Novel Statistical Methods for Oral Microbiome Data Analysis

口腔微生物组数据分析的新统计方法

• 批准号: 10657765
• 负责人: Ryan T. Demmer
• 金额: $ 14.24万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10657765
• 关键词: 16S ribosomal RNA sequencing; Affect; Biological; Biological Markers; Bypass; Cardiovascular Diseases; Clinical; Clinical Trials; Complex; Computer software; Data; Data Analyses; Dental caries; Development; Diabetes Mellitus; Diet; Disease; Ecology; Equation; Exposure to; Foundations; Future; Glucose Intolerance; Goals; Health; Home; Human; Individual; Inflammatory; Insulin Resistance; Intuition; Linear Regressions; Link; Measures; Mediating; Mediation; Mediator; Metabolic; Metabolic Marker; Metadata; Methodology; Methods; Microbe; Modeling; Mouth Diseases; Noise; Oral; Oral cavity; Oral health; Outcome; Parents; Participant; Pathway interactions; Periodontitis; Phenotype; Play; Prediabetes syndrome; Procedures; Reproducibility of Results; Research; Resources; Respiratory System; Risk; Risk Factors; Risk Marker; Role; Sampling; Signal Transduction; Solid; Statistical Data Interpretation; Statistical Methods; Taxonomy; Testing; complex data; epidemiology study; follow-up; gastrointestinal system; high dimensionality; inflammatory marker; innovation; insight; interest; microbial; microbiome; microbiome analysis; microbiome research; microbiota; new therapeutic target; novel; oral infection; oral microbial community; oral microbiome; physical state; predict clinical outcome; recruit; response; semiparametric; sound; success; user-friendly

PROJECT SUMMARY The oral cavity harbors a great diversity of microbiota that play an important role in human health. Studies have shown that alterations in the oral microbiome are closely associated with oral diseases such as periodontitis and dental caries, as well as numerous extra-oral disorders such as diabetes and cardiovascular diseases. Large- scale epidemiological studies, including our motivating Oral Infections, Glucose Intolerance and Insulin Resistance Study (ORIGINS), have collected rich data to further investigate the association and mechanistic pathways between the oral microbial communities and health outcomes. However, microbiome data are complex, subject to constraints such as compositionality, zero inflation, overdispersion, taxonomic hierarchy, and high dimensionality. Most existing statistical analyses rely on ad hoc data manipulation and transformation approaches to bypass these constraints, often leading to uninterpretable and irreproducible results. There is a pressing need for more principled statistical methods. This proposal will develop novel association and mediation methods that fully accommodate the unique data features and harness the power of microbiome data. We will build upon a nascent relative-shift regression framework that provides a highly intuitive and interpretable way of modeling compositional data. In Aim 1, we will significantly extend the scope of the relative-shift model by generalizing it to non-Gaussian responses and nolinear relationships. The new methods can better depict the complex association between microbiota and diverse phenotypes. In Aim 2, we will develop novel mediation methods with microbial exposures (Sub-Aim 2a) and microbial mediators (Sub-Aim 2b). The methods will provide a solid foundation for quantifying and testing mediation effects pertaining to microbiome data. The proposed methods will be applied to the ORIGINS data to identify crucial oral microbiota associated with oral/extra-oral phenotypes and to delineate the mediation pathways. The methods and an accompanying software package will also have general utilities in other microbiome studies and promise to enhance our understanding of the microbiome’s critical role in human health.

项目摘要 口腔含有大量的微生物群，在人类健康中起着重要作用。研究有 表明口服微生物组的改变与口腔疾病（例如牙周炎和 龋齿，以及许多口腔外疾病，例如糖尿病和心血管疾病。大的- 比例流行病学研究，包括我们激励的口腔感染，葡萄糖不耐症和胰岛素 抗药性研究（起源），已收集丰富的数据，以进一步研究关联和机理 口腔微生物群落与健康成果之间的途径。但是，微生物组数据是 复杂，受限制，例如组成性，零通货膨胀，过度分散，分类层次结构， 和高维度。大多数现有的统计分析都取决于临时数据操纵和转换 绕过这些约束的方法，通常会导致无法解释和不可再生的结果。有一个 迫切需要更多的主要统计方法。该建议将发展新颖的协会和调解 完全适应独特数据功能并利用微生物组数据的功能的方法。我们将 建立在一个新生的相对偏移回归框架的基础上，该框架提供了一种高度直观且可解释的方式 建模复合数据。在AIM 1中，我们将通过 将其推广到非高斯反应和野性关系。新方法可以更好地描述 菌群与潜水员表型之间的复杂关联。在AIM 2中，我们将开发新颖的调解 具有微生物暴露（Sub-Aim 2a）和微生物介质（Sub-Aim 2b）的方法。这些方法将提供 量化和测试与微生物组数据有关的介导效应的坚实基础。提议 方法将应用于原始数据，以识别与口服/口腔外的至关重要的口服微生物群 表型并描述调解途径。方法和累积软件包将 在其他微生物组研究中也有一般公用事业，并有望增强我们对 微生物组在人类健康中的关键作用。

项目成果

It's all relative: Regression analysis with compositional predictors.

• DOI: 10.1111/biom.13703
• 发表时间: 2023-06
• 期刊: BIOMETRICS
• 影响因子: 1.9
• 作者: Li, Gen;Li, Yan;Chen, Kun
• 通讯作者: Chen, Kun

Principal Amalgamation Analysis for Microbiome Data.

• DOI: 10.3390/genes13071139
• 发表时间: 2022-06-24
• 期刊: Genes
• 影响因子: 3.5