Spatial IMIX: A Mixture Model Approach to Spatially Correlated Multi-Omics Data Integration

Abstract

Spatial high-throughput omics data allow scientists to study gene activity in a tissue sample and map where it occurs at the same time. This enables the possibility to investigate important early cancer-initiating events occur in normal-appearing tissue and gene activities that progress and carry through tumor tissue, as defined by "field effect." The "field effect" genes are differentially expressed or methylated genes in the spatially resolved high-dimensional datasets with respect to the pathology subtype in each geographical sample across the tissue region. Current statistical methods for spatially resolved genomics data focus on the association of omics data with spatial coordinates without being able to incorporate and test for the association with the sample subtypes. In addition, analytical methods are underdeveloped for spatially resolved multi-omics data integration. We propose a novel statistical frame-work spatial IMIX to integratively analyze spatially resolved high-dimensional multi-omics data associated with a specific trait, such as sample subtypes while modeling the spatial correlations between samples and the inter-data-type correlations between omics data simultaneously. Through extensive simulations, spatial IMIX demonstrated well-controlled type I error, great power by relaxing the independence assumptions between data types, model selection features, and the ability to control FDR across data types. Data applications to a geographically annotated tissue area of bladder cancer discovered cancer-initiating gene activities and revealed interesting fundamental biological mechanisms through path-way analysis. We have implemented our method in R package spatialimix available at https://github.com/ziqiaow/spatialimix.