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

植物-微生物群相互作用的多组学分层建模和高速算法的开发

• 批准号: 22KJ0656
• 负责人: Dang Tung
• 金额: $ 1.41万
• 依托单位: The University of Tokyo
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for JSPS Fellows
• 财政年份: 2023
• 资助国家: 日本
• 起止时间: 2023-03-08 至 2024-03-31
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-22KJ0656/
• 关键词: integrative analysis; variational inference; Bayesian model; variable selection; drought irrigation; microbiome; stochastic optimization; metabolome

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.

与其他生物，特别是土壤微生物群的相互作用的影响，作为种植管理的一个关键因素，正在得到越来越多的关注。土壤微生物群在养分循环、植物生长和健康以及整体土壤质量中起着至关重要的作用。下一代测序技术能够对土壤微生物进行大规模的基因组（宏基因组）和功能分析。为了识别微生物组数据中个体间变异性的异质模式，我引入了随机变分变量选择（SVVS）来识别代表性微生物物种的最小尺寸核心集，这显著提高了聚类方法的性能，大大降低了计算负担并捕获了生物变异性。我的新方法发表在Microbiome journal（IF：16.837）。目前，我提出了一个新的框架，整合随机变分变量选择（I-SVVS），这是我以前的论文中针对高维微生物组数据的随机变分变量选择的扩展。I-SVVS方法针对不同类型的组学数据中的每一种解决了特定的贝叶斯混合模型，即，微生物组数据的无限Dirichlet多项混合（DMM）模型和代谢组数据的无限高斯混合模型，以提高聚类过程的准确性和计算时间。该方法还可以识别多组学微生物组数据中的一组关键的代表性变量。我在三个大型数据集上展示了I-SVVS，整合了来自大豆，小鼠和人类的微生物组和代谢组。

项目成果

高次元マイクロバイオームデータのクラスタリン グと代表的な微生物種の選択を可能にする方

一种能够对高维微生物组数据进行聚类并选择代表性微生物物种的方法。

• 发表时间: 2022
• 作者: Dang;Tung and Kumaishi;Kie and Usui;Erika and Kobori;Shungo and Sato;Takumi and Ichihashi;Yasunori and Yusuke;Toda and Yamasaki;Yuji and Tsujimoto;Hisashi and Iwata;Hiroyoshi;Tung Dang and Hiroyoshi Iwata
• 通讯作者: Tung Dang and Hiroyoshi Iwata

Automatic package for optimized decoding of neuroimaging data supported by forward variable selection

用于由前向变量选择支持的神经影像数据优化解码的自动包

• 发表时间: 2022
• 作者: Tung Dang;Alan S. R. Fermin;and Maro G. Machizawa
• 通讯作者: and Maro G. Machizawa

oFVSD: A Python package of optimized forward variable selection decoder for high-dimensional neuroimaging data

oFVSD：用于高维神经影像数据的优化前向变量选择解码器的 Python 包

• DOI: 10.1101/2022.12.25.521906
• 发表时间: 2022
• 期刊: bioRxiv
• 作者: Dang Tung;Fermin Alan S. R.;Machizawa Maro G.
• 通讯作者: Machizawa Maro G.