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
| Project/Area Number |
22KJ0656
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| Project/Area Number (Other) |
21J21850 (2021-2022)
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| Research Category |
Grant-in-Aid for JSPS Fellows
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| Allocation Type | Multi-year Fund (2023)
Single-year Grants (2021-2022) |
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| Section | 国内 |
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| Review Section |
Basic Section 39010:Science in plant genetics and breeding-related
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| Research Institution | The University of Tokyo |
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Principal Investigator |
Dang Tung 東京大学, 農学生命科学研究科, 特別研究員(DC1)
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| Project Period (FY) |
2023-03-08 – 2024-03-31
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| Project Status |
Completed (Fiscal Year 2023)
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| Budget Amount *help |
¥2,200,000 (Direct Cost: ¥2,200,000)
Fiscal Year 2023: ¥700,000 (Direct Cost: ¥700,000)
Fiscal Year 2022: ¥700,000 (Direct Cost: ¥700,000)
Fiscal Year 2021: ¥800,000 (Direct Cost: ¥800,000)
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| Keywords | integrative analysis / variational inference / Bayesian model / variable selection / microbiome / metabolome / drought irrigation / stochastic optimization |
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| Outline of Research at the Start |
High-dimensional multiomics microbiome data plays an important role in elucidating microbial communities’ interactions with their hosts and environment in critical diseases and ecological changes. I develop a novel framework, which is an extension of stochastic variational variable selection for high-dimensional microbiome data. My approach address a specific Bayesian mixture model for each of different types of omics data, to improve the accuracy and computational time of cluster process. I demonstrate integration of microbiome and metabolome from soybean, mice and human.
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| Outline of Annual Research Achievements |
In my Ph.D. research (DC1), the methodologies of Random Forest with Forward Variable Selection (RF-FVS), Stochastic Variational Variable Selection (SVVS), and Integrative Stochastic Variational Variable Selection (I-SVVS) introduce crucial tools for multi-omics microbiome data analysis. These approaches effectively tackle challenges like high dimensionality, computational efficiency, and feature selection. While these methods have demonstrated their worth in analyzing 16S ribosomal RNA microbiome datasets, they do have limitations. Factors like short read lengths from sequencing, potential sequencing errors, and variability stemming from sequencing region choices can limit the accuracy and comprehensiveness of taxonomic profiles. Future expansions of these methodologies will involve comprehensive analyses integrating diverse host databases. These include the host genome, transcriptome, proteome, and metabolome, alongside microbiome databases covering whole metagenome, metatranscriptome, and metaproteome. This broader scope will accommodate various types of data, including multicategory data (e.g., copy number states: loss/normal/gain), binary data (e.g., mutation status), and count data (e.g., sequencing data).
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Report
(3 results)
Research Products
(15 results)
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[Journal Article] Stochastic variational variable selection for high-dimensional microbiome data2021
Author(s)
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
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Journal Title
bioRxiv
Volume: 1
Pages: 1-19
DOI
Related Report
Open Access
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