Byron Adam
Cancer Research UK Edinburgh Centre, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XR, UK.
Methods Mol Biol. 2017;1606:171-191. doi: 10.1007/978-1-4939-6990-6_12.
Molecular profiling of proteins and phosphoproteins using a reverse phase protein array (RPPA) platform, with a panel of target-specific antibodies, enables the parallel, quantitative proteomic analysis of many biological samples in a microarray format. Hence, RPPA analysis can generate a high volume of multidimensional data that must be effectively interrogated and interpreted. A range of computational techniques for data mining can be applied to detect and explore data structure and to form functional predictions from large datasets. Here, two approaches for the computational analysis of RPPA data are detailed: the identification of similar patterns of protein expression by hierarchical cluster analysis and the modeling of protein interactions and signaling relationships by network analysis. The protocols use freely available, cross-platform software, are easy to implement, and do not require any programming expertise. Serving as data-driven starting points for further in-depth analysis, validation, and biological experimentation, these and related bioinformatic approaches can accelerate the functional interpretation of RPPA data.
使用反相蛋白质阵列（RPPA）平台及一组靶标特异性抗体对蛋白质和磷酸化蛋白质进行分子谱分析，能够以微阵列形式对许多生物样品进行平行、定量蛋白质组分析。因此，RPPA分析可生成大量多维数据，必须对这些数据进行有效查询和解释。一系列用于数据挖掘的计算技术可应用于检测和探索数据结构，并从大型数据集中形成功能预测。本文详细介绍了两种RPPA数据计算分析方法：通过层次聚类分析识别蛋白质表达的相似模式，以及通过网络分析对蛋白质相互作用和信号关系进行建模。这些方案使用免费的跨平台软件，易于实施，且不需要任何编程专业知识。作为进一步深入分析、验证和生物学实验的数据驱动起点，这些及相关的生物信息学方法可加速RPPA数据的功能解释。
