2023 Fiscal Year Annual Research Report
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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| Allocation Type | Multi-year Fund |
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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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| Keywords | integrative analysis / variational inference / Bayesian model / variable selection / microbiome / metabolome |
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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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