Data Analysis Tools for Emerging High-Throughput Technologies

适用于新兴高通量技术的数据分析工具

• 批准号: 10159937
• 负责人: Rafael Angel Irizarry
• 金额: $ 59.68万
• 依托单位: DANA-FARBER CANCER INST
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-05-01 至 2024-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10159937
• 关键词: Adoption; Affect; Algorithms; Basic Science; Biological; Biomedical Research; Complex; Computer software; DNA Methylation; DNA Sequence; Data; Data Analyses; Data Analytics; Data Set; Database Management Systems; Development; FarGo; Gene Expression; Goals; Investigation; Laboratories; Measurement; Measures; Methodology; Molecular; National Research Council; Nucleic Acids; Outcome; Protocols documentation; Province; Publications; Research Personnel; Sampling; Scanning; Signal Transduction; Source; Statistical Methods; Systematic Bias; Techniques; Technology; Translational Research; Variant; Work; base; clinical application; disease phenotype; flexibility; frontier; high throughput technology; indexing; interest; precision medicine; success; tool

Project Summary Biomedical research and the basic sciences are increasingly dependent on high-throughput technologies that have the ability to simultaneously measure thousands of nucleic acid molecules in a sample. In combination with ingenious laboratory protocols, these technologies have permitted unprecedented ways of studying the molecular basis of disease and phenotypic variation. As a result of the increasing adoption of these technologies, more investigations rely on complex datasets and require the development of new statistical techniques to adequately interpret data. Today, high-throughput technologies applications go far beyond their original task of studying DNA sequence itself and also include the measurement of quantitative and dynamic outcomes such as gene expression levels and DNA methylation (DNAm) status. These quantitative and dynamic outcomes introduce levels of variability that give rise to further data analytic challenges related to distinguishing unwanted sources of variability from bio- logically relevant signals. Furthermore, when measuring these quantitative outcomes, data are subject to severe technological and biological biases that can substantially impact downstream analyses. Our group has previously demonstrated that statistical methodology can provide great improvements over ad-hoc algorithms offered as de- faults by technology developers. Our highly cited statistical methodology and our widely used software demonstrate the success of our work. The National Research Council's Frontiers in Massive Data Analysis publication states that, “the challenges for massive data go beyond the storage, indexing, and querying that have been the province of classical database systems and instead hinge on the ambitious goal of inference”. Inference is particularly relevant in biomedical applications since we often look to draw conclusions based on observed differences between groups in the presence of within group variability. Two particularly challenging tasks relate to performing valid inference when 1) we perform scans over large spaces to identify small regions of interests and 2) the data is affected by unexpected systematic bias or batch effects. We will focus on these two general challenges. Our specific proposal is to work on the most urgent needs of researchers facing new challenges as they increasingly rely on high-throughput techniques. We will leverage the expertise of our collaborators to prioritize projects. We greatly appreciate the flexibility permitted by the R35 mechanism as it will help us maximize the impact of our work.

项目摘要 生物医学研究和基础科学越来越依赖于高通量技术， 同时测量样品中数千个核酸分子的能力。结合巧妙的 实验室协议，这些技术已经允许前所未有的方式研究分子基础， 疾病和表型变异。随着这些技术的日益普及， 依赖复杂的数据集，需要开发新的统计技术来充分解释数据。 如今，高通量技术的应用远远超出了其最初研究DNA序列的任务 它本身也包括定量和动态结果的测量，如基因表达水平， DNA甲基化（DNAm）状态。这些定量和动态的结果带来了不同程度的可变性， 引起了与区分不想要的变异性来源与生物多样性相关的进一步数据分析挑战。 逻辑相关信号。此外，在衡量这些量化结果时，数据受到严重的影响。 技术和生物偏见可能会对下游分析产生重大影响。我们的团队以前 证明了统计方法可以提供比ad-hoc算法更大的改进， 技术开发人员的失误。我们被高度引用的统计方法和我们广泛使用的软件表明， 我们工作的成功。 国家研究理事会的《大规模数据分析前沿》出版物指出，“挑战 对于海量数据来说，它已经超出了传统数据库的存储、索引和查询范围 系统，而是取决于雄心勃勃的推理目标”。推理在生物医学领域尤其重要 应用程序，因为我们经常期待得出结论的基础上观察到的差异群体之间的存在 组内变异性。两个特别具有挑战性的任务涉及执行有效的推理，当1）我们 在大空间上执行扫描以识别感兴趣的小区域，以及2）数据受到意外的干扰， 系统偏差或批次效应。我们将集中讨论这两个一般性挑战。我们的具体建议是， 研究人员面临新的挑战，因为他们越来越依赖于高通量技术的最迫切的需求。 我们将利用合作者的专业知识来确定项目的优先顺序。我们非常感谢 这是R35机制所允许的，因为它将帮助我们最大限度地发挥我们工作的影响。