A Genome Data Analysis Center Focused on Batch Effect Analysis and Data Integration

* * 项目概要 * 翻译后摘要：技术批效应构成了一个根本的挑战，质量控制和再现性，甚至 单实验室的研究项目，但严重错误的可能性大大放大了复杂，多， 机构企业，如NCI中心正在进行的癌症分子谱分析项目， 癌症基因组学（CCG）。为了帮助检测、定量、解释和（适当时）校正 技术批量效应在这样的数据，我们已经开发了MBatch软件系统。MBatch证明 对于癌症基因组图谱（TCGA）和正在进行的CCG中的数据质量控制“监测”不可或缺 项目但是检测和量化批次效应（或趋势效应或统计离群值）只是第一步 在一个过程中。接下来的步骤包括与那些生成数据的人合作进行侦查工作， 基于跨数据类型，途径和系统级生物学的综合分析专业知识。那个警探 在诊断批量效应的原因时，通常能成功地将其诊断为技术原因或生物原因。如果是技术性的， 可以（明智地）应用计算方法来改善分批效应。 拟议中的基因组数据分析中心（GDAC）的主要目标是继续翻译， 成功的质量控制模型CCG的其他当前和未来的大规模分子分析项目 我们将在第一天做好准备。我们将继续加强和扩大MBatch的力量， 将许多创新的新算法、工具和交互式可视化结合到其中（OmicPioneer-sc， MutBatch、CarDEC和CorNet）。评估和校正批次效应是一个复杂的过程，因此我们将 与其他GDAC和数据生成中心合作，确定人工因素对任何分析的影响 他们产生的结果。第二个目标是促进和加强其他能力。我们准备 ㈠提供综合集群解决方案，将病例分为生物相关组; ㈡提供工具， 和专业知识的高层次可视化组学数据（包括单细胞数据）;和（iii）分析RPPA 蛋白质组学数据来自产生此类数据的项目子集。我们的最终目标是传达结果， 将更正后的数据分发给其他网络成员、项目利益相关者和科学界。 我们带来了许多资产，包括生物信息学，生物统计学， 软件工程、癌症生物学和癌症医学;在以下领域拥有40多年经验的PI： 癌症的分子特征分析;在10年的TCGA批次效应监测中获得的专业知识， 其他CCG项目;一个高度专业的软件工程团队，具有生产高端 生物信息学工具;广泛的计算资源，包括世界上最强大的学术集群之一。 世界;并与一流的基础，翻译和临床研究人员在医学博士密切的工作关系 安德森是美国最重要的癌症中心之一。GDAC的底线使命是帮助 研究界的努力，以了解癌症和预防，检测，诊断和治疗更有效。

* * * * PROJECT SUMMARY * * * * Abstract: Technical batch effects pose a fundamental challenge to quality control and reproducibility of even single-laboratory research projects, but the possibilities for serious error are greatly magnified in complex, multi- institutional enterprises such as the cancer molecular profiling projects being undertaken by the NCI Center for Cancer Genomics (CCG). To aid in detection, quantitation, interpretation, and (when appropriate) correction for technical batch effects in such data, we have developed the MBatch software system. MBatch proved indispensable for quality-control “surveillance” of data in The Cancer Genome Atlas (TCGA) and ongoing CCG projects. But detecting and quantitating batch effects (or trend effects or statistical outliers) are just the first steps in a process. The next steps involve detective work in collaboration with those who generated the data, drawing upon expertise in integrative analysis across data types, pathways, and systems-level biology. That detective work usually succeeds in diagnosing the cause of a batch effect as technical or biological. If technical, then computational methods to ameliorate the batch effect can be applied (judiciously). The primary aim of the proposed Genome Data Analysis Center (GDAC) is to continue to translate that successful quality-control model to the CCG’s other current and future large-scale molecular profiling projects We will be ready to do that on Day 1. We will continue to enhance and extend the power of MBatch and incorporate a number of innovative new algorithms, tools, and interactive visualizations into it (OmicPioneer-sc, MutBatch, CarDEC, and CorNet). Evaluating and correcting batch effects is a complex process, so we will collaborate with other GDACs and data generating centers to determine the influence of artifacts on any analysis results they produce. The second aim is to contribute and enhance additional competencies. We are prepared to (i) provide integrated cluster solutions to segregate cases into biologically relevant groups; (ii) provide tools and expertise for high-level visualization of omic data (including single-cell data); and (iii) analyze RPPA proteomic data from the subset of projects that generate such data. Our final aim is to communicate results and distribute corrected data back to other network members, project stakeholders, and the scientific community. We bring a number of assets to the table, including multidisciplinary expertise in bioinformatics, biostatistics, software engineering, cancer biology and cancer medicine; PIs with a combined 40+ years of experience in molecular profiling of cancers; expertise gained in 10 years of doing the batch effects surveillance for TCGA and other CCG projects; a highly professional software engineering team with a track record of producing high-end bioinformatics tools; extensive computing resources, including one of the most powerful academic clusters in the world; and close working relationships with first-class basic, translational, and clinical researchers across MD Anderson, one of the foremost cancer centers in the U.S. The bottom-line mission of the GDAC will be to aid the research community’s effort to understand cancer and to prevent, detect, diagnose, and treat it more effectively.