SOTIP is a versatile method for microenvironment modeling with spatial omics data

Zhiyuan Yuan (), Yisi Li, Minglei Shi, Fan Yang, Juntao Gao, Jianhua Yao () and Michael Q. Zhang ()
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Zhiyuan Yuan: Fudan University
Yisi Li: Tsinghua University
Minglei Shi: Tsinghua University
Fan Yang: Tencent AI Lab
Juntao Gao: Tsinghua University
Jianhua Yao: Tencent AI Lab
Michael Q. Zhang: Tsinghua University

Nature Communications, 2022, vol. 13, issue 1, 1-19

Abstract: Abstract The rapidly developing spatial omics generated datasets with diverse scales and modalities. However, most existing methods focus on modeling dynamics of single cells while ignore microenvironments (MEs). Here we present SOTIP (Spatial Omics mulTIPle-task analysis), a versatile method incorporating MEs and their interrelationships into a unified graph. Based on this graph, spatial heterogeneity quantification, spatial domain identification, differential microenvironment analysis, and other downstream tasks can be performed. We validate each module’s accuracy, robustness, scalability and interpretability on various spatial omics datasets. In two independent mouse cerebral cortex spatial transcriptomics datasets, we reveal a gradient spatial heterogeneity pattern strongly correlated with the cortical depth. In human triple-negative breast cancer spatial proteomics datasets, we identify molecular polarizations and MEs associated with different patient survivals. Overall, by modeling biologically explainable MEs, SOTIP outperforms state-of-art methods and provides some perspectives for spatial omics data exploration and interpretation.

Date: 2022
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DOI: 10.1038/s41467-022-34867-5

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