Distance-based Panomic Analytics for Microbiome Data

项目摘要 我们研究微生物组的能力是由同样的技术，使我们能够量化主机 更深入、更精确地检测生理状态，包括基因组学的先进高通量测序 和转录组学;代谢组学、蛋白质组学和脂质组学的质谱分析; 循环细胞群的表征。宿主和微生物组全组学数据的整合是 未来生物医学发现的路线图。这类研究的一个例子是综合人类微生物组 项目（iHMP），该项目目前正在三个国家生成微生物及其宿主环境的panomic数据。 不同的疾病（糖尿病、肠易激综合征、早产）。缺乏适当的分析 数据和陡峭的曲线，其验证和采用是一个主要关注的社会。的主要挑战 在panomic-scale微生物组数据集的推理是克服“维数的诅咒”。局部因果 学习已被证明是有用的，使发现与高维数据，而远程学习是 多元数据分析的一个有前途的范例。我们建议将这些联合收割机结合起来开发下一个 生成panomic数据分析，并将这些工具直接提供给生物医学研究人员。的 该项目的目标是：（1）开发基于距离的综合panomic集成分析;（2）开发 自上而下的远程子系统相互依存学习方法。总体目标是 利用目标1和目标2中的方法开发面向用户的应用程序，并将其应用于现有的几个系统， 研究生成全基因组数据。分析、应用和教育资源（案例研究和 该项目产生的教程）将使生物医学界能够以一种 连贯和全面的方式。该项目产生的方法和工具将支持新的生物医学 发现。 ! !

PROJECT SUMMARY Our ability to study the microbiomes is enabled by the same technologies that allow us to quantify the host physiological state at greater depth and precision, including advanced high-throughput sequencing for genomics and transcriptomics; mass spectrometry for metabolomics, proteomics, and lipidomics; and flow-cytometry for characterization of circulating cell populations. The integration of host and microbiome panomic data is the roadmap for future biomedical discoveries. One example of such studies is the Integrative Human Microbiome Project (iHMP), which is currently generating panomic data on microbes and their host environment in three different diseases (diabetes, irritable bowel disease, pre-term delivery). Lack of appropriate analytics for these data and a steep curve for their validation and adoption is a major concern for the community. The main challenge of inference in panomic-scale microbiome datasets is overcoming the ‘curses of dimensionality’. Local causal learning has proven useful for making discoveries with high-dimensional data, while distance-based learning is a promising paradigm for multivariate data analysis. We are proposing to combine these to develop the next generation of panomic data analytics and make these tools available directly to the biomedical investigators. The aims of this project are: (1) Develop analytics for distance-based omnibus panomic integration; (2) Develop methodology for top-down distance-based sub-system interdependence learning. The overarching goal is to develop user-facing applications utilizing the methodologies in Aims 1 and 2 and apply those in several existing studies generating panomic data. The analytics, applications, and educational resources (case studies and tutorials) resulting from this project will enable the biomedical community to study panomic-scale datasets in a coherent and comprehensive way. The methods and tools resulting from this project will support new biomedical discoveries. ! !