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bioRxiv · 10.1101/2022.10.25.513785

A Unified Modular Framework to Incorporate Structural Dependency in Spatial Omics Data

Su, J.·Reynier, J.-B.·Fu, X.·Zhong, G.·Jiang, J.·Supo Escalante, R.·Wang, Y.·Izar, B.·Knowles, D. A.·Rabadan, R.
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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 revealed plasma cell and fibroblast subtype localizations linked to tumor microenvironment restructuring. Additionally, joint modeling of spatial and single-cell human prostate data with Smoother allowed for spatial mapping of reference populations with significantly reduced ambiguity.

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Su, J., Reynier, J.-B., Fu, X., Zhong, G., Jiang, J., Supo Escalante, R., Wang, Y., Izar, B., Knowles, D. A., Rabadan, R.. 2022-10-27. A Unified Modular Framework to Incorporate Structural Dependency in Spatial Omics Data. https://doi.org/10.1101/2022.10.25.513785

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