Bayesian multivariate 3D spatial modeling for microbiome image analysis

用于微生物组图像分析的贝叶斯多元 3D 空间建模

• 批准号: 10586135
• 负责人: KYU HA LEE
• 金额: $ 61.99万
• 依托单位: BRIGHAM AND WOMEN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-05-04 至 2025-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10586135
• 关键词: 3-Dimensional; Address; Affect; Analysis of Variance; Architecture; Area; Astronomy; Bacteria; Biological; Calibration; Cells; Characteristics; Clinical; Communities; Complex; Computer software; Confocal Microscopy; Data; Data Set; Dependence; Development; Dimensions; Disease; Ecology; Environment; Epithelium; Evaluation; Exhibits; Fluorescent in Situ Hybridization; Forestry; Free Will; Goals; Health; Hour; Human; Image; Image Analysis; Imaging Techniques; Joints; Knowledge; Label; Location; Maps; Mathematics; Measures; Medical Imaging; Methods; Microbe; Microbial Biofilms; Microbiology; Modeling; Multivariate Analysis; Nutrient; Oral; Oral cavity; Organelles; Pathogenicity; Pattern; Performance; Physiology; Play; Process; Radial; Recording of previous events; Role; Running; Salivary; Sampling; Site; Slice; Spatial Distribution; Statistical Methods; Structure; Surface; System; Taxon; Taxonomy; Techniques; Technology; Testing; Three-Dimensional Image; Tongue; Visualization; Work; biomedical imaging; cell type; community organizations; computerized tools; data integration; disorder control; disorder prevention; experience; flexibility; high dimensionality; improved; innovation; insight; microbial; microbial community; microbiome; microbiome research; microorganism interaction; novel; oral biofilm; organizational structure; scale up; skills; software development; spectrograph; statistics; three-dimensional modeling; tool; two-dimensional; user-friendly; virtual

Bacteria play critical beneficial and harmful roles in human health. Living in biofilm communities, one species may attack, protect, or provide nutrients for neighboring species. These interactions determine the community's net effects. Clarifying community organization is needed to understand how biofilm affects health. To begin to meet this need, we developed an imaging technique, Combinatory Labeling and Spectral Imaging Fluorescence in Situ Hybridization (CLASI-FISH), which displays how taxa's cells are located relative to each other and to host cells. Yet biofilm's complex, three-dimensional (3D) architecture is poorly captured by commonly used measures, such as intercellular distances or global biofilm volume for one or two taxa. Here, we propose to extend Log Gaussian Cox process models (LGCP) to describe and test hypotheses about human biofilm architecture, a novel application. Computational burden limits existing LGCP models for geostatistical data to datasets with thousands of observations. These methods cannot be applied to biofilm image data typically containing millions of observations. In preliminary work on two-dimensional (2D) biofilm images, we have successfully scaled up multivariate LGCPs for six taxa. Estimated pairwise cross-correlation functions differ in univariate analyses, which ignore other taxa's locations, versus multivariate analyses, which leverage taxa's joint spatial distribution. We propose statistical innovations to address challenges raised by, but not unique to, 3D biofilm images. Comparing biofilm across sample groups defined experimentally or based on exposure history requires integrating data across subjects' images that lack true spatial correspondence. Further, 3D spatial analyses have not been applied to multivariate data with millions of observations. The goal of this proposal is therefore to build a Bayesian multivariate 3D LGCP that incorporates different images—thereby allowing for non-spatial covariate factors—by applying a separate coordinate system to each image. This proposal has three parts: (a) the development of novel multivariate 3D spatial analysis methods (aims 1-3), (b) evaluation of a hypothesis regarding the spatial structure of human tongue microbiome (aim 4), and (c) software development and dissemination, based on best practices (aim 5). The interdisciplinary team has a deep skill set and experience developing Bayesian high-dimensional multivariate analysis methods. The core innovation proposed is to integrate non-spatial covariates with multivariate spatial data across 3D images lacking a common coordinate system. Sample accessibility and prior biological knowledge make the oral cavity the best starting point to develop a flexible modeling framework that will allow testing of hypotheses regarding microbial interactions and associations with host characteristics. This is a fundamental shift for how such images will be analyzed, potentially providing new insight into the role of oral microbes. In advancing capabilities for studying multivariate 3D spatial patterns across images, the mathematical adaptations and software we develop will have the potential to yield a breakthrough technology.

细菌在人类健康中起着至关重要的有益和有害作用。生活在生物膜社区，一个 物种可能会攻击，保护或为邻近物种提供营养。这些互动决定了 社区的净效应。需要澄清社区组织，以了解生物膜如何影响健康。 为了开始满足这种需求，我们开发了成像技术，组合标签和光谱 成像荧光原位杂交（CLASI-FISH），该杂交显示分类单元的相对位置 彼此和宿主细胞。然而，生物膜的复杂的三维（3D）结构的捕获很差 通常使用的措施，例如一个或两个分类单元的细胞间距离或全球生物膜体积。 在这里，我们建议扩展日志高斯Cox过程模型（LGCP）以描述和检验假设 关于人类生物膜建筑，一种新颖的应用。计算燃烧限制现有的LGCP模型 对数据集的地理数据进行了数千个观察结果。这些方法不能应用于生物膜 图像数据通常包含数百万个观测值。在二维（2D）生物膜的初步工作中 图像，我们已成功地扩大了六个分类单元的多元LGCP。估计的成对互相关 单变量分析的功能不同，该分析忽略了其他分类单元的位置，而不是多变量分析， 利用分类单元的联合空间分布。我们提出统计创新，以应对由此提出的挑战，但 不是独特的3D生物膜图像。比较跨样本组的生物膜，以实验或基于 暴露历史需要在受试者缺乏真正的空间对应的图像中整合数据。 此外，3D空间分析尚未应用于具有数百万个观察结果的多元数据。 因此，该提案的目的是建立一个包含不同的贝叶斯多元3D LGCP 图像 - 允许非空间协变量因素 - 通过在每个方面应用单独的坐标系 图像。该提案有三个部分：（a）新型多元3D空间分析方法的发展 （目的1-3），（b）评估有关人舌微生物组空间结构的假设（AIM 4）， （c）基于最佳实践的软件开发和传播（AIM 5）。跨学科团队 具有深厚的技能和经验，开发贝叶斯高维多元分析方法。 提出的核心创新是将非空间协变量与3D的多元空间数据相结合 缺少公共坐标系的图像。样本可访问性和先前的生物学知识使得 口腔是开发灵活建模框架的最佳起点，该框架将允许测试假设 考虑微生物相互作用和与宿主特征的关联。这是对如何的基本转变 将分析此类图像，并有可能对口腔微生物的作用提供新的见解。在前进 研究图像跨图像，数学适应和 我们开发的软件将有可能产生突破性技术。