Equivalent Partial Correlation Methods for Integrative Genetic Network Analysis

综合遗传网络分析的等效偏相关方法

• 批准号: 9273537
• 负责人: FAMING LIANG
• 金额: $ 3.46万
• 依托单位: UNIVERSITY OF FLORIDA
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2017-09-05
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9273537
• 关键词: Age; Algorithms; Cancer Genetics Network; Cell physiology; Clinical; Computing Methodologies; Copy Number Polymorphism; Data; Disease; Gaussian model; Gender; Genes; Genetic; Learning; Light; Malignant neoplasm of lung; Measures; Methodology; Methods; Methylation; MicroRNAs; Modeling; Molecular; Molecular Profiling; Pathway Analysis; Patient Care; Regulator Genes; Research Personnel; Scientist; Series; Statistical Methods; Variant; base; cancer type; computer framework; data integration; flexibility; high dimensionality; high throughput technology; improved; innovation; knowledge integration; mRNA Expression; novel; protein expression; public health relevance; therapeutic target

 DESCRIPTION (provided by applicant): The emergence of high-throughput technologies has made it feasible to measure the activities of thousands of genes simultaneously, which provides scientists a major opportunity to infer global gene regulatory networks (GRNs). Accurate inference of GRNs is very important, which allows people to gain a systematic understanding of the molecular mechanism, to shed light on the mechanism of diseases that occur when cellular processed are dysregulated, and furthermore to identify potential therapeutic targets for diseases. Given the high dimensionality and high complexity of high-throughput data, inference of global GRNs largely relies on the advance of computational methods. However, the current computational methods for inference of global GRNs are either inaccurate or computationally infeasible. How to infer global GRNs has put a great challenge on the current statistical methodology. The investigators propose an equivalent measure of partial correlation coefficients, and based on which develop an innovative computational framework for inference of global GRNs. The new measure of partial correlation coefficients can be evaluated with a reduced conditional set and thus feasible for high dimensional problems. Under the new framework, the investigators develop a series of algorithms which are able to provide a comprehensive inference for global GRNs by integrating various types of high-throughput molecular profiling data, e.g., mRNA expression, copy number variation, methylation, microRNA, and protein expression, and adjusting with various clinical covariates, e.g., age, gender, and disease stage. The proposed algorithms are applied to infer the global GRNs for various types of cancer with a special focus on lung cancer, while they are applicable to all other types of diseases. Statistically, this project proposes an innovative framework for inference of global GRNs, and the algorithms developed under which are not only computationally efficient, but also very flexible in data integration, covariate adjustment, prior knowledge integration, and network comparison. Biomedically, this is the first study to integrate such comprehensive and complementary information using rigorous statistical methods to study the global GRNs for various types of cancer.

 描述（由适用提供）：高通量技术的出现使得可以简单地衡量数千个基因的活动，这为科学家提供了推断全球基因调节网络（GRNS）的主要机会。 GRN的准确推断非常重要，它使人们能够对分子机制进行系统的了解，以阐明当细胞处理失调时发生的疾病机制，并识别疾病的潜在治疗靶标。鉴于高通量数据的高维和高复杂性，全局GRN的推断在很大程度上取决于计算方法的发展。但是，当前用于全局GRN的计算方法是不准确或计算上不可行的。如何推断全球GRNS在当前的统计方法上构成了巨大挑战。研究人员提出了对部分相关系数的同等测量，并基于该系数开发了用于推断全球GRN的创新计算框架。可以通过降低的条件集来评估部分相关系数的新测量，因此对于高维问题可行。在新框架下，研究人员开发了一系列算法，能够通过整合各种类型的高通量分子分析数据，例如mRNA表达，拷贝数变化，甲基化，微N和蛋白质表达和蛋白质表达，并与各种临床协方差（例如，年龄，gender，gender，gender，gender和diess）进行调整。提出的算法用于推断各种类型的癌症的全球GRN，特别关注肺癌，同时适用于所有其他类型的疾病。从统计上讲，该项目提出了一个创新的框架，用于推断全球GRN，而开发的算法不仅在计算上是有效的，而且在数据集成，协方差调整，先验知识集成和网络比较方面也非常灵活。从生物医学上讲，这是第一个使用严格的统计方法整合如此全面和完整信息的研究，以研究各种类型癌症的全球GRN。