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wTO:一个用于计算加权拓扑重叠和共识网络的 R 包,具有集成的可视化工具。

wTO: an R package for computing weighted topological overlap and a consensus network with integrated visualization tool.

机构信息

Department of Computer Science, Interdisciplinary Center of Bioinformatics, University of Leipzig, Haertelstrasse 16-18, Leipzig, 04109, Germany.

Swarm Intelligence and Complex Systems Group, Faculty of Mathematics and Computer Science, University of Leipzig, Augustusplatz 10, Leipzig, 04109, Germany.

出版信息

BMC Bioinformatics. 2018 Oct 24;19(1):392. doi: 10.1186/s12859-018-2351-7.


DOI:10.1186/s12859-018-2351-7
PMID:30355288
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6201546/
Abstract

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. CONCLUSION: 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/ ).

摘要

背景:网络分析,如基因共表达网络、代谢网络和生态网络,已成为系统研究生物数据的核心方法。有几个软件包可用于生成和分析此类网络,这些网络可以基于相关分数,或者基于称为加权拓扑重叠(weighted topological overlap,wTO)的分数的绝对值来构建。然而,由于基因调控过程可以上调或下调基因,因此在构建基因共表达网络时,明确考虑正相关和负相关非常重要。

结果:本文介绍了一个用于计算加权拓扑重叠(weighted topological overlap,wTO)的 R 包,与现有的包不同,该包明确考虑了 wTO 值的符号,因此对于基因调控网络的分析特别有价值。该包包括计算每个基因对分数的 p 值(原始和调整后)。我们的包还允许从时间序列(无重复)计算网络。由于独立数据集(生物重复或相关研究)的网络由于数据中的技术和生物学噪声而不同,我们还在我们的 R 包中加入了一种从两个或多个网络计算共识网络(consensus network,CN)的新方法。为了图形化地检查生成的网络,R 包包含一个可视化工具,允许直接进行网络操作并访问节点和链接信息。在标准笔记本电脑上测试该包时,我们可以在不到两个小时的时间内对超过 20000 个基因的系统进行所有计算。我们将我们的新 wTO 包与最先进的包进行了比较,并使用来自健康人类前额叶皮层样本的 3 个独立数据集演示了 wTO 和 CN 函数的应用。为了展示时间序列应用的示例,我们使用了一个宏基因组数据集。

结论:在这项工作中,我们开发了一个软件包,允许在 R 统计环境中计算 wTO 网络、CN 和可视化工具。它在 CRAN 存储库中以 GPL-2 开源许可证(https://cran.r-project.org/web/packages/wTO/)的形式公开提供。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d112/6201546/2fae4f7b9f5a/12859_2018_2351_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d112/6201546/3d4f7d1a11f8/12859_2018_2351_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d112/6201546/1b6ef718bc91/12859_2018_2351_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d112/6201546/8c2b341a3551/12859_2018_2351_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d112/6201546/a1242c9f23dd/12859_2018_2351_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d112/6201546/c4ebf43ee034/12859_2018_2351_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d112/6201546/c4cb91a992e0/12859_2018_2351_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d112/6201546/2fae4f7b9f5a/12859_2018_2351_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d112/6201546/3d4f7d1a11f8/12859_2018_2351_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d112/6201546/1b6ef718bc91/12859_2018_2351_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d112/6201546/8c2b341a3551/12859_2018_2351_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d112/6201546/a1242c9f23dd/12859_2018_2351_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d112/6201546/c4ebf43ee034/12859_2018_2351_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d112/6201546/c4cb91a992e0/12859_2018_2351_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d112/6201546/2fae4f7b9f5a/12859_2018_2351_Fig7_HTML.jpg

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