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

View ORCID ProfileTom C. Freeman, Sebastian Horsewell, View ORCID ProfileAnirudh Patir, Josh Harling-Lee, Tim Regan, Barbara B. Shih, James Prendergast, David A. Hume, Tim Angus
doi: https://doi.org/10.1101/2020.09.02.279349
Tom C. Freeman
1The Roslin Institute, Easter Bush Campus, The University of Edinburgh EH25 9RG, UK
2Kajeka Limited, Roslin Innovation Centre, Easter Bush Campus, The University of Edinburgh EH25 9RG, UK
4Janssen Immunology, 1400 McKean Road, Spring House, PA 19477 US
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  • For correspondence: tom.freeman@roslin.ed.ac.uk
Sebastian Horsewell
2Kajeka Limited, Roslin Innovation Centre, Easter Bush Campus, The University of Edinburgh EH25 9RG, UK
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Anirudh Patir
1The Roslin Institute, Easter Bush Campus, The University of Edinburgh EH25 9RG, UK
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Josh Harling-Lee
1The Roslin Institute, Easter Bush Campus, The University of Edinburgh EH25 9RG, UK
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Tim Regan
1The Roslin Institute, Easter Bush Campus, The University of Edinburgh EH25 9RG, UK
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Barbara B. Shih
1The Roslin Institute, Easter Bush Campus, The University of Edinburgh EH25 9RG, UK
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James Prendergast
1The Roslin Institute, Easter Bush Campus, The University of Edinburgh EH25 9RG, UK
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David A. Hume
3Mater Research Institute-University of Queensland, Translational Research Institute, Woolloongabba, Qld 4012, Australia
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Tim Angus
2Kajeka Limited, Roslin Innovation Centre, Easter Bush Campus, The University of Edinburgh EH25 9RG, UK
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Abstract

Quantitative and qualitative data derived from the analysis of genomes, genes, proteins or metabolites from tissue or cells are currently generated in huge volumes during biomedical research. Graphia is an open-source platform created for the graph-based analysis of such complex data, e.g. transcriptomics, proteomics, genomics data. The software imports data already defined as a network or a similarity matrix and is designed to rapidly visualise very large graphs in 2D or 3D space, providing a wide range of functionality for graph exploration. An extensive range of analysis algorithms, routines for graph transformation, and options for the visualisation of node and edge attributes are also available. Graphia’s core is extensible through the deployment of plugins, supporting rapid development of additional computational analyses and features necessary for a given analysis task or data source. A plugin for correlation network analysis is distributed with the core application, to support the generation of correlation graphs from any tabular matrix of continuous or discrete values. This provides a powerful analysis solution for the interpretation of high-dimensional data from many sources. Several use cases of Graphia are described, to showcase its wide range of applications. Graphia runs on all major desktop operating systems and is freely available to download from https://graphia.app/.

Competing Interest Statement

Graphia was originally designed and built by Kajeka Ltd., a University of Edinburgh spin-out company (2015-2020). Kajeka Ltd was founded by TCF, and TA and SH were employees of the company, DAH was an investor/director. Following the closure of the company the source code of Graphia was made open source.

Footnotes

  • https://graphia.app/

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted September 03, 2020.
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Graphia: A platform for the graph-based visualisation and analysis of complex data
Tom C. Freeman, Sebastian Horsewell, Anirudh Patir, Josh Harling-Lee, Tim Regan, Barbara B. Shih, James Prendergast, David A. Hume, Tim Angus
bioRxiv 2020.09.02.279349; doi: https://doi.org/10.1101/2020.09.02.279349
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Graphia: A platform for the graph-based visualisation and analysis of complex data
Tom C. Freeman, Sebastian Horsewell, Anirudh Patir, Josh Harling-Lee, Tim Regan, Barbara B. Shih, James Prendergast, David A. Hume, Tim Angus
bioRxiv 2020.09.02.279349; doi: https://doi.org/10.1101/2020.09.02.279349

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