Data Processing, Analysis and Modeling Unit

项目摘要/摘要：数据分析股 华盛顿大学人类肿瘤图谱研究数据分析单元的总体目标 中心(WU-HTARC)是为深入研究提供生物信息学工具和处理/分析基础设施 对特定股产生的数据进行分析。最重要的是，我们将跨 方法学(组学/成像/表型分析)和维度(一维/二维/三维/时间)光谱 为三种癌症类型(GBM、PDAC和BRCA/TNBC)中的每一种提供连贯和可访问的肿瘤图谱。 在基本层面上，每个地图集将首先对单元的各种数字计算的指标进行编目 类型，包括分数(1D)、密度、分散度和单个细胞类型的位置测量以及 欧几里得测量不同细胞类型的空间分布，例如免疫细胞和肿瘤细胞(2D和 3D)，以及这些指标如何随时间变化。然后我们将把这些信息与两个基因组 分析，如突变特征、克隆性和显著突变的基因/区域/途径、蛋白质组学 和代谢组学分析，以及图像衍生数据。地图集的核心将是MySQL关系 数据库，不仅存储所有收集的数据，而且将它们沿着这些不同的维度链接起来。用户将 通过复杂的基于查看器/查询浏览器的Web门户与地图集进行交互，该门户将同时支持 传统的基于文本的查询，以及基于空间的查询(形状、特征位置等)。凝聚力 在三份地图集中，就所使用的数据范围和编制方法而言，将 允许用户生成现在不可能的新类型的假设，并执行泛癌症分析以 揭示三种具有代表性的实体肿瘤的共性和差异，并潜在地推断 这些发现也适用于其他肿瘤。

Project Summary/Abstract: Data Analysis Unit The over-arching goal of the Data Analysis Unit for the Washington University Human Tumor Atlas Research Center (WU-HTARC) is to provide bioinformatics tools and processing/analysis infrastructure for in-depth analyses of the data generated in the Characterization Unit. Most importantly, we will integrate data across both the methodological (omics/imaging/phenotypic analyses) and the dimensional (1D/2D/3D/time) spectrums into coherent and accessible tumor atlases for each of the three cancer types: GBM, PDAC, and BRCA/TNBC. At the basic level, each atlas will consist of first cataloging a variety of numerically-computed metrics for cell types, including fractions (1D), density, dispersion, and location measures for individual cell types and Euclidean measures of spatial interspersedness of different cell types, e.g. immune and tumor cells (2D and 3D), and how these metrics change with time. We will then correlate this information with both genomic analyses, such as mutation signatures, clonality, and significantly mutated genes/regions/pathways, proteomic and metabolomics analyses, and image-derived data. The core of the atlas will be a MySQL relational database that not only stores all collected data, but links them along these different dimensions. Users will interface with the atlas through a sophisticated viewer/query browser-based web portal that will support both traditional text-based queries, as well as spatial-based queries (shape, feature locations, etc.). The cohesion among the three atlases, in terms of the spectrum of data used and the approaches of their construction, will allow users to generate new types of hypotheses not now possible and to perform pan-cancer analyses to reveal commonalities and differences in the three representative solid tumors, and to potentially extrapolate these findings to other tumors.