Graphia (software)

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GraphiaDeveloper(s)| Graphia Technologies Limited

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Initial release| 1 March 2019; 6 years ago (2019-03-01)[1]
Stable release|

3.2 / 22 November 2022; 2 years ago (2022-11-22)[2]

Written in| C++, OpenGL, Qt (software)
Operating system| Windows, macOS, Linux
Available in| English
Type| Visualization
License| GNU General Public License
Website| graphia.app

Graphia is an open source data visualization software package that uses graph drawing for the interpretation of large and complex datasets.

## Contents

* 1 History
* 2 Usage
* 3 Functionality
* 4 See also
* 5 References
* 6 External links

## History[edit]

Created by researchers hailing from the Roslin Institute, Graphia was developed by a company spun out from the University of Edinburgh. Unable to make a commercial success of it, the company folded in 2020[3] and the software was subsequently released[4] under an open source license.

## Usage[edit]

Graphia was originally designed to create and visualize correlation graphs from the Raw data|primary data that is produced in the life sciences. In this sense it may be considered a bioinformatics tool, indeed this is its primary use.[5][6][7][8] Nevertheless, Graphia is data type agnostic and has also been used in other fields such as Social network analysis[9] and Blockchain analysis.[10] Architecturally, a plugin system is used, meaning that Graphia can be extended beyond what is available by default.

## Functionality[edit]

Graphia has a focus on interactivity, with its Force-directed graph drawing|layout process occurring in real time and graph construction parameters adjustable on the fly. The latter is realised through a parametric style transformation system whereby individual processes which permute the graph are chained together, the results of which are immediately visible to the user. This approach reduces the round trip time that might be experienced compared to a process where the graph is repeatedly constructed from scratch, and therefore encourages experimentation. In this way Graphia is highly data driven. A number of transforms are provided for manipulating and analysing the graph, using various algorithms such as Louvain Clustering, Betweenness centrality and k-NN.

Given Graphia's focus on creating graphs from large and complex datasets, it follows that it is able to equivalently visualize very large graphs.[11] This capability is obviously hardware dependent, but at the top end graphs consisting of millions of nodes and edges are successfully rendered. Furthermore, the visualization is (optionally) presented in 3D which can illuminate structure not otherwise apparent using more traditional methods.

## See also[edit]

* Graph (discrete mathematics)
* Graph theory
* Graph (data structure)
* Network science
* Bioinformatics
* Biological network

## References[edit]

1. ↑ https://github.com/graphia-app/graphia/releases/tag/1.0 Graphia 1.0 release date
2. ↑ https://github.com/graphia-app/graphia/releases/tag/3.2 Graphia 3.2 release date
3. ↑ "Notice of dissolution for Kajeka Limited". Companies House. Retrieved 15 January 2022.
4. ↑ Freeman, Tom C.; Horsewell, Sebastian; Patir, Anirudh; Harling-Lee, Josh; Regan, Tim; Shih, Barbara B.; Prendergast, James; Hume, David A.; Angus, Tim (2022). "Graphia: A platform for the graph-based visualisation and analysis of high dimensional data". PLOS Comput Biol. 18 (7): e1010310. doi:10.1371/journal.pcbi.1010310. PMC 9352203. PMID 35877685.
5. ↑ Thwaites, Ryan S.; Sanchez Sevilla Uruchurtu, Ashley; Siggins, Matthew K.; Liew, Felicity; Russell, Clark D.; Moore, Shona C.; Fairfield, Cameron; Carter, Edwin; Abrams, Simon; Short, Charlotte-Eve; Thaventhiran, Thilipan; Bergstrom, Emma; Gardener, Zoe; Ascough, Stephanie; Chiu, Christopher; Docherty, Annemarie B.; Hunt, David; Crow, Yanick J.; Solomon, Tom; Taylor, Graham P.; Turtle, Lance; Harrison, Ewen M.; Dunning, Jake; Semple, Malcolm G.; Baillie, J. Kenneth; Openshaw, Peter J. M. (10 March 2021). "Inflammatory profiles across the spectrum of disease reveal a distinct role for GM-CSF in severe COVID-19". Science Immunology. American Association for the Advancement of Science (AAAS). 6 (57): eabg9873. doi:10.1126/sciimmunol.abg9873. ISSN 2470-9468. PMC 8128298. PMID 33692097.
6. ↑ Dimonaco, Nicholas J.; Salavati, Mazdak; Shih, Barbara B. (30 December 2020). "Computational Analysis of SARS-CoV-2 and SARS-Like Coronavirus Diversity in Human, Bat and Pangolin Populations". Viruses. MDPI AG. 13 (1): 49. doi:10.3390/v13010049. ISSN 1999-4915. PMC 7823979. PMID 33396801.
7. ↑ Pridans, Clare; Irvine, Katharine M.; Davis, Gemma M.; Lefevre, Lucas; Bush, Stephen J.; Hume, David A. (1 February 2021). "Transcriptomic Analysis of Rat Macrophages". Frontiers in Immunology. Frontiers Media SA. 11: 594594. doi:10.3389/fimmu.2020.594594. ISSN 1664-3224. PMC 7902030. PMID 33633725.
8. ↑ Bartley, Kathryn; Chen, Wan; Lloyd Mills, Richard I.; Nunn, Francesca; Price, Daniel R. G.; Rombauts, Stephane; Van de Peer, Yves; Roy, Lise; Nisbet, Alasdair J.; Burgess, Stewart T. G. (7 April 2021). "Transcriptomic analysis of the poultry red mite, Dermanyssus gallinae, across all stages of the lifecycle". BMC Genomics. Springer Science and Business Media LLC. 22 (1): 248. doi:10.1186/s12864-021-07547-9. ISSN 1471-2164. PMC 8028124. PMID 33827430.
9. ↑ Marinov, Boris (20 October 2021). "Combining NLP Features with 3D Network Visualisations: A Game of Thrones Analysis". Medium. Retrieved 12 August 2022.
10. ↑ Makarov, Igor; Schoar, Antoinette (2021). "Blockchain Analysis of the Bitcoin Market". Cambridge, MA: National Bureau of Economic Research. doi:10.3386/w29396.
11. ↑ Iguana, Sviatoslav (5 June 2020). "Large Graph Visualization Tools and Approaches". Towards Data Science. Retrieved 6 October 2021.

## External links[edit]

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