Speaker
Description
Spatial transcriptomics (ST) is a methodological suite that facilitates the in situ, high-resolution measurement of the transcriptome across a designated tissue section. By integrating transcriptional data with spatial coordinates, ST techniques enable the elucidation of key biological phenomena, including cell-type-specific gene regulatory networks, the spatial patterning of cellular architecture, and the mechanisms governing intercellular communication. This capability provides transformative value for research in oncology, neurobiology, developmental biology, and immunology.
Analyses of ST data typically focus on features of tissue organization such as cell type abundance, spatial co-localization, and neighborhood structure. Statistical methods which quantify changes in cell type proportions and methods which characterize spatial organization and local tissue microenvironments still have complicated sample size and statistical power modeling approaches.
Recent work has begun to address this gap through simulation-based frameworks and non-parametric spatial resampling techniques designed to estimate power under realistic spatial constraints [1]. These strategies leverage generative models and synthetic ST data to approximate experimental variability and spatial heterogeneity. However, these approaches have not been systematically extended to analyses of differential cell type abundance or spatial co-localization. Also, recent methodologies are computationally heavy due to bootstrapping as well as P-splines cannot enforce 3D monotonicity.
Here, we review emerging methods for statistical power estimation in ST and evaluate their relevance for studies focused on tissue composition and spatial organization [2]. Specifically, we aim to contribute to the development of a novel, theoretically-grounded methodology for power estimation that minimizes or eliminates the dependence on computationally intensive bootstrapping procedures. We discuss key conceptual considerations, methodological limitations, and opportunities for extending power analysis to abundance- and co-localization-based workflows, with the goal of guiding more robust experimental design and interpretation in future ST studies.
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