dCortools: Distance Correlation Methods for Detecting Nonlinear Associations in High-Dimensional Molecular Data

dCortools：用于检测高维分子数据中非线性关联的距离相关方法

• 批准号: 417754611
• 负责人: Dr. Dominic Edelmann
• 金额: --
• 依托单位: Abteilung Biostatistik
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2019
• 资助国家: 德国
• 起止时间: 2018-12-31 至 2021-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/417754611?language=en
• 关键词: dCortools; Distance; Correlation; Methods; Detecting

Virtually all methods that are currently used for testing associations in high-dimensional molecular data can only detect linear or monotone associations. This concerns both tests for the association between different molecular variables (e.g. gene-gene-interactions) and tests for the association between molecular and clinical variables (e.g. gene-environment-interactions).However, it is known that many biological relations are more complex, including nonmonotone or even nonfunctional dependencies. Distance correlation is a novel dependence measure that can detect every kind of dependence between random vectors of arbitrary dimensions. Moreover, the distance correlation coefficient is very easy to compute, which predestines it for the application in statistical practice. In spite of these convincing properties, there are hitherto only few applications of the distance correlation coefficient on high-dimensional molecular data. This is due to missing methodology for biostatistical problems on the one hand and to a lack of application-oriented software on the other hand. The goal of this project is to close this gap. In the first part of the project, we plan to develop distance correlation methodology for biomedical applications. First, we aim to derive iterative variable selection procedures that are much more efficient than univariate procedures under the assumption of strong correlation structures, which are typically present in molecular data. Moreover, we propose to extend the distance correlation coefficient to survival data, which are particularly important in cancer research.For the second part of the project we plan to create a user friendly R package that combines distance correlation methods that are useful for biostatistics and hence allows the application of this methodology for the practitioner. The techniques developed in the first part of the project will be important components of this R package. Finally, we propose to apply the R package on a data set from the DACHS study, consisting of epigenome-wide methylation data, epidemiological and clinical data for more than 2000 patients with colorectal cancer.We are confident that the planned project will lead to a considerable increase of the use of distance correlation methodology in biostatistical practice. For molecular data, this will allow to detect complex associations that would be missed if linear procedures were used. This in turn may lead to a better understanding of biological processes.

几乎所有的方法，目前用于测试关联在高维分子数据只能检测线性或单调的关联。这既涉及不同分子变量之间的关联性检验（例如基因-基因-相互作用），也涉及分子和临床变量之间的关联性检验（例如基因-环境-相互作用）。然而，众所周知，许多生物学关系更为复杂，包括非单调甚至非功能依赖性。距离相关性是一种新的相关性度量，它可以检测任意维数的随机向量之间的各种相关性。此外，距离相关系数计算简单，这为它在统计实践中的应用奠定了基础。尽管有这些令人信服的性质，迄今为止只有少数应用程序的距离相关系数的高维分子数据。这一方面是由于缺乏生物统计问题的方法，另一方面是由于缺乏面向应用的软件。该项目的目标是缩小这一差距。在项目的第一部分中，我们计划开发生物医学应用的距离相关方法。首先，我们的目标是获得迭代变量选择程序，这是更有效的比单变量程序的假设下，强相关结构，这是通常存在于分子数据。此外，我们建议将距离相关系数扩展到生存数据，这在癌症研究中特别重要。对于项目的第二部分，我们计划创建一个用户友好的R包，它结合了对生物统计学有用的距离相关方法，从而允许实践者应用这种方法。在项目的第一部分开发的技术将是这个R包的重要组成部分。最后，我们建议将R软件包应用于DACHS研究的数据集，包括2000多例结直肠癌患者的表观基因组甲基化数据、流行病学和临床数据，我们相信，计划中的项目将导致距离相关方法在生物统计实践中的使用大大增加。对于分子数据，这将允许检测如果使用线性程序将被遗漏的复杂关联。这反过来可能会导致更好地了解生物过程。