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

***项目摘要*** 文摘：技术批次效应对EVEN的质量控制和重现性提出了根本的挑战 单一实验室的研究项目，但在复杂的，多个实验室的研究项目中，发生严重错误的可能性被极大地放大. 机构企业，如NCI中心正在进行的癌症分子图谱项目 癌症基因组学(CCG)协助检测、量化、解释和(在适当时)纠正 针对这类数据的技术批量效应，我们开发了MBatch软件系统。MBatch已被证明 对《癌症基因组图谱》(TCGA)和正在进行的CCG中的数据进行质量控制是必不可少的 项目。但检测和量化批次效应(或趋势效应或统计异常值)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检测工作，绘制 基于跨数据类型、路径和系统级生物学的综合分析方面的专业知识。那个侦探 这项工作通常能够成功地将批量效应的原因诊断为技术或生物原因。如果是技术性的，那么 可以(明智地)应用改善批次效应的计算方法。 拟议的基因组数据分析中心(GDAC)的主要目标是继续将这一点 CCG目前和未来其他大型分子图谱项目的成功质量控制模式 我们将在第一天做好准备。我们将继续增强和扩展MBatch和 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)。评估和纠正批处理效果是一个复杂的过程，因此我们将 与其他GDAC和数据生成中心协作，确定人工产物对任何分析的影响 他们产生的结果。第二个目标是贡献和增强更多的能力。我们已经准备好了 (一)提供综合分组解决方案，将病例分成具有生物相关性的群体；(二)提供工具 基因组数据(包括单细胞数据)的高级可视化方面的专门知识；以及(3)分析RPPA 来自产生这种数据的项目子集的蛋白质组数据。我们的最终目标是交流结果和 将更正后的数据发回给其他网络成员、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；PI具有40年以上的 癌症的分子图谱；在TCGA和TCGA进行批次效应监测的10年中获得的专业知识 CCG的其他项目；一个高度专业的软件工程团队，有生产高端产品的记录 生物信息学工具；广泛的计算资源，包括全球最强大的学术集群之一 世界；与MD的一流基础、翻译和临床研究人员建立密切的工作关系 安德森癌症中心，美国最重要的癌症中心之一。GDAC的底线任务是帮助 研究界为了解癌症以及更有效地预防、检测、诊断和治疗癌症所做的努力。

* * * * 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.