Division of Experimental Hematology and Cancer Biology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.
BMC Genomics. 2012 Jun 25;13:282. doi: 10.1186/1471-2164-13-282.
Functional analyses of genomic data within the context of a priori biomolecular networks can give valuable mechanistic insights. However, such analyses are not a trivial task, owing to the complexity of biological networks and lack of computational methods for their effective integration with experimental data.
We developed a software application suite, NetWalker, as a one-stop platform featuring a number of novel holistic (i.e. assesses the whole data distribution without requiring data cutoffs) data integration and analysis methods for network-based comparative interpretations of genome-scale data. The central analysis components, NetWalk and FunWalk, are novel random walk-based network analysis methods that provide unique analysis capabilities to assess the entire data distributions together with network connectivity to prioritize molecular and functional networks, respectively, most highlighted in the supplied data. Extensive inter-operability between the analysis components and with external applications, including R, adds to the flexibility of data analyses. Here, we present a detailed computational analysis of our microarray gene expression data from MCF7 cells treated with lethal and sublethal doses of doxorubicin.
NetWalker, a detailed step-by-step tutorial containing the analyses presented in this paper and a manual are available at the web site http://netwalkersuite.org.
在先验生物分子网络的背景下对基因组数据进行功能分析，可以提供有价值的机制见解。然而，由于生物网络的复杂性以及缺乏将其与实验数据有效整合的计算方法，此类分析并非易事。
我们开发了一个软件应用程序套件 NetWalker，作为一个一站式平台，具有许多新颖的整体（即评估整个数据分布，而无需数据截止）数据集成和分析方法，用于基于网络的基因组规模数据的比较解释。中央分析组件 NetWalk 和 FunWalk 是基于随机游走的新型网络分析方法，它们分别提供了独特的分析能力，可评估整个数据分布以及网络连接性，以优先考虑分子和功能网络，这在提供的数据中得到了充分体现。分析组件之间以及与外部应用程序（包括 R）之间的广泛互操作性增加了数据分析的灵活性。在这里，我们对 MCF7 细胞用致死和亚致死剂量的阿霉素处理后的微阵列基因表达数据进行了详细的计算分析。
NetWalker 网站提供了详细的分步教程，其中包含本文中呈现的分析以及手册。http://netwalkersuite.org
