Author
Listed:
- Tom C Freeman
- Sebastian Horsewell
- Anirudh Patir
- Josh Harling-Lee
- Tim Regan
- Barbara B Shih
- James Prendergast
- David A Hume
- Tim Angus
Abstract
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/.Author summary: Graphia is a new visual analytics platform specifically created for the network-based analysis of large and complex data, such as that generated in huge amounts by modern biological analyses. It works in a data agnostic, hypothesis-free manner to generate correlation networks from any table of numerical or discrete values, thereafter providing a means to rapidly visualise the often very large networks that result, in either 2D or 3D space. Following network construction, the tool offers an extensive range of analysis algorithms, routines for network transformation, and options for the visualisation of metadata. This provides a powerful analysis solution for the exploration and interpretation of high-dimensional data from any source, as well as any data already defined as a network. Several use cases of Graphia are described to showcase its wide range of applications in the analysis biological data. Graphia is open source and free to all.
Suggested Citation
Tom C Freeman & Sebastian Horsewell & Anirudh Patir & Josh Harling-Lee & Tim Regan & Barbara B Shih & James Prendergast & David A Hume & Tim Angus, 2022.
"Graphia: A platform for the graph-based visualisation and analysis of high dimensional data,"
PLOS Computational Biology, Public Library of Science, vol. 18(7), pages 1-17, July.
Handle:
RePEc:plo:pcbi00:1010310
DOI: 10.1371/journal.pcbi.1010310
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