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.

抽象的 该项目的广泛、长期目标涉及开发新颖的统计方法和计算方法。 用于人类微生物组和鸟枪宏基因组数据统计和概率建模的推论工具 受到重要的生物学问题和实验的启发。当前项目的具体目标是开发 用于 16S rRNA 分析的新统计模型、新颖推理程序和快速计算算法 以及大规模人类微生物组研究中的鸟枪法宏基因组测序数据。该项目的重点是 开发基于模型的多样本方法来量化微生物组的组成和发育 成分介导分析方法，以量化微生物组介导的影响 治疗/危险因素对结果的影响。此外，该项目还将开发新的统计方法 推理，包括稀疏离散马尔可夫随机场 (MRF) 模型的大规模多重测试程序 用于微生物相互作用网络构建和微分网络分析。这些问题都是动机 PI 与宾夕法尼亚大学研究人员在克罗恩病、儿童宏基因组研究方面的密切合作感到高兴 慢性肾病（CKD）患者的肥胖和疾病进展）。方法取决于新颖性 整合生物学见解和稀疏计数数据建模方法、高维成分 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 税收丰度估计、成分中介模型和基于马尔可夫随机场的微生物网络和 微分网络分析。新方法可应用于 16S rRNA 和鸟枪法宏基因组测序。 测序数据，将理想地促进微生物组成、亚组成和微生物的识别 各种复杂的人类疾病和生物过程背后的网络。该项目还将调查 这些方法的稳健性、功效和效率，并将其与现有方法进行比较。此外， 该项目将开发实用且可行的计算机程序来实施拟议的方法 ods，并通过广泛的模拟和分析来评估这些方法的性能 通过 PI 与宾夕法尼亚大学医生和生物学家的合作，正在进行各种微生物组研究。这 这里提出的工作将为宏基因组测序数据和高通量建模提供统计方法。 维度成分数据、MFR 模型的理论推理方法，并提供对每个方面的见解 由各种数据集代表的生物领域。此项拨款下开发的所有计划均详细 文件将免费提供给感兴趣的研究人员。