Forman Joshua J, Clemons Paul A, Schreiber Stuart L, Haggarty Stephen J
The Broad Institute of MIT & Harvard University, Cambridge, MA 02141, USA.
BMC Bioinformatics. 2005 Oct 19;6:260. doi: 10.1186/1471-2105-6-260.
Graph theory provides a computational framework for modeling a variety of datasets including those emerging from genomics, proteomics, and chemical genetics. Networks of genes, proteins, small molecules, or other objects of study can be represented as graphs of nodes (vertices) and interactions (edges) that can carry different weights. SpectralNET is a flexible application for analyzing and visualizing these biological and chemical networks.
Available both as a standalone .NET executable and as an ASP.NET web application, SpectralNET was designed specifically with the analysis of graph-theoretic metrics in mind, a computational task not easily accessible using currently available applications. Users can choose either to upload a network for analysis using a variety of input formats, or to have SpectralNET generate an idealized random network for comparison to a real-world dataset. Whichever graph-generation method is used, SpectralNET displays detailed information about each connected component of the graph, including graphs of degree distribution, clustering coefficient by degree, and average distance by degree. In addition, extensive information about the selected vertex is shown, including degree, clustering coefficient, various distance metrics, and the corresponding components of the adjacency, Laplacian, and normalized Laplacian eigenvectors. SpectralNET also displays several graph visualizations, including a linear dimensionality reduction for uploaded datasets (Principal Components Analysis) and a non-linear dimensionality reduction that provides an elegant view of global graph structure (Laplacian eigenvectors).
SpectralNET provides an easily accessible means of analyzing graph-theoretic metrics for data modeling and dimensionality reduction. SpectralNET is publicly available as both a .NET application and an ASP.NET web application from http://chembank.broad.harvard.edu/resources/. Source code is available upon request.
图论为各种数据集建模提供了一个计算框架，包括那些来自基因组学、蛋白质组学和化学遗传学的数据集。基因、蛋白质、小分子或其他研究对象的网络可以表示为节点（顶点）和相互作用（边）的图，这些边可以携带不同的权重。SpectralNET是一个用于分析和可视化这些生物和化学网络的灵活应用程序。
SpectralNET既可以作为独立的.NET可执行文件，也可以作为ASP.NET Web应用程序使用，它在设计时专门考虑了图论指标的分析，这是当前可用应用程序难以完成的计算任务。用户可以选择上传一个网络以使用各种输入格式进行分析，或者让SpectralNET生成一个理想化的随机网络，以便与真实世界的数据集进行比较。无论使用哪种图生成方法，SpectralNET都会显示有关图的每个连通组件的详细信息，包括度分布、按度聚类系数和按度平均距离的图。此外，还会显示有关所选顶点的大量信息，包括度、聚类系数、各种距离度量以及邻接、拉普拉斯和归一化拉普拉斯特征向量的相应组件。SpectralNET还会显示几种图可视化，包括上传数据集的线性降维（主成分分析）和提供全局图结构优雅视图的非线性降维（拉普拉斯特征向量）。
SpectralNET为数据建模和降维提供了一种易于访问的分析图论指标的方法。SpectralNET作为.NET应用程序和ASP.NET Web应用程序均可从http://chembank.broad.harvard.edu/resources/公开获取。可根据要求提供源代码。
