Biologic Pathway Analysis

最近的生物医学研究在揭示人类疾病的复杂性方面取得了很大进展，而技术突破现在允许对分子行为进行更详细的分析。然而，结果是，在通过单个基因追踪疾病的传统模式中，实验结果往往过于复杂，无法合成。取而代之的是，分子途径作为分析研究的新框架正变得越来越突出。路径整合了整个基因组中的信息，同时反映了真实的生物过程。破坏整个通路的良性行为，而不一定是通路的单个组成部分，可能是疾病的基础。到目前为止，还没有可靠或直接的方法来将大多数基因研究中常见的大规模分子表达数据转化为途径水平上的有意义的数据。为了促进这种有希望的研究模式，病理学家已经发展成为一种能够对分子数据进行系统和有效的以途径为中心的分析的资源。病理学家是一种新的工具，旨在自动分析分子途径背景下的大量遗传数据。该工具旨在以实验室研究人员和信息学分析人员都可访问的形式促进对途径行为的定量和定性分析。最重要的是，病理学家使用核糖核酸表达数据来计算一组500多条典型通路中每条通路的2个描述性指标-活性和一致性(来源：通路相互作用数据库http://pid.nci.nih.gov).活动分数提供了路径内发生交互的可能性的度量，而一致性评分通过比较交互的预期结果和实际结果来提供路径逻辑的度量。可以为任意数量的样本以及整个路径集合的任何子集生成路径分数。然后，该程序允许通过综合可视化路径组件、结构和分数、路径和样本的分级聚类以及旨在确定路径分数与癌症类型或患者生存等临床特征之间的关联的统计分析来详细探索结果。病理学家提供了一种强大的手段来识别与疾病有关的常见分子过程。通过观察途径水平上的分子行为，病理学家产生的指标通常提供了比单个基于基因的分析更深入的疾病病理见解。该工具已经被用于预测对癌症治疗的反应和识别与癌症表型相关的分子特征等多种应用。

Recent biomedical research has made great progress in unveiling the complexity of human disease, while technological breakthroughs now allow much more detailed analysis of molecular behavior. As a result however, experimental results are frequently too complex for synthesis in the traditional model of tracing disease through individual genes. Instead, molecular pathways are gaining prominence as a new framework for analytic research. Pathways integrate information from across the entire genome while mirroring real biological processes. Disruption of the benign behavior of a pathway as a whole, not necessarily a single component of the pathway, could be the basis for disease. As yet, there exists no robust or straightforward means to transform the large-scale molecular expression data common to most genetic studies into meaningful data at the pathway level. To facilitate this promising mode of investigation, the PathOlogist has been developed as a resource capable of systematic and efficient pathway-centric analysis of molecular data. The PathOlogist is a new tool designed to automatically analyze large sets of genetic data within the context of molecular pathways. The tool aims to facilitate both a quantitative and qualitative analysis of pathway behavior in a format accessible to both laboratory researchers and informatics analysts. Foremost, the PathOlogist uses RNA expression data to calculate 2 descriptive metrics - activity and consistency - for each pathway in a set of more than 500 canonical pathways (source: Pathway Interaction Database http://pid.nci.nih.gov). Activity scores provide a measure of how likely the interactions within the pathway are to occur while consistency scores provide a measure of pathway logic by comparing the expected with de facto outcome of interactions. Pathway scores can be generated for any number of samples, and for any subset of the entire pathway collection. The program then allows a detailed exploration of the results through integrated visualization of pathway components, structure, and scores, hierarchical clustering of pathways and samples, and statistical analyses designed to identify associations between pathway scores and clinical features such as cancer type or patient survival. The PathOlogist provides a powerful means of identifying common molecular processes implicated in disease. By viewing molecular behavior at the pathway level, the metrics generated by the PathOlogist often provide further insight into disease pathology than could be gained from individual gene-based analyses. The tool is already being used for such diverse applications as predicting response to cancer treatment and identifying molecular signatures associated with cancer phenotype.