Visual Analysis of Genomic and Clinical Data from Large Patient Cohorts

 描述（由申请人提供）：收集各种基因组、表观基因组和临床数据的综合性大型队列研究在生命科学中越来越常见。虽然大样本量仍然局限于资金充足的财团，但数据采集成本的持续下降将使各个实验室能够以更少的资源创建更大的数据集，并使用于患者诊断的基因组数据分析变得可行。虽然这为理解许多疾病的分子过程提供了前所未有的可能性，但也带来了挑战，特别是在数据分析和数据管理方面。人们对更好的分析和可视化方法有很高的需求，以跟上不断增长的数据量。同时，这些数据采集方法也将为罕见病的发现和诊断带来革命性的变化。将基因组学数据与大量患者记录和大型患者队列相结合，有望对罕见或未确诊疾病的患者进行诊断和潜在治疗。在这个项目中，我们将创造新的方法，并提供独特的软件工具，将满足这一重大需求。我们的方法与现有的可视化方法不同，现有的可视化方法通常专注于可视化特定的分子和临床数据类型，而忽略了患者队列的背景。我们提出的方法是区别于以往的工作，考虑到这些复杂的关系，患者之间的队列。此外，我们的方法是第一个整合所有尺度的基因组数据，同时支持交互式分析，创建和细化患者子集。我们将通过以下方式应对这一挑战：（1）开发可视化技术，与算法支持深度集成，以识别和表征疾病亚型。具体来说，我们将开发方法，使临床和实验研究人员去 除了分析简单的关系，创造了揭示许多疾病的不太明显和间接的分子原因的潜力。(2)我们将创建新的可视化，采用算法来选择和显示重要的基因组特征和患者的临床病史，以研究和诊断罕见疾病。(3)我们将创建一个框架来支持基于Web的可视化探索工具的开发，我们将使用该工具来创建亚型和罕见疾病分析的可视化。此外，我们还将为社区提供此框架，以用于其他工具。这将使未来的项目能够产生可视化的分析方法，以更少的工程开销来应对大数据的挑战。该项目将是哈佛医学院首席研究员Peter Park实验室的计算（epi）基因组学和癌症研究人员团队与哈佛工程与应用科学学院联合研究员Hanspeter Pfister实验室的数据可视化专家之间的密切合作。该团队拥有独特的专业知识组合，能够成功应对推动该应用程序的挑战。

 DESCRIPTION (provided by applicant): Comprehensive large cohort studies that collect a wide variety of genomic, epigenomic and clinical data are increasingly commonplace in the life sciences. While large sample sizes are still limited to well-funded consortia, the continuous cost decrease of data acquisition will allow individual labs to create larger datasets with fewer resources and will make genomic data analysis for the diagnosis of patients feasible. While this opens unprecedented possibilities for understanding the molecular processes underlying many diseases, it also poses challenges, especially with respect to data analysis and data management. There is a high demand for better analysis and visualization methods to keep pace with the increasing amount of data. At the same time, these data acquisition methods will also revolutionize the discovery and diagnosis of rare diseases. The integration of genomics data with extensive patient records and large patient cohorts promises diagnosis and potentially treatment to those with rare or undiagnosed diseases. In this project we will create novel methods and provide unique software tools that will meet this significant demand. Our methods are a departure from existing visualization approaches that are typically focused on visualizing particular molecular and clinical data types while neglecting the context of a patient cohort. Our proposed approach is distinguished from previous work by taking into account these complex relationships between patients in a cohort. In addition, our approach is the first to integrate genomic data at all scales while supporting the interactive analysis, creation and refinement of patient subsets. We will address this challenge by (1) developing visualization techniques, deeply integrated with algorithmic support, to identify and characterize disease subtypes. Specifically, we will develop methods that will allow clinical and experimental investigators to go beyond analyzing simple relationships, creating the potential to reveal the less obvious and indirect molecular causes of many diseases. (2) We will create novel visualizations that employ algorithms to select and display important genomic characteristics and the patient's clinical history to study and diagnose rare diseases. (3) We will create a framework to support the development of web-based visual exploration tools, which we will use to create the visualizations for subtype and rare disease analysis. Additionally, we will also make this framework available for the community to use for other tools. This will allow future projects to produce visual analysis methods that scale to the challenges of big data with less engineering overhead. This project will be a close collaboration between a team of computational (epi) genomics and cancer researchers in the laboratory of the Principal Investigator Peter Park at the Harvard Medical School and data visualization experts in the laboratory of the Co-Investigator Hanspeter Pfister at the Harvard School of Engineering and Applied Sciences. This team possesses the unique combination of expertise that is required to successfully address the challenges that motivate this application.