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

与其他生物，特别是土壤微生物群相互作用的影响，作为栽培管理中的一个关键因素，正受到越来越多的关注。土壤微生物群在养分循环、植物生长和健康以及土壤整体质量中起着至关重要的作用。下一代测序使土壤微生物群的大规模基因组（宏基因组）和功能分析成为可能。为了确定微生物组数据中个体间变异的异质性模式，我引入了随机变分变量选择（SVVS）来确定具有代表性的微生物物种的最小大小核心集，该方法显著提高了聚类方法的性能，大大减少了计算负担并捕获了生物变异。我的新方法发表在Microbiome杂志上（IF: 16.837）。目前，我提出了一个新的框架——综合随机变分变量选择（I- svvs），这是我在之前的论文中对高维微生物组数据的随机变分变量选择的扩展。I-SVVS方法针对不同类型组学数据分别采用特定的贝叶斯混合模型，即微生物组学数据采用无限Dirichlet多项式混合（DMM）模型，代谢组学数据采用无限高斯混合模型，以提高聚类过程的准确性和计算时间。该方法还可以在多组学微生物组数据中识别一组关键的代表性变量。在大豆、小鼠和人类的微生物组和代谢组整合的三个大型数据集上展示了I- svvs。

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.