Statistical Methods for Microbiome and Metagenomics

微生物组和宏基因组学的统计方法

• 批准号: 9983111
• 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 2022-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9983111
• 关键词: 16S ribosomal RNA sequencing; Address; Algorithmic Analysis; Area; Base Composition; Big Data; Biological; Biological Markers; Biological Process; Cardiovascular Diseases; Charge; Chronic Kidney Failure; Chronic Kidney Insufficiency; Collaborations; Collection; Communities; Complex; Computational algorithm; Computing Methodologies; Crohn' s disease; Data; Data Analyses; Data Set; Development; Diabetes Mellitus; Disease; Disease Progression; Documentation; Ecosystem; Evaluation; Genes; Grant; Health; High-Throughput Nucleotide Sequencing; Human; Human Microbiome; Human body; Inflammatory Bowel Diseases; Lead; Malignant Neoplasms; Mediating; Mediation; Metagenomics; Methodology; Methods; Microbe; Modeling; Nature; Network-based; Nuclear; Obesity; Organism; Outcome; Pathway Analysis; Patients; Pediatric Crohn' s disease; 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; human model; insight; interest; metagenome; metagenomic sequencing; method development; microbial; microbiome; microbiome composition; microbiome research; microorganism interaction; model development; multidimensional data; next generation sequencing; novel; obesity in children; outcome forecast; programs; response; simulation; treatment effect; treatment response

Abstract The broad, long-term objective of this project concerns the development of novel statistical methods and computational tools for statistical and probabilistic modeling of human microbiome and shotgun metagenomic data motivated by important biological questions and experiments. The specific 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 effects of microbiome mediating the effect 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 field (MRF) models for microbial interaction network construction and for differential network analysis. These problems are all motivated 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 field based microbial network and differential network analysis. The new methods can be applied to both 16S rRNA and shotgun metagenomic sequencing data and will ideally facilitate the identifications of microbial composition, subcomposition and microbial networks underlying various complex human diseases and biological processes. The project will also investigate the robustness, power and efficiencies 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 methods, and for the evaluation of the performance of these methods through extensive simulations 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 offer 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 该项目广泛的长期目标涉及开发新的统计方法和计算工具，用于对人类微生物组和鸟枪元基因组数据进行统计和概率建模 受到重要的生物学问题和实验的激励。当前项目的具体目标是开发 用于分析16S rRNA的新的统计模型、新的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超基因组测序数据。该项目的重点是 基于模型的多样本微生物组组成定量方法的研究进展 用于量化微生物组中介效应的成分中介分析方法 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 稀疏离散马尔可夫随机场(MRF)模型的大规模多重测试推理 用于微生物相互作用网络的构建和差异网络分析。这些问题都是由PI与宾夕法尼亚大学研究人员在克罗恩病、儿童时期的元基因组研究上的密切合作引起的 肥胖与慢性肾脏病(CKD)患者的疾病进展)。方法取决于小说 集成生物学见解和建模稀疏计数数据、高维成分的方法 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 基于微生物网络和马尔可夫随机场的税收丰度估计、成分中介模型和 差分网络分析。新方法可以应用于16S rRNA和鸟枪式元基因组测序数据，并将理想地促进微生物组成、亚组分和微生物的鉴定 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 比较了这些方法的稳健性、计算能力和效率，并与现有的方法进行了比较。此外, 该项目将开发实用和可行的计算机程序来实施所提出的方法，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，进行各种正在进行的微生物组研究。这个 本文提出的工作将有助于建立元基因组测序数据和HIGH模型的统计方法 维度组成数据，MFR模型的理论推理方法，并提供对每个模型的见解 由各种数据集表示的生物区域。根据此赠款开发的所有计划，并详细说明 文件将免费提供给感兴趣的研究人员。