4.6 Article

Graphia: A platform for the graph-based visualisation and analysis of high dimensional data

期刊

PLOS COMPUTATIONAL BIOLOGY
卷 18, 期 7, 页码 -

出版社

PUBLIC LIBRARY SCIENCE
DOI: 10.1371/journal.pcbi.1010310

关键词

-

资金

  1. Scottish Enterprise [SMART/14/034/14/9168]
  2. UK's Biotechnology and Biological Sciences Research Council (BBSRC) [BBS/E/D/10002071]

向作者/读者索取更多资源

Graphia is an open-source platform designed for graph-based analysis of large amounts of data generated from genome, gene, protein metabolite, and cell studies. It supports fast visualization of correlation matrices, measurement algorithms, graph transformation routines, as well as node and edge attribute visualization options, making it suitable for high-dimensional data interpretation and network analysis.
Graphia is an open-source platform created for the graph-based analysis of the huge amounts of quantitative and qualitative data currently being generated from the study of genomes, genes, proteins metabolites and cells. Core to Graphia's functionality is support for the calculation of correlation matrices from any tabular matrix of continuous or discrete values, whereupon the software is designed to rapidly visualise the often very large graphs that result in 2D or 3D space. Following graph construction, an extensive range of measurement algorithms, routines for graph transformation, and options for the visualisation of node and edge attributes are available, for graph exploration and analysis. Combined, these provide a powerful solution for the interpretation of high-dimensional data from many sources, or data already in the form of a network or equivalent adjacency matrix. Several use cases of Graphia are described, to showcase its wide range of applications in the analysis biological data. Graphia runs on all major desktop operating systems, is extensible through the deployment of plugins and is freely available to download from https://graphia.app/.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.6
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据