背景:网络分析,例如基因共表达网络,代谢网络和生态网络已成为系统级生物数据研究的核心方法。存在用于生成和分析此类网络的几个软件包,来自相关分数或称为加权拓扑重叠(wTO)的转换分数的绝对值。然而,因为基因调节过程可以上调或下调基因,在构建基因共表达网络时,明确考虑正相关和负相关是非常有意义的。
结果:这里,我们给出了一个用于计算加权拓扑重叠(wTO)的R包,That,与现有的软件包相比,明确解决wTO值的符号,因此对于分析基因调控网络特别有价值。该软件包包括每个成对基因得分的p值(原始和调整)的计算。我们的软件包还允许从时间序列计算网络(没有重复)。由于来自独立数据集(生物重复或相关研究)的网络由于数据中的技术和生物噪声而不相同,我们另外,将一种从两个或多个网络计算共识网络(CN)的新方法纳入我们的R包中。要以图形方式检查生成的网络,R包包含一个可视化工具,这允许直接网络操作和访问节点和链路信息。在标准笔记本电脑上测试软件包时,我们可以在两个小时内对超过20,000个基因的系统进行所有计算。我们将我们的新wTO软件包与最先进的软件包进行了比较,并使用来自健康人类前额叶皮层样本的3个独立衍生数据集演示了wTO和CN功能的应用。为了展示时间序列应用程序的示例,我们使用了宏基因组学数据集。
结论:在这项工作中,我们开发了一个软件包,允许计算wTO网络,CN和R统计环境中的可视化工具。它可以在GPL-2开源许可证(https://cran)下在CRAN存储库中公开提供。r-project.org/web/packages/wTO/)。
BACKGROUND: Network analyses, such as of gene co-
expression networks, metabolic networks and ecological networks have become a central approach for the systems-level study of biological data. Several software packages exist for generating and analyzing such networks, either from correlation scores or the absolute value of a transformed score called weighted topological overlap (wTO). However, since gene regulatory processes can up- or down-regulate genes, it is of great interest to explicitly consider both positive and negative correlations when constructing a gene co-
expression network.
RESULTS: Here, we present an R package for calculating the weighted topological overlap (wTO), that, in contrast to existing packages, explicitly addresses the sign of the wTO values, and is thus especially valuable for the analysis of gene regulatory networks. The package includes the calculation of p-values (raw and adjusted) for each pairwise gene score. Our package also allows the calculation of networks from time series (without replicates). Since networks from independent datasets (biological repeats or related studies) are not the same due to technical and biological noise in the data, we additionally, incorporated a novel method for calculating a
consensus network (CN) from two or more networks into our R package. To graphically inspect the resulting networks, the R package contains a visualization tool, which allows for the direct network manipulation and access of node and link information. When testing the package on a standard laptop computer, we can conduct all calculations for systems of more than 20,000 genes in under two hours. We compare our new wTO package to state of art packages and demonstrate the application of the wTO and CN functions using 3 independently derived datasets from healthy human pre-frontal cortex samples. To showcase an example for the time series application we utilized a metagenomics data set.
CONCLUSIONS: In this work, we developed a software package that allows the computation of wTO networks, CNs and a visualization tool in the R statistical environment. It is publicly available on CRAN repositories under the GPL -2 Open Source License ( https://cran.r-project.org/web/packages/wTO/ ).