2023 Articles

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

Su, Jiayu; Reynier, Jean-Baptiste; Fu, Xi; Zhong, Guojie; Jiang, Jiahao; Escalante, Rydberg S.; Wang, Yiping; Aparicio, Luis; Izar, Benjamin; Knowles, David A.; Rabadan, Raul

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.