Spatial Multi‐Omics Data with CANDIES
Source: PubMed Central Open Access, NCBI / U.S. National Library of Medicine
ABSTRACT Spatial multi‐omics data offer a powerful framework for integrating diverse molecular profiles while maintaining the spatial organization of cells. However, inherent variations in data quality and noise levels across different modalities pose significant challenges to accurate integration and analyses. In this paper, we introduce CANDIES, which leverages a conditional diffusion model and contrastive learning to effectively denoise and integrate spatial multi‐omics data. With our innovative model and algorithm designs, CANDIES not only enhances the quality of spatial multi‐omics data, but also yields a unified and comprehensive joint representation, thereby empowering many downstream analyses. We conduct extensive evaluations on diverse synthetic and real datasets, including MISAR‐seq data from the mouse brain, spatial CITE‐seq data from human skin biopsy tissue, spatial Mux‐seq, and spatial ATAC‐RNA‐seq data from the mouse embryo, and 10Visium data from human lymph nodes. CANDIES shows superior performance on various downstream tasks, including denoising, spatial domain identification, spatiotemporal trajectory reconstruction, and spatial association mapping for complex human traits. In particular, we show that CANDIES representations can be integrated with the rich resources from genome‐wide association studies (GWASs), allowing the spatial domains to be linked with complex human traits, yielding spatially resolved interpretations of complex traits in their relevant
Abstract
ABSTRACT Spatial multi‐omics data offer a powerful framework for integrating diverse molecular profiles while maintaining the spatial organization of cells. However, inherent variations in data quality and noise levels across different modalities pose significant challenges to accurate integration and analyses. In this paper, we introduce CANDIES, which leverages a conditional diffusion model and contrastive learning to effectively denoise and integrate spatial multi‐omics data. With our innovative model and algorithm designs, CANDIES not only enhances the quality of spatial multi‐omics data, but also yields a unified and comprehensive joint representation, thereby empowering many downstream analyses. We conduct extensive evaluations on diverse synthetic and real datasets, including MISAR‐seq data from the mouse brain, spatial CITE‐seq data from human skin biopsy tissue, spatial Mux‐seq, and spatial ATAC‐RNA‐seq data from the mouse embryo, and 10Visium data from human lymph nodes. CANDIES shows superior performance on various downstream tasks, including denoising, spatial domain identification, spatiotemporal trajectory reconstruction, and spatial association mapping for complex human traits. In particular, we show that CANDIES representations can be integrated with the rich resources from genome‐wide association studies (GWASs), allowing the spatial domains to be linked with complex human traits, yielding spatially resolved interpretations of complex traits in their relevant tissues. In this paper, we introduce CANDIES, which leverages a conditional diffusion model and contrastive learning to effectively denoise and integrate spatial multi‐omics data. We conduct extensive evaluations on diverse synthetic and real datasets, CANDIES shows superior performance on various downstream tasks, including denoising, spatial domain identification, spatiotemporal trajectory reconstruction, and spatial association mapping for complex human traits. advs75404-abs-0001 graphical