Biologic Pathway Analysis

近年来的生物医学研究在揭示人体复杂性方面取得了很大进展， 疾病，而技术突破现在允许更详细的分子分析， 行为然而，结果是，实验结果常常太复杂而不能综合 通过单个基因追踪疾病的传统模型。相反，分子 路径作为分析研究的一个新框架越来越突出。路径整合 从整个基因组中提取信息，同时反映真实的生物过程。 破坏整个通路的良性行为，而不一定是单个组件 可能是疾病的基础。到目前为止，还没有一个强大的或 直接的手段来转换大规模的分子表达数据， 遗传学研究转化为途径水平的有意义的数据。为了促进这种有前途的模式， 研究，病理学家已被开发为一个资源，能够系统和 分子数据的高效途径中心分析。病理学家是一个新的工具， 在分子通路的背景下自动分析大量的遗传数据。 该工具旨在促进对路径行为的定量和定性分析 以实验室研究人员和信息分析人员都可以使用的格式。首先是 PathOlogist使用RNA表达数据计算2个描述性指标-活性和 一致性-对于500多个经典途径中的每一个途径（来源：Pathway 交互数据库http://pid.nci.nih.gov）。活动分数提供了一个衡量 路径内的相互作用将发生，而一致性分数提供了一个衡量指标， 路径逻辑通过比较预期与实际的互动结果。Pathway评分 可以为任意数量的样本以及整个路径的任意子集生成 收藏.然后，该计划允许通过集成详细探索结果 可视化的途径组成部分，结构和分数，层次聚类的 途径和样本，以及旨在确定 途径评分和临床特征，例如癌症类型或患者存活率。病理学家 提供了一个强有力的手段，确定共同的分子过程中牵连的疾病。通过 在途径水平上观察分子行为，病理学家生成的指标 往往提供了更深入的了解疾病的病理比可以获得从个人 基于基因的分析该工具已被用于各种不同的应用程序， 预测对癌症治疗的反应，并识别与癌症相关的分子特征。 癌症表型

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.