Core B: Integrative Data-Science Core

核心 B – 摘要 该项目旨在发现阿尔茨海默氏症网络功能障碍的分子驱动因素和后果 通过严格表征细胞类型特异性基因调控和多模式表型来识别疾病（AD）。 我们将使用人类样本和各种小鼠模型。不同领域数据的广度和深度 有机体和细胞环境为整合建模提供了独特的机会。为了利用这一点 然而，机会不同项目之间的数据必须具有定量可比性。为了应对这一挑战， 综合数据科学核心（核心 B）将使用“推理设计”方法，这意味着 我们计划进行的预测建模和假设检验将指导实验设计的所有阶段。尽量减少 并纠正批次效应，我们将标准化实验方案并建立重复测量 实验设计，这将提高分析能力。第二个挑战是如何总结和 利用单细胞和单核转录组图谱联合建模复杂的高维表型。 为了解决这个问题，我们将开发创新的机器学习和网络模型，重点是深度学习 学习和稀疏典型相关分析从多元数据中提取信息并发现 数据类型对之间的关系。促进结果的实时共享和数据探索 项目中，我们将实施数据跟踪系统、带有管道和分析代码的 Jupyter 笔记本，以及 具有可视化和查询功能的交互式数据门户。这些协作工具也将帮助我们分享 我们的数据、代码和结果通过我们的 Synapse 网站与 AD 研究社区快速分享。总的来说， Core B 的活动将为所有四个项目提供尖端计算支持，实现跨项目 发现，并为使用大规模数据集成破译分子机制设定新标准 AD 和其他疾病。

CORE B – ABSTRACT This program aims to discover the molecular drivers and consequences of network dysfunction in Alzheimer’s disease (AD) through rigorous characterization of cell-type specific gene regulation and multi-modal phenotypes. We will use human samples and a variety of mouse models. This breadth and depth of data across different organismal and cellular contexts present a unique opportunity for integrative modeling. To capitalize on this opportunity, however, the data must be quantitatively comparable across projects. To address this challenge, the Integrative Data-Science Core (Core B) will use the “design for inference” approach, which means that the predictive modeling and hypothesis testing we plan to do will guide all stages of experimental design. To minimize and correct batch effects, we will standardize experimental protocols and establish a repeated-measures experimental design, which will boost the power for analyses. A second challenge is how to summarize and jointly model complex, high-dimensional phenotypes with single-cell and single-nucleus transcriptomic profiles. To solve this problem, we will develop innovative machine-learning and network models, with a focus on deep learning and sparse canonical correlation analysis to extract information from multivariate data and discover relationships between pairs of data types. To facilitate real-time sharing of results and exploration of data across projects, we will implement data tracking systems, Jupyter notebooks with pipelines and analytical code, and an interactive data portal with visualization and query capabilities. These collaborative tools will also help us share our data, code, and results rapidly with the AD research community through our Synapse website. Collectively, the activities of Core B will provide cutting-edge computational support to all four projects, enable cross-project discovery, and set new standards for the use of large-scale data integration to decipher molecular mechanisms in AD and other diseases.