Oxford Centre for Industrial and Applied Mathematics, Mathematical Institute, University of Oxford, Oxford, UK; Department of Biomedical Engineering and Computational Science, School of Science, Aalto University, Helsinki, Finland.
Mol Ecol Resour. 2015 Jan;15(1):117-22. doi: 10.1111/1755-0998.12290. Epub 2014 Jun 26.
The recent application of graph-based network theory analysis to biogeography, community ecology and population genetics has created a need for user-friendly software, which would allow a wider accessibility to and adaptation of these methods. EDENetworks aims to fill this void by providing an easy-to-use interface for the whole analysis pipeline of ecological and evolutionary networks starting from matrices of species distributions, genotypes, bacterial OTUs or populations characterized genetically. The user can choose between several different ecological distance metrics, such as Bray-Curtis or Sorensen distance, or population genetic metrics such as FST or Goldstein distances, to turn the raw data into a distance/dissimilarity matrix. This matrix is then transformed into a network by manual or automatic thresholding based on percolation theory or by building the minimum spanning tree. The networks can be visualized along with auxiliary data and analysed with various metrics such as degree, clustering coefficient, assortativity and betweenness centrality. The statistical significance of the results can be estimated either by resampling the original biological data or by null models based on permutations of the data.
最近,基于图的网络理论分析在生物地理学、群落生态学和种群遗传学中的应用,产生了对用户友好型软件的需求,这将使这些方法更容易被使用和适应。EDENetworks 旨在通过提供一个易于使用的界面来填补这一空白,该界面用于从物种分布、基因型、细菌 OTU 或遗传特征的种群矩阵开始,对生态和进化网络的整个分析流程进行分析。用户可以在几种不同的生态距离度量(如 Bray-Curtis 或 Sorensen 距离)或种群遗传距离度量(如 FST 或 Goldstein 距离)之间进行选择,将原始数据转换为距离/不相似矩阵。然后,该矩阵通过基于渗流理论的手动或自动阈值处理或通过构建最小生成树来转换为网络。可以结合辅助数据可视化网络,并使用各种度量标准(如度数、聚类系数、配度和中间中心性)对其进行分析。结果的统计显著性可以通过对原始生物数据进行重采样或通过基于数据置换的零模型来估计。