Statistical Power Analysis Framework for Multi-Sample and Cross-Platform Spatial Omics Experiments - Abstract Recently, high-throughput spatial omics technologies have made it possible to simultaneously measure close- to-cell-level gene or protein expressions and spatial locations of these cells within a tissue or organ. These new technologies have provided an unprecedented opportunity to investigate tissue architecture and cell-cell communications. Although multiple computational tools for spatial omics data analysis have recently become available, a rigorous statistical framework for designing spatial omics experiments is still missing in the literature. Researchers need to determine various experimental design parameters, such as the sequencing depth and the number and sizes of Field-Of-View (FoV), in planning a spatial omics experiment. These choices affect whether key goals of spatial omics experiments can be achieved, e.g., identification of tissue architecture and cell-cell communications. In this proposal, we aim to develop a rigorous power analysis framework for spatial omics experiments across various experimental designs and profiling technologies. The assembled team has strong and complementary expertise in development of statistical frameworks for power analysis and design of high throughput sequencing studies, statistical modeling of spatial omics and scRNA-seq data, technology development in spatial omics, spatial statistics, bioinformatics tool development, pulmonary science, and lung fibrosis and relevant age-related mechanisms. We will achieve the proposed goal by implementing three specific aims. In Aim 1, we will develop a rigorous power analysis framework for multi-sample and longitudinal sequencing-based spatial transcriptomics experiments (e.g., 10X Genomics Visium). In Aim 2, we will develop a statistical power analysis framework for imaging-based spatial transcriptomics and proteomics experiments (e.g., seqFISH+, MERFISH, CODEX) in single- and multi-sample and longitudinal settings. In Aim 3, we will develop an interactive web interface for power analysis of spatial omics experiments and utilize and validate it for designing spatial omics experiments for lung cells. The proposed power analysis framework will be developed and evaluated using simulation data, spatial omics data in the public domain, and in-house spatial omics datasets from collaborators. The to-be-developed statistical framework in this project, along with the open-source software implementing this framework, will provide essential tools for the optimal design of future spatial omics experiments.
