Statistical Methods for Microbiome and Metagenomics

微生物组和宏基因组学的统计方法

• 批准号: 9447252
• 负责人: Hongzhe Lee
• 金额: $ 46.08万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-15 至 2021-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9447252
• 关键词: Address; Algorithmic Analysis; Area; Base Composition; Big Data; Biological; Biological Markers; Biological Process; Cardiovascular Diseases; Cations; Charge; Childhood; Chronic; Chronic Kidney Failure; Collaborations; Collection; Communities; Complex; Computational algorithm; Computing Methodologies; Crohn' s disease; Data; Data Analyses; Data Set; Databases; Development; Diabetes Mellitus; Disease; Disease Progression; Documentation; Ecosystem; Evaluation; Genes; Grant; Health; High-Throughput Nucleotide Sequencing; Human; Human Microbiome; Human body; Intestines; Kidney; Lead; Malignant Neoplasms; Mediating; Mediation; Metagenomics; Methodology; Methods; Microbe; Modeling; Nature; Network-based; Nuclear; Obesity; Organism; Outcome; Pathway Analysis; Patients; Pennsylvania; Performance; Phenotype; Physicians; Play; Procedures; Public Health; Research; Research Personnel; Ribosomal RNA; Risk Factors; Role; Sampling; Shotguns; Statistical Data Interpretation; Statistical Methods; Statistical Models; Taxes; Technology; Testing; Universities; Variant; Work; base; cohort; computer program; computerized tools; disease diagnosis; experimental study; gut microbiome; high dimensionality; human disease; insight; interest; metagenome; metagenomic sequencing; method development; microbial; microbiome; microorganism interaction; model development; next generation sequencing; novel; obesity in children; outcome forecast; programs; response; simulation; treatment response

Abstract The broad, long-term objective of this project concerns the development of novel statistical methods and com- putational tools for statistical and probabilistic modeling of human microbiome and shotgun metagenomic data motivated by important biological questions and experiments. The speci c aim of the current project is to develop new statistical models, novel inference procedures, and fast computational algorithms for the analysis of 16S rRNA and shotgun metagenomic sequencing data in large-scale human microbiome studies. The project focuses on the development of model-based multi-sample approaches for quantifying microbiome compositions and development methods of compositional mediation analysis in order to quantify the e ects of microbiome mediating the e ect of treatment/risk factor on outcomes. In addition, this project will also develop novel methods for statistical inference including large-scale multiple testing procedures on sparse discrete Markov random eld (MRF) models for microbial interaction network construction and for di erential network analysis. These problems are all moti- vated by the PI's close collaborations with Penn investigators on metagenomic studies of Crohn disease, childhood obesity and disease progression among patients with chronic kidney disease (CKD)). The methods hinge on novel integration of biological insights and methods for modeling sparse count data, high dimensional compositional data analysis and network-based analysis, including nuclear-norm penalized maximum likelihood estimation for tax abundance estimation, compositional mediation model and Markov random eld based microbial network and di erential network analysis. The new methods can be applied to both 16S rRNA and shotgun metagenomic se- quencing data and will ideally facilitate the identi cations of microbial composition, subcomposition and microbial networks underlying various complex human diseases and biological processes. The project will also investigate the robustness, power and eciencies of these methods and compare them with existing methods. In addition, this project will develop practical and feasible computer programs for the implementation of the proposed meth- ods, and for the evaluation of the performance of these methods through extensive simulatons and analysis of various on-going microbiome studies through the PI's collaborations with Penn physicians and biologists. The work proposed here will contribute statistical methodology for modeling metagenomic sequencing data and high dimensional compositional data, theoretical inference methods for the MFR models and o er insights into each of the biological areas represented by the various data sets. All programs developed under this grant and detailed documentation will be made available free-of-charge to interested researchers.

摘要 该项目的广泛，长期目标涉及新的统计方法的发展和COM- 用于人类微生物组和鸟枪宏基因组数据的统计和概率建模的推定工具 被重要的生物学问题和实验所激励。本项目的具体目标是开发 新的统计模型，新的推理程序，和快速计算算法的分析16 S rRNA 和大规模人类微生物组研究中的鸟枪宏基因组测序数据。该项目的重点是 开发基于模型的多样品方法，用于定量微生物组组成和开发 成分中介分析方法，以量化微生物组介导的效应 治疗/风险因素对结果的影响。此外，该项目还将开发新的统计方法， 包括稀疏离散马尔可夫随机场（MRF）模型上大规模多重检验过程的推理 用于微生物相互作用网络的构建和代谢网络的分析。这些问题都是动机- 通过PI与宾夕法尼亚大学研究人员在克罗恩病，儿童， 慢性肾病（CKD）患者的肥胖和疾病进展）。这些方法取决于小说 集成生物学见解和方法，用于建模稀疏计数数据、高维组成 数据分析和基于网络的分析，包括核范数惩罚最大似然估计， 税丰估计、组分中介模型和基于马尔可夫随机场的微生物网络， 扩散网络分析新方法可应用于16 S rRNA和鸟枪宏基因组序列。 测序数据，并将理想地促进微生物组成、亚组成和微生物的鉴定。 各种复杂的人类疾病和生物过程的基础网络。该项目还将调查 这些方法的鲁棒性，功率和效率，并与现有的方法进行比较。此外，本发明还提供了一种方法， 该项目将开发实用可行的计算机程序来实施拟议的方法， ods，并通过大量的模拟和分析， 通过PI与宾夕法尼亚大学医生和生物学家的合作进行各种正在进行的微生物组研究。的 这里提出的工作将有助于建模宏基因组测序数据的统计方法和高 三维组成数据，MFR模型的理论推理方法，以及对每个模型的深入了解。 各种数据集所代表的生物区域。所有在此资助下开发的项目， 文件将免费提供给感兴趣的研究人员。