Project 003 - VICI

项目3.病毒、免疫学和细胞数据集成(VICI)研究项目 40年来，研究人员将艾滋病毒医学发展到了使艾滋病毒携带者(PWH)能够正常生活的地步 如果他们能够获得抗逆转录病毒治疗(ART)，他们就会有健康的生活。然而，艾滋病毒并不是很容易治愈的。 治愈艾滋病毒需要在多个尺度上研究生物系统的方法方面取得进一步进展，从 细胞内基因之间的相互作用对艾滋病毒在组织之间的迁移。需要创新的方法来 在ART正在和没有停止的环境中，更全面地描述艾滋病毒的动态。 迫切需要这些方法来解决在概念验证研究中出现的挑战 参与者数量。VICI研究项目(RP)建议开发和验证以下方法 分析其他RPS(Veni和Vidi)从20个特征良好的数据中生成的大型复杂数据集 参与者登记在创新的最后一份礼物队列中。因为可重现的科学结果同样依赖于 开发新的分析方法，以应对这些数据集对其 在这一代，我们建议投入相当多的资源和人才在拟议的维西资源计划上。 在整个VICI RP中，我们描述了模型的开发、统计验证和应用 具有临床和其他低维协变量的高维、单细胞和单基因组数据。 所提出的方法使用一种将复杂和不同实体之间的联系结合在一起的“系统”方法 (例如，基因表达、整合位置、表观遗传标记、组织类型)或阐明 预测者对整个数据进行整合。 目标1和2侧重于新的统计方法，以(1)将新的网络方法与离散特征相结合 分析(在Veni RP中描述)以推断病毒迁移网络及其预测因素，以及(2)识别细胞 通过一种新的递归形式识别的基于基因调控网络类别的表型 分区。这些方法将直接应用于分析艾滋病毒的激活和组织的再繁殖。 AIM 3使用中介分析⸺，包括新的中介效果异质性测试⸺来评估 驱动艾滋病毒在体内持续存在的机制。 这些互补的目标共享相同的总体目标，即提供基于系统的框架，该框架 便于分析大型、复杂和高维数据集。说明研究框架和指南 回顾，我们描述了提出的创新方法在研究定义的储集层状态上的应用 艾滋病病毒的离开、到来和呆在家里的时间和时间。

PROJECT 3. Viral, Immunologic and Cellular data Integration (VICI) Research Project - ABSTRACT For 40 years, research has advanced HIV medicine to the point where persons with HIV (PWH) can live normal and healthy lives if they have access to antiretroviral therapy (ART). Nevertheless, HIV cannot be readily cured. Curing HIV requires further advances in approaches to investigating biological systems at multiple scales, from interactions among genes within a cell to migration of HIV between tissues. Innovative methods are needed to characterize HIV dynamics more fully in settings where ART is and is not stopped. These methods are urgently needed to address the challenges that arise in proof-of-concept studies with a small number of participants. The VICI Research Project (RP) proposes development and validation of methods for analyzing large and complex datasets generated by the other RPs (VENI and VIDI) from 20 well-characterize participants enrolled in the innovative Last Gift cohort. As reproducible scientific results depend as much on development of novel analytical methods to address the challenges posed by these datasets as on their generation, we propose to devote considerable resources and talent to the proposed VICI RP. Throughout this VICI RP, we describe development, statistical validation, and application of models that integrate high-dimensional, single-cell and single-genome data with clinical and other low-dimensional covariates. Proposed methods use a ‘systems’ approach that incorporates connections among complex and distinct entities (e.g., gene expression, integration site, epigenetic marks, tissue types) or elucidates relationships among predictors to integrate the totality of the data. Aims 1 and 2 focus on novel statistical methods to (1) combine novel network methods with the discrete trait analyses (described in the VENI RP) to infer viral migration networks and its predictors, and (2) identify cell phenotypes based on classes of gene regulatory networks identified through a novel form of recursive partitioning. These methods will be directly applied to analyze HIV activation and repopulation of tissues. Aim 3 uses mediation analysis⸺including novel tests for heterogeneity in mediation effects⸺to assess mechanisms that drive HIV persistence in the body. These complementary aims share the same overarching goal of providing a system-based framework that facilitates analysis of large, complex, and high dimensional datasets. To illustrate the study framework and guide reviewers, we describe the application of the proposed innovative methods on the study-defined reservoir states of HIV leaving, coming, and staying HOME on and off ART.