Systems Biology Group, Biostatistics Branch, National Institute of Environmental Health Sciences, National Institute of Health, Research Triangle Park, NC 27709, USA, School of Mathematics and Statistics, University of Sydney, Diabetes and Obesity Program, Garvan Institute of Medical Research, NSW 2006, Australia and Metabolism in Human Disease Unit, Institute of Molecular and Cellular Biology, A*Star, 61 Biopolis Drive, Proteos 138673, Singapore.
Bioinformatics. 2014 Mar 15;30(6):808-14. doi: 10.1093/bioinformatics/btt616. Epub 2013 Oct 27.
With the advancement of high-throughput techniques, large-scale profiling of biological systems with multiple experimental perturbations is becoming more prevalent. Pathway analysis incorporates prior biological knowledge to analyze genes/proteins in groups in a biological context. However, the hypotheses under investigation are often confined to a 1D space (i.e. up, down, either or mixed regulation). Here, we develop direction pathway analysis (DPA), which can be applied to test hypothesis in a high-dimensional space for identifying pathways that display distinct responses across multiple perturbations.
Our DPA approach allows for the identification of pathways that display distinct responses across multiple perturbations. To demonstrate the utility and effectiveness, we evaluated DPA under various simulated scenarios and applied it to study insulin action in adipocytes. A major action of insulin in adipocytes is to regulate the movement of proteins from the interior to the cell surface membrane. Quantitative mass spectrometry-based proteomics was used to study this process on a large-scale. The combined dataset comprises four separate treatments. By applying DPA, we identified that several insulin responsive pathways in the plasma membrane trafficking are only partially dependent on the insulin-regulated kinase Akt. We subsequently validated our findings through targeted analysis of key proteins from these pathways using immunoblotting and live cell microscopy. Our results demonstrate that DPA can be applied to dissect pathway networks testing diverse hypotheses and integrating multiple experimental perturbations.
The R package 'directPA' is distributed from CRAN under GNU General Public License (GPL)-3 and can be downloaded from: http://cran.r-project.org/web/packages/directPA/index.html
Supplementary data are available at Bioinformatics online.
随着高通量技术的进步，对具有多种实验扰动的生物系统进行大规模分析变得越来越普遍。途径分析结合了先前的生物学知识，以在生物学背景下对基因/蛋白质进行分组分析。然而，所研究的假设通常局限于一维空间（即上调、下调、要么或混合调节）。在这里，我们开发了方向途径分析（DPA），它可以应用于在高维空间中测试假设，以识别在多个扰动中显示出不同反应的途径。
我们的 DPA 方法允许识别在多个扰动中显示出不同反应的途径。为了展示其效用和有效性，我们在各种模拟场景下评估了 DPA，并将其应用于研究胰岛素在脂肪细胞中的作用。胰岛素在脂肪细胞中的主要作用是调节蛋白质从内部向细胞膜表面的运动。基于定量质谱的蛋白质组学用于在大规模上研究这个过程。合并数据集包括四个单独的处理。通过应用 DPA，我们确定了质膜运输中几个对胰岛素有反应的途径仅部分依赖于胰岛素调节的激酶 Akt。我们随后通过使用免疫印迹和活细胞显微镜对这些途径中的关键蛋白质进行靶向分析来验证我们的发现。我们的结果表明，DPA 可用于剖析测试不同假设和整合多个实验扰动的途径网络。
R 包 'directPA' 以 GNU 通用公共许可证 (GPL)-3 的形式从 CRAN 分发，可以从以下网址下载：http://cran.r-project.org/web/packages/directPA/index.html
补充数据可在 Bioinformatics 在线获取。
