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使命： 三个互锁功能：（目标1）确保有效的数据质量和完全的可计算性，（目标2）提供 创新的综合分析，以支持这个罗宾中心的科学目标，并（目标3），以确保 ROBIN网络间协作以及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。在这一目标下，我们将收集、协调、 并提供在ROBIN中心收集的数字数据，包括分子表征 试验，以及项目1和2。这些不同的数据集包括：1）临床数据（去识别的患者和 肿瘤特征、诊断性直肠癌样本的免疫评分等），影像学资料（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 分布到轮廓解剖结构），以及3）由两个科学项目产生的生物数据 与MCT相关的（例如，空间转录组学、微生物组学、循环血液中的免疫生物标志物， 等）。我们将整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数据库中的所有ROBIN数据和成像数据 Commons，必要时进行插补，提供完全可计算的主题数据配置文件。目标2.进行 创新，综合分析，以支持这个罗宾中心的科学目标。在这个目标下，我们将 无偏/非参数和机器学习综合（多数据类型）分析，以确定关键 免疫表型和它们的肿瘤/免疫分子特征， 和成像数据。为了识别生物学和成像/放射组学特征或亚型，我们将应用创新的 聚类使用网络最优质量传输方法。了解辐射对外周血管的影响 血液单核细胞（PBMC），我们将使用机器学习进行系统的多变量分析 接近。为了了解RT反应的亚型，我们将应用一种新的非线性机器学习方法， 综合表型作图工具（iPhenMap），基于稀疏贝叶斯因子分析建模， 整合了分子和功能多模态模式。目标3。为了支持跨部门的无缝数据共享， ROBIN网络协作和交叉培训。在这个目标下，我们将记录和展示我们的工具， FireCloud和Imaging 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 ROBIN网络间的合作，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.