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

• Activity Number: 70 - Novel Approaches for Omics and Multi-Omics Analysis
• Type: Contributed
• Date/Time: Sunday, August 7, 2022 : 4:00 PM to 5:50 PM
• Sponsor: Section on Statistics in Genomics and Genetics
• Author(s): Ziqiao Wang* and Peng N/A Wei
• Companies: Eli Lilly and Company; The University of Texas MD Anderson Cancer Center and The University of Texas MD Anderson Cancer Center
• Keywords: spatially resolved genomics data; integrative omics analysis; mixture model; spatial mixed model; EM algorithm

Abstract

Current statistical methods for spatially resolved genomics data focus on the association of omics data with spatial coordinates without testing for the association with an outcome in high-dimensional spatially correlated data settings. In addition, analytical methods are underdeveloped for spatially resolved multi-omics data integration. To fill the research gaps, we propose a new method to integratively analyze spatially resolved high-dimensional multi-omics data associated with a specific trait, such as sample subtypes. This is an extension of the multi-omics integration framework IMIX proposed by Wang and Wei, 2021. We further incorporate spatial information by proposing spatial IMIX using spatial mixed model that characterizes spatial correlations between samples. Through extensive simulations, spatial IMIX showed great power by relaxing the independence assumptions between data types 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. [Disclosure: This work was done while at The University of Texas MD Anderson Cancer Center]