Collaborative Research: Novel Statistical Tools for Metagenomics and Metabolomics Data

合作研究：宏基因组学和代谢组学数据的新型统计工具

• 批准号: 1903139
• 负责人: Jun Liu
• 金额: $ 35万
• 依托单位: Harvard University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-06-15 至 2023-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1903139&HistoricalAwards=false
• 关键词: Collaborative; Research; Novel; Statistical; Tools

Accumulating evidence suggests that disrupting intestinal microbial ecosystems can cause many serious diseases including chronic diseases such as coronary heart disease (CHD), neurobehavioral diseases such as autism, inflammatory diseases such as inflammatory bowel disease (IBD) and etc. For example, the developing infant intestinal microbiome has been implicated in central myelination and the maturation and function of microglia (CNS immune cells), a core deficiency in Autism Spectrum Disorders. Thus, the study of the gut microbial distributions and their metabolites are very important to find new therapeutic targets for many diseases. However, compared to the huge amounts of medical research on human cells, our understanding of the microbial ecosystem is very limited: the biodiversity of them is barely studied, not to mention their interactions with the human host. This project will develop a suite of statistical theory and methods as well as computational tools to facilitate the understanding of the intestinal microbial ecosystem. The proposed methods are fast, efficient, and highly accurate. They can be widely applied to any metagenomic and metabolomic investigations. Graduate students will be trained by participating in research activities. The main goal of this project is to extend our knowledge of intestinal microbial ecosystem by developing novel quantitative methods for microbial species and their metabolites detection, identification, and quantification in various diseases. The sensitivity and specificity of our methods permit accurate detection of microbial species at very low coverage levels. This is a translational technology that should find substantial use in biomedical researches and drug developments. More specifically, the PIs shall develop methods for identifying microbial species especially unknown species, reducing error in metabolomic analysis, estimating microbial and metabolite distributions, quantifying microbial or metabolites distributional differences that are associated with diseases, integrating metagenomic and metabolomic analysis together to study the microbial ecosystem, building analytical models to link metabolite profiling with species profiling to understand how metabolites interact with genetic contents and eventually affect cell metabolism.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

越来越多的证据表明，破坏肠道微生物生态系统会导致许多严重疾病，包括冠心病（CHD）等慢性疾病、自闭症等神经行为疾病、炎症性肠病（IBD）等炎症性疾病等。肠道微生物群与中枢髓鞘形成以及小胶质细胞（中枢神经系统免疫细胞）的成熟和功能有关，而小胶质细胞是自闭症谱系障碍的核心缺陷。因此，研究肠道微生物分布及其代谢产物对于寻找许多疾病的新治疗靶点非常重要。然而，与对人体细胞的大量医学研究相比，我们对微生物生态系统的了解非常有限：它们的生物多样性几乎没有被研究，更不用说它们与人类宿主的相互作用了。该项目将开发一套统计理论和方法以及计算工具，以促进对肠道微生物生态系统的理解。所提出的方法快速、高效且高度准确。它们可以广泛应用于任何宏基因组和代谢组学研究。研究生将通过参加研究活动来接受培训。 该项目的主要目标是通过开发新的定量方法来扩展我们对肠道微生物生态系统的了解，用于各种疾病中微生物物种及其代谢物的检测、识别和定量。 我们方法的灵敏度和特异性允许在非常低的覆盖水平下准确检测微生物物种。这是一项转化技术，应该在生物医学研究和药物开发中得到广泛应用。更具体地说，PI应开发识别微生物物种特别是未知物种的方法，减少代谢组学分析中的误差，估计微生物和代谢物分布，量化与疾病相关的微生物或代谢物分布差异，将宏基因组学和代谢组学分析结合在一起以研究微生物生态系统，建立分析模型将代谢物分析与物种分析联系起来，以了解代谢物如何与遗传内容相互作用并最终影响细胞代谢。该奖项反映了通过使用基金会的智力价值和更广泛的影响审查标准进行评估，NSF 的法定使命被认为值得支持。

项目成果

Neuronized Priors for Bayesian Sparse Linear Regression

• DOI: 10.1080/01621459.2021.1876710
• 发表时间: 2018-09
• 期刊: Journal of the American Statistical Association
• 影响因子: 3.7
• 作者: Minsuk Shin;Jun S. Liu

On the optimality of sliced inverse regression in high dimensions

• DOI: 10.1214/19-aos1813
• 发表时间: 2017-01
• 期刊: The Annals of Statistics
• 作者: Q. Lin;Xinran Li;Dongming Huang;Jun S. Liu

Stratification and Optimal Resampling for Sequential Monte Carlo

顺序蒙特卡罗的分层和最佳重采样

• DOI: 10.1093/biomet/asab004
• 发表时间: 2020-04
• 期刊: Biometrika
• 影响因子: 2.7
• 作者: Yichao Li;Wenshuo Wang;Ke Deng;Jun S. Liu
• 通讯作者: Jun S. Liu

Bayesian bi-clustering methods with applications in computational biology

• DOI: 10.1214/22-aoas1622
• 发表时间: 2020-07
• 期刊: The Annals of Applied Statistics
• 作者: Han Yan;Jiexing Wu;Y. Li;Jun S. Liu

On Posterior Consistency of Bayesian Factor Models in High Dimensions

• DOI: 10.1214/21-ba1281
• 发表时间: 2020-06
• 期刊: Bayesian Analysis
• 影响因子: 4.4
• 作者: Yucong Ma;Jun S. Liu