植物-微生物叢相互作用のマルチオミクス階層モデリングとその高速アルゴリズムの開発

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The influence of interactions with other organisms, particularly soil microbiota, is gaining attention as a crucial factor in cultivation management. Soil microbiota plays a vital role in nutrient cycling, plant growth and health, and overall soil quality. Next-generation sequencing enables large-scale genomic (metagenomic) and functional analysis of the soil microbiota. To identify the heterogeneous pattern of individual-to-individual variability in the microbiome data, I introduced the stochastic variational variable selection (SVVS) to identify a minimal-size core set of representative microbial species that significantly improved the performances of clustering method, considerably reduced computational burden and captured biological variabilities. My novel methodology was published in Microbiome journal (IF: 16.837).Currently, I propose a novel framework, integrative stochastic variational variable selection (I-SVVS), which is an extension of stochastic variational variable selection for high-dimensional microbiome data in my previous paper. The I-SVVS approach address a specific Bayesian mixture model for each of different types of omics data, i.e., an infinite Dirichlet multinomial mixture (DMM) model for microbiome data and an infinite Gaussian mixture model for metabolomic data, to improve the accuracy and computational time of cluster process. The method can also identify a critical set of representative variables in multiomics microbiome data. I demonstrate I-SVVS on three large datasets in integration of microbiome and metabolome from soybean, mice and human.