Bayesian multivariate 3D spatial modeling for microbiome image analysis

细菌在人类健康中扮演着重要的有益和有害的角色。生活在生物膜社区，一个 物种可以攻击、保护或为邻近物种提供营养。这些相互作用决定了 社区的净影响。为了了解生物膜如何影响健康，需要澄清社区组织。 为了开始满足这一需求，我们开发了一种成像技术，组合标记和光谱 成像荧光原位杂交(Clasi-FISH)，它显示了类群细胞的相对位置 彼此之间以及宿主细胞之间的关系。然而，生物膜的复杂的三维(3D)结构却很难被 常用的衡量标准，如一个或两个分类群的细胞间距离或全球生物膜体积。 在这里，我们建议扩展对数高斯-考克斯过程模型(LGCP)来描述和检验假设 关于人体生物膜建筑，一种新的应用。计算负担限制了现有的LGCP模型 将地质统计数据转换为具有数千个观测值的数据集。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，我们已经成功地放大了六个分类群的多变量LGCP。估计的成对互相关 函数在单变量分析中不同，它忽略其他分类群的位置，而多变量分析中，它忽略其他分类群的位置 利用分类群的联合空间分布。我们提出了统计创新，以应对以下挑战： 不是3D生物膜图像所独有的。比较不同样本组之间的生物被膜 曝光史需要将缺乏真正的空间对应关系的受试者图像中的数据进行整合。 此外，3D空间分析还没有应用于拥有数百万观测值的多变量数据。 因此，该建议的目标是构建一个贝叶斯多变量3D LGCP，它结合了不同的 图像-从而允许非空间协变量因素-通过对每个图像应用单独的坐标系 图像。该建议包括三个部分：(A)新的多变量三维空间分析方法的发展 (目标1-3)，(B)评估关于人类舌头微生物组空间结构的假设(目标4)， (C)基于最佳做法的软件开发和传播(目标5)。跨学科团队 具有深厚的技能和开发贝叶斯高维多元分析方法的经验。 提出的核心创新是将非空间协变量与3D中的多变量空间数据进行集成 缺少公共坐标系的图像。样本的可及性和先前的生物学知识使 口腔是开发灵活的建模框架的最佳起点，该框架将允许对假设进行测试 关于微生物相互作用和与宿主特征的关联。这是一个根本性的转变，因为 这些图像将被分析，有可能为口腔微生物的作用提供新的见解。在前进中 跨图像研究多变量3D空间模式的能力、数学适应性和 我们开发的软件将有可能产生突破性的技术。

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.