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Smoother: a unified and modular framework for incorporating structural dependency in spatial omics data.

ABSTRACT: Spatial omics technologies can help identify spatially organized biological processes, but existing computational approaches often overlook structural dependencies in the data. Here, we introduce Smoother, a unified framework that integrates positional information into non-spatial models via modular priors and losses. In simulated and real datasets, Smoother enables accurate data imputation, cell-type deconvolution, and dimensionality reduction with remarkable efficiency. In colorectal cancer, Smoother-guided deconvolution reveals plasma cell and fibroblast subtype localizations linked to tumor microenvironment restructuring. Additionally, joint modeling of spatial and single-cell human prostate data with Smoother allows for spatial mapping of reference populations with significantly reduced ambiguity.

SUBMITTER: Su J

PROVIDER: S-EPMC10726548 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature

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Smoother: a unified and modular framework for incorporating structural dependency in spatial omics data.

Su Jiayu J Reynier Jean-Baptiste JB Fu Xi X Zhong Guojie G Jiang Jiahao J Escalante Rydberg Supo RS Wang Yiping Y Aparicio Luis L Izar Benjamin B Knowles David A DA Rabadan Raul R

Genome biology 20231218 1

Spatial omics technologies can help identify spatially organized biological processes, but existing computational approaches often overlook structural dependencies in the data. Here, we introduce Smoother, a unified framework that integrates positional information into non-spatial models via modular priors and losses. In simulated and real datasets, Smoother enables accurate data imputation, cell-type deconvolution, and dimensionality reduction with remarkable efficiency. In colorectal cancer, S ...[more]

PMID: 38110959

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