TY - JOUR
T1 - Graphia
T2 - A platform for the graph-based visualisation and analysis of high dimensional data
AU - Freeman, Tom C
AU - Horsewell, Sebastian
AU - Patir, Anirudh
AU - Harling-Lee, Josh
AU - Regan, Tim
AU - Shih, Barbara B
AU - Prendergast, James
AU - Hume, David A
AU - Angus, Tim
N1 - Funding Information:
Graphia was originally designed and built by Kajeka Ltd., a University of Edinburgh spin-out company (2015-2020). We would like to acknowledge all those who supported this venture, in particular grant funding from Scottish Enterprise (SMART/14/034 / 14/9168). Although no specific academic grant funding was received for this project, during a period of Graphia’s development by Kajeka, TCF and JP were employed by the University and supported by the Roslin Institute’s Strategic Grant from the UK’s Biotechnology and Biological Sciences Research Council (BBSRC) [BBS/E/D/10002071]. More recently Janssen Immunology have funded further development of the tool (core funding). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Graphia was originally designed and built by Kajeka Ltd., a University of Edinburgh spin-out company (2015–2020).
Publisher Copyright:
Copyright: © 2022 Freeman et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
PY - 2022/7/25
Y1 - 2022/7/25
N2 - 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/.
AB - 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/.
U2 - 10.1371/journal.pcbi.1010310
DO - 10.1371/journal.pcbi.1010310
M3 - Article
C2 - 35877685
SN - 1553-734X
VL - 18
JO - PLoS Computational Biology
JF - PLoS Computational Biology
IS - 7
M1 - e1010310
ER -