Core B: Integrative Data-Science Core

核心 B：综合数据科学核心

• 批准号: 10271125
• 负责人: KATHERINE S. POLLARD
• 金额: $ 73.2万
• 依托单位: J. DAVID GLADSTONE INSTITUTES
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-15 至 2026-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10271125
• 关键词: Address; Affect; Alzheimer' s Disease; Alzheimer' s disease model; Amyloid beta-Protein; Apolipoprotein E; Atlases; Attention; Autopsy; Behavioral; Behavioral Assay; Bioinformatics; Biological Assay; Biology; Brain; Cell Nucleus; Cells; Cellular Assay; Code; Communities; Complex; Coupled; Data; Data Analyses; Data Collection; Data Display; Data Science Core; Data Set; Disease; Disease model; Electrophysiology (science); Etiology; Experimental Designs; Gene Expression; Gene Expression Regulation; Generations; Genes; Genetic; Genetic Predisposition to Disease; Genomics; Genotype; Goals; Human; Impaired cognition; Inherited; Intuition; Lead; Libraries; Link; Machine Learning; Measures; Modeling; Molecular; Mus; Neurons; Pathologic; Pathology; Pattern; Phenotype; Problem Solving; Procedures; Process; Program Research Project Grants; Protocols documentation; Public Domains; Regulator Genes; Research; Research Design; Resources; Sampling; Small Nuclear RNA; Source Code; Standardization; Statistical Models; Synapses; System; Systems Development; Techniques; Testing; Time; Tissue Donors; Tissues; Variant; Visualization; brain tissue; cell type; cohort; computerized tools; data exploration; data integration; data portal; deep learning; design; diverse data; epigenome; experimental study; genetic variant; genomic data; high dimensionality; humanized mouse; improved; innovation; large scale data; learning network; machine learning method; mouse model; multimodality; network dysfunction; network models; neural network; novel; open source; phenotypic data; power analysis; predictive modeling; programs; response; sequencing platform; statistics; tau Proteins; tool; transcriptome; transcriptomics; user-friendly; web portal; web site

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.

核心B--摘要 该计划旨在发现阿尔茨海默病患者网络功能障碍的分子驱动因素和后果 疾病(AD)通过严格表征细胞类型的特定基因调控和多模式表型。 我们将使用人体样本和各种老鼠模型。这种数据的广度和深度跨越不同的 生物和细胞环境为综合建模提供了一个独特的机会。利用这一点 然而，机会，数据必须在数量上可以跨项目进行比较。为了应对这一挑战， 综合数据-科学核心(核心B)将使用“为推理而设计”的方法，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。要最小化 并纠正批次效应，规范实验方案，建立重复测量 实验设计，这将提高分析的能力。第二个挑战是如何总结和 用单细胞和单核转录图谱联合模拟复杂的高维表型。 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 学习和稀疏典型相关分析从多变量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对数据的探索 项目，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。这些协作工具还将帮助我们共享 我们的数据、代码和结果通过我们的Synapse网站与AD研究社区快速联系。总而言之， 核心B的活动将为所有四个项目提供尖端计算支持，使跨项目成为可能 发现，并为使用大规模数据集成来破译分子机制设定了新的标准 在AD和其他疾病中。