17: MEM-Seq: A scalable adjustment method for correcting spatial autocorrelation in spatial multiscale analysis of microbiome sequencing data

Thomas M. Menino Convention & Exhibition Center 

Recent advances in spatial profiling technologies have enabled the collection of microbiome sequencing data with explicit spatial information, creating new opportunities—and challenges—for microbiome analysis. In this study, we introduce and analyze a novel data type: spatially resolved microbiome sequencing data obtained from a mouse intestinal tissue section containing a tumor. Samples were collected from multiple spatial locations across tumor and adjacent normal regions, with the goal of characterizing microbial community differences and identifying factors driving these differences. A key methodological challenge in spatial microbiome analysis is spatial autocorrelation, which violates the independence assumptions underlying many standard statistical methods. We developed a novel and principled approach for addressing the spatial autocorrelation by borrowing and improving ideas from classic spatial statistics toolkits. Our approach allows for scalable and effective analysis on the microbiome community level differences between tissue compartments (such as normal vs tumor). Applying our method, we identify significant microbial community differences between tumor and normal tissue regions after accounting for spatial autocorrelation. Notably, we find that sequencing depth emerges as a key factor driving community variation, challenging the common assumption that sequencing depth acts solely as a technical nuisance variable. These findings highlight the importance of jointly modeling spatial structure and experimental factors in spatial microbiome studies and provide new insights into tumor–microbiome interactions, laying the groundwork for future methodological and biological investigations in spatial microbiome analysis.