Data Sharing and Integrative Analysis Core

该数据共享和综合分析(DSIA)核心将通过以下方式支持中心的总体任务 三个联锁功能：(目标1)确保有效的数据质量和完全可计算性，(目标2)提供 创新的综合分析，以支持该罗宾中心的科学目标，并(目标3)确保 Robin间网络协作以及NCI癌症研究数据的无缝数据共享 生态系统。目标1.确保有效的数据质量和可计算性。在这个目标下，我们将收集，协调， 并使在罗宾中心收集的数字数据，包括分子特征，可供访问 试验以及项目1和2。这些不同的数据集包括：1)临床数据(未确认的患者和 肿瘤特征、诊断直肠癌标本的免疫积分等)、成像数据(MRI/CT 基线和手术前的图像)、2)放射治疗计划数据(DICOM图像、剂量 等高线解剖结构的分布)，以及3)由两个科学项目产生的生物学数据 与MCT相关(例如，空间转录组、微生物组、循环血液中的免疫生物标记物， 等)。我们将管理和传输来自分子表征试验(MCT)和科学项目1的数据 和2，并将完全链接NCI云资源FireCloud工作区内的所有Robin数据和映像数据 Commons，在必要时加以归罪，提供完全可计算的主题数据配置文件。目标2.进行 创新的综合分析，以支持这个罗宾中心的科学目标。在这一目标下，我们将适用 无偏/非参数和机器学习集成(多数据类型)分析，以确定关键 免疫表型及其肿瘤/免疫分子特征的全谱生物学研究 和成像数据。为了识别生物和成像/放射组学特征或亚型，我们将应用创新 聚类法采用网络最优质量运输方法。了解辐射对外周血的影响 血液单个核细胞(PBMC)，我们将使用机器学习进行系统的多变量分析 接近了。为了理解RT反应的亚型，我们将应用一种新的非线性机器学习 基于稀疏贝叶斯因子分析建模的综合表型映射工具(IPhenMap)， 整合了分子和功能多模式。目标3.支持企业间无缝数据共享 罗宾的网络协作和交叉训练。在这一目标下，我们将记录和演示我们的工具， FireCloud和Image Data Commons基础设施中的数据和可重新运行的分析工作流，以支持 罗宾之间的网络协作，以及跨学科的交叉培训。

This Data Sharing and Integrative Analysis (DSIA) Core will support the overall mission of the Center through three interlocking functions: (Aim 1) to ensure effective data quality and full computability, (Aim 2) to provide innovative integrative analyses to support the scientific goals of this ROBIN center, and (Aim 3) to ensure seamless data sharing for inter-ROBIN network collaborations as well as to the NCI Cancer Research Data Ecosystem. Aim 1. To ensure effective data quality and computability. Under this Aim, we will collect, harmonize, and make accessible the digital data collected in this ROBIN Center, including the Molecular Characterization Trial, as well as Projects 1 and 2. These diverse sets of data include: 1) clinical data (de-identified patient and tumor characteristics, Immunoscore of the diagnostic rectal cancer specimen, etc.), imaging data (MRI / CT images at baseline and prior to surgery), 2) radiotherapy treatment planning data (DICOM images, dose distributions to the contoured anatomic structures), and 3) biological data resulting from the two scientific projects associated with the MCT (e.g., spatial transcriptomics, microbiome, immune biomarkers in circulating blood, etc.). We will curate and transfer data from the Molecular Characterization Trial (MCT) and Scientific Projects 1 and 2, and will fully link all ROBIN data within NCI Cloud Resource FireCloud workspaces and the Imaging Data Commons, with imputation where necessary, providing fully computable subject data profiles. Aim 2. To conduct innovative, integrative analyses to support the scientific goals of this ROBIN center. Under this Aim, we will apply both unbiased/non-parametric and machine learning integrative (multi-datatype) analyses to identify critical immune phenotypes and their tumor/immune molecular signatures using the full spectrum of available biological and imaging data. To identify biological and imaging/radiomics signatures or subtypes, we will apply innovative clustering using network optimal mass transport methods. To understand the impact of radiation on Peripheral Blood Mononuclear Cells (PBMCs), we will conduct systematic multivariate analyses using machine learning approaches. To understand subtypes of RT response, we will apply a novel non-linear machine-learning integrative phenotypic mapping tool (iPhenMap), based on sparse Bayesian factor analysis modeling, that integrates molecular and functional multimodal patterns. Aim 3. To support seamless data sharing for inter- ROBIN network collaborations and cross-training. Under this Aim, we will document and demonstrate our tools, data, and rerunnable analysis workflows in FireCloud and Imaging Data Commons infrastructure, to support inter-ROBIN network collaborations, as well as inter-disciplinary cross-training.