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: 10461727
• 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/10461727
• 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.

项目概要 生物医学研究和基础科学越来越依赖于高通量技术 能够同时测量样品中数千个核酸分子。与巧妙的结合 实验室协议，这些技术提供了前所未有的方法来研究分子基础 疾病和表型变异。由于这些技术的越来越多的采用，更多的调查 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研究 DNA 序列的最初任务 本身还包括定量和动态结果的测量，例如基因表达水平和 DNA 甲基化 (DNAm) 状态。这些定量和动态的结果引入了可变性水平， 引起进一步的数据分析挑战，这些挑战与区分不需要的变异来源和生物变异有关。 逻辑上相关的信号。此外，在衡量这些定量结果时，数据会受到严重影响 可能严重影响下游分析的技术和生物学偏差。我们组此前曾 证明统计方法可以比临时算法提供很大的改进 技术开发人员的错误。我们被广泛引用的统计方法和广泛使用的软件证明 我们工作的成功。 国家研究委员会的《海量数据分析前沿》出版物指出，“挑战 对于海量数据，超越了传统数据库的存储、索引和查询范围 系统，而是取决于推理的雄心勃勃的目标”。推理在生物医学中尤其重要 应用程序，因为我们经常希望根据存在的组之间观察到的差异得出结论 组内变异性。两项特别具有挑战性的任务与执行有效推理有关：1）我们 对大空间进行扫描以识别感兴趣的小区域，并且 2) 数据受到意外情况的影响 系统偏差或批次效应。我们将重点关注这两个普遍挑战。我们的具体建议是致力于 随着越来越多地依赖高通量技术，研究人员面临新挑战的最迫切需求。 我们将利用合作者的专业知识来确定项目的优先顺序。我们非常欣赏这种灵活性 R35 机制允许，因为它将帮助我们最大限度地发挥我们工作的影响。