New data science approaches to visualize and understand the impact of the microbiome on risk of graft-versus-host disease

项目摘要/摘要 异基因干细胞移植是一种治疗多种血液疾病的救命方法，但其使用受到高度限制 严重副反应发生率，包括移植物抗宿主病(ff)的发展。肠道微生物群，或称 消化道中微生物的组成，在触发fl炎症反应中起着关键作用， 因此迫切需要对患者的微生物群进行fi分析，以预测和减轻移植物抗宿主病的风险。然而， 由于高维性，微生物组数据提出了许多现有方法无法解决的统计挑战， 不同学科之间的异质性，以及复杂的系统发育关系。在这个提案中，我们发展了新的数据科学 理解微生物组数据的方法，提供可指导未来干预措施发展的见解 旨在降低移植物抗宿主病的发病率。我们将开发准确和高效的微生物组数据分析方法和 让它们以用户友好的格式提供。我们专注于开发新的可视化方法和 使用微生物组数据进行预测，详见以下规范fic的目标： SPECIfic目标1：开发和评估用于微生物组数据可视化的先进工具。高潮 微生物组数据的维度和独特结构对ff数据可视化提出了挑战。在这个目标中， 我们将开发微生物组数据的无监督可视化和有监督可视化的方法，以及RShiny APP和QIIME2插件，将使临床医生和生物信息学家都可以访问这些工具。方法和方法 由这一目标产生的软件将提供强大的方法，使研究人员能够更好地可视化全球微生物组 他们研究人群的异质性，增强了数据探索和识别潜在混杂因素的fi 或异常值。 SPECIfic目标2：开发二元和生存结果的预测性建模方法。为了实现这一目标，我们 将侧重于在回归的背景下选择预测微生物组特征。我们将重点推进 使稀疏建模的良好应用能够预测移植物抗宿主病风险：处理二进制的新的统计方法 和事件发生时间结果，包括那些具有竞争风险的结果，以及计算上有效的ffi实施，将是 作为R包和RShiny应用程序免费提供。 SPECIfic目标3：开发理解稀有特性影响的方法。目前的微生物组前fi林 方法允许对每个样品中存在的菌株进行非常fiNe的分辨率。在这一目标中，我们提出了两种方法 了解罕见功能的影响。我们将首先开发一种方法，通过以下方式提供对内核关联结果的洞察 获得单个微生物组特征的估计eff等大小。然后，我们将开发一种非参数方法 回归COEffi特征的聚类，允许fl对观察到的罕见特征进行灵活的聚合。 这项工作的成功完成将产生新的统计和计算方法，以提供对 微生物组数据，生成可以指导未来战略发展的假设，以预测和缓解 GVHD。这些方法将通过易于使用和基于ffi的云软件实施来传播。

Project Summary/Abstract Allogeneic stem cell transplantation is a life-saving therapy for a variety of blood disorders, but its use is limited by a high rate of serious side effects, including the development of graft-versus-host-disease (GVHD). The gut microbiome, or the composition of microorganisms populating the digestive tract, plays a key role in triggering this inflammatory response, and there is an urgent need to analyze patient microbiome profiles to both predict and mitigate risk of GVHD. However, microbiome data pose a number of statistical challenges not addressed by existing methods due to high dimensionality, heterogeneity across subjects, and complex phylogenetic relationships. In this proposal, we develop new data science approaches to make sense of microbiome data, providing insight that can guide the development of future interventions aimed at reducing GVHD incidence. We will develop accurate and efficient methods for microbiome data analysis and make them available in user-friendly formats. We focus on the development of novel methods for visualization and prediction using microbiome data, as detailed in the following specific aims: Specific Aim 1: To develop and evaluate advanced tools for visualization of microbiome data. The high dimensionality and unique structure of microbiome data present challenges to effective data visualization. In this aim, we will develop approaches for both unsupervised and supervised visualization of microbiome data, along with an RShiny app and QIIME2 plug-in that will make these tools accessible to both clinicians and bioinformaticians. The methods and software resulting from this aim will provide robust approaches to enable researchers to better visualize global microbiome heterogeneity across their study population, enhancing data exploration and identification of potential confounding factors or outliers. Specific Aim 2: To develop predictive modeling approaches for binary and survival outcomes. In this aim, we will focus on selection of predictive microbiome features in the context of regression. We will carry out key advances enabling the effective application of sparse modeling to predict GVHD risk: novel statistical approaches to handle binary and time-to-event outcomes, including those with competing risks, and computationally efficient implementations, to be made freely available as both an R package and RShiny application. Specific Aim 3: To develop methods for understanding the impact of rare features. Current microbiome profiling methods allow for very fine resolution of the strains present in each sample. In this aim, we propose two methods to understand the impact of rare features. We will first develop a method to provide insight into kernel association results, by obtaining estimated effect sizes for individual microbiome features. We will then develop an approach for nonparametric clustering of the regression coefficients, which allows flexible aggregation of the observed rare features. Successful completion of this work will result in new statistical and computational approaches to provide insights into microbiome data, generating hypotheses that can guide the development of future strategies to predict and mitigate GVHD. These methods will be disseminated through easy-to-use and efficient cloud-based software implementations.