Incorporating Gene Ontology and Pathway Knowledge into Over Representation Analys

描述(申请人提供)：高通量筛选(HTS)在旨在揭示生物学中特定生化过程相互作用的研究中变得越来越受欢迎。在典型的HTS实验中，如基因表达微阵列和全基因组RNAi筛选，分析的第一阶段通常是识别具有某些特征的基因(例如，差异表达的基因)。为了获得对潜在生物学的更多洞察，下一阶段是进行过度表征分析(ORA)，它调查与特定生物功能相关的基因集在已识别的基因组中是否在统计上被过度表征。ORA基于这样的假设，即参与同一生物过程的基因将得到协调表达。然而，传统的ORA通常基于超几何P值，单独分析单个基因集合，没有考虑具有相似生物学功能的基因集合之间的相互关系。在这个项目中，我们结合了ORA中的基因本体和途径知识。该方法可以借用相关基因集合中的信息来加强对过度表达信号的检测。它能够提供比传统ORA更可靠和更有意义的生物学见解。我们考虑了生物功能之间的两种典型依赖结构：层次化的基因本体结构和相互关联的路径结构。通过分层先验将每个依赖结构合并到贝叶斯ORA模型中。贝叶斯模型提供了一个灵活的框架，可以方便地扩展以实现各种目标，如适应差异基因表达的二进制指示符或连续测试统计，评估支持基因本体注释的不同类型证据的可靠性，以及纳入不同的依赖结构。此外，我们还提出了一种同时进行微阵列分析和ORA的集成方法。该方法不仅在ORA中利用了基因本体(或通路)结构的信息，而且在微阵列分析中也利用了这些信息，减少了微阵列分析中的随机性，进一步提高了ORA中的结果。 与公共健康相关：高通量筛选(HTS)，如基因表达微阵列和全基因组RNAi筛选，已成为生物和医学研究中日益不可或缺的工具。然而，在HTS数据分析中，对基因功能特征(如基因本体论和途径)的不断扩展的知识还没有得到充分的探索。该项目的目标是将基因本体论和通路知识结合到HTS数据分析中，为HTS结果的解释提供更可靠和有意义的生物学见解。

DESCRIPTION (provided by applicant): High-throughput screening (HTS) has become increasingly popular in studies that aim to reveal the interaction of particular biochemical processes in biology. In typical HTS experiments, such as gene expression microarray and genome-wide RNAi screening, the first stage in the analysis is often to identify genes with certain characteristics (e.g., genes that are differentially expressed). To gain more insights into the underlying biology, the next stage is to conduct over- representation analysis (ORA), which investigates whether gene sets associated with particular biological functions are statistically over-represented in the identified group of genes. ORA is based on the postulate that genes involved in the same biological process would be coordinately expressed. However, the traditional ORA, which is usually based on the hypergeometic P-value, analyzes individual gene sets separately and does not take into account the interrelationship among gene sets which share similar biological functions. In this project, we incorporate gene ontology and pathway knowledge in ORA. The proposed method can borrow information across related gene sets to strengthen the detection of over- representation signals. It is capable of providing more reliable and meaningful biological insights than the traditional ORA. We consider two typical dependence structures among biological functions: the hierarchical gene ontology structure and the interconnected pathway structure. Each dependence structure is incorporated in a Bayesian ORA model via a hierarchical prior. The Bayesian model provides a flexible framework which allows easy extensions to achieve various goals, such as accommodating either binary indictor or continuous test statistic on differential gene expression, evaluating the reliability of different types of evidence supporting gene ontology annotations, and incorporating a different dependence structure. In addition, we propose an integrated approach to conduct microarray analysis and ORA simultaneously. The information of the gene ontology (or pathway) structure is utilized not only in ORA but also in microarray analysis, which reduces the randomness in microarray analysis and further improves the results in ORA. PUBLIC HEALTH RELEVANCE: High-throughput screening (HTS), such as gene expression microarray and genome-wide RNAi screening, has become an increasingly indispensable tool in biological and medical research. However, the ever expanding knowledge of the functional characteristics of genes (such as gene ontology and pathways) has not been fully explored in HTS data analysis. The goal of this project is to incorporate gene ontology and pathway knowledge in HTS data analysis to provide more reliable and meaningful biological insights into the interpretation of HTS results.