Data Management and Analysis Core

摘要：数据管理和分析核心(DMAC) 在我们建议的ACE中，我们提出了一个多模式、跨物种的计划，其中包括四个协同 旨在确定睡眠碎片和睡眠生理学对核心症状的作用的项目， 自闭症谱系障碍(ASD)的重复行为与认知功能利用比较优势 ASD的人类生物学和ASD的互补遗传动物模型，我们的总体目标是 ACEs将检查跨物种的多系统睡眠测量是否在一个共同的表型上收敛 睡眠碎片和结构在ASD中的差异；b)与核心、行为和 ASD的认知症状；以及c)如果ASD患者和ASD动物模型的睡眠正常， 缓解ASD的核心、行为和认知症状。管理和分析如此庞大的、 多领域的比较数据集包括仔细维护和跟踪质量和最终 异质性的来源，正确的数据校准和质量控制，以及对数据多样性的控制 正常化和指标方面的选择。DMAC将采用可重复使用的方法进行开发 完成统计分析和记录，以实现以下目标。 目标1：协调、传输、整合和管理所有ACE项目及其 不同平台；目标2：来自不同平台的数据集的整合、校准和标准化 来源；目标3：设计、分析和功率评估；以及目标4：整合和分析 RAC和项目1-4的比较数据。

ABSTRACT: DATA MANAGEMENT AND ANALYTIC CORE (DMAC) In our proposed ACE, we propose a multi-modal, cross-species program which encompasses four synergistic projects aimed at characterizing the role of sleep fragmentation and sleep physiology on the core symptoms, repetitive behaviors and cognitive function in Autism Spectrum Disorder (ASD). Leveraging comparative biology across humans with ASD and complementary genetic animal models of ASD, the overall aims of our ACE are to examine if multisystem sleep measurements across species a) converge on a common phenotype of sleep fragmentation and architecture in ASD; b) are differentially associated with the core, behavioral and cognitive symptoms of ASD; and c) if sleep normalization in humans with ASD and animal models of ASD, alleviate the core, behavioral and cognitive symptoms of ASD. The management and analysis of such large, multi-domain, comparative data sets include careful maintenance and tracking of the quality and eventual sources of heterogeneity, correct calibration and quality control of the data and control for the multiplicity of choices in the normalization and metrics. The DMAC will employ a reproducible approach to the development of the complete statistical analyses and record to attain the following objectives. Objective 1: Coordination, transfer, integration and management of data across all ACE Projects, and their different platforms; Objective 2: Integration, calibration and normalization of data sets from the different sources; Objective 3: Design, Analysis, and Power Estimation; and Objective 4: Integrating and analyzing comparative data across the RAC and Projects 1-4.