Statistical Power Calculation Framework for Spatially Resolved Transcriptomics Experiments

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Abstract Recently, high-throughput spatial transcriptomics (HST) technologies (e.g., 10X Genomics Visium, Slide-seq, and Slide-seqV2) have made it possible to simultaneously measure close-to-cell-level gene expressions and spatial locations of these cells within a tissue or organ. These new technologies have provided an unprecedented opportunity to investigate cellular heterogeneity and cell-cell communications. Although a few computational tools for HST data analysis have recently become available, a rigorous statistical framework for design of HST experiments is still missing in the literature. Researchers planning an HST experiment need to determine various experimental design parameters such as the sequencing depth, and these choices affect whether key goals of HST experiments can be achieved, e.g., identification of tissue architecture, spatially variable genes, and cell- cell communications. In this proposal, we aim to develop a rigorous power analysis framework for HST experiments. The assembled team has strong and complementary expertise in statistical modeling of HST data, development of statistical frameworks and software for power analysis and design of high throughput sequencing data, single-cell genomics technologies, spatial statistics, computational tool development, and utilization of these computational tools for investigation of molecular and immunologic basis of diseases. We will achieve the proposed goal by implementing two specific aims. In Aim 1, we will develop a rigorous power analysis framework for HST experiments. In Aim 2, we will develop an interactive web interface and an R package for power analysis of HST experiments. The proposed power analysis framework will be developed and evaluated using simulation data, HST data in the public domain, and in-house HST datasets from collaborators. The statistical framework that will be developed in this project, along with the open-source software implementing this framework, will provide essential tools for the optimal design of future HST experiments.