Statistical Methods for Microbiome and Metagenomics

摘要 该项目的长期目标是开发新的统计方法和计算工具，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 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 组成中介分析方法，以量化微生物组介导效应的影响 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）模型上的大规模多重检验过程的推理 用于微生物相互作用网络构建和差分网络分析。这些问题都是由于PI与宾夕法尼亚大学研究人员在克罗恩病、儿童和青少年的宏基因组研究方面的密切合作而引起的。 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、组成调解模型和基于马尔可夫随机场的微生物网络， 微分网络分析新方法可应用于16 S rRNA和鸟枪法宏基因组测序数据，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，并将其与现有方法进行比较。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，用于实施所提出的方法，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。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 维度组成数据，MFR模型的理论推理方法，并提供对每个 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。

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.