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

• Published in: Genome Biology Vol. 24; no. 1; p. 291
• Main Authors: Su, Jiayu, Reynier, Jean-Baptiste, Fu, Xi, Zhong, Guojie, Jiang, Jiahao, Escalante, Rydberg Supo, Wang, Yiping, Aparicio, Luis, Izar, Benjamin, Knowles, David A., Rabadan, Raul
• Format: Journal Article
• Language: English
• Published: London BioMed Central 18.12.2023; BMC
• Subjects: Algorithms; Animal Genetics and Genomics; Bioinformatics; Biomedical and Life Sciences; Cell-type deconvolution; Colorectal cancer; Colorectal carcinoma; colorectal neoplasms; data collection; Data imputation; Datasets; Dimensionality reduction; Evolutionary Biology; Fibroblasts; Genes; genome; Human Genetics; Humans; Life Sciences; Localization; Male; Method; Microbial Genetics and Genomics; Neighborhoods; Plant Genetics and Genomics; Prostate; Reference mapping; Spatial data; Spatial omics; Spatial prior; Tumor Microenvironment; Dimensionality reduction; Data imputation; Reference mapping; Cell-type deconvolution; Joint analysis of single-cell and spatial data; Spatial omics; Spatial prior
• ISSN: 1474-760X; 1474-7596; 1474-760X
• DOI: 10.1186/s13059-023-03138-x

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.