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

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 - Journal article

C2 - 35877685

AN - SCOPUS:85135380594

VL - 18

JO - PLoS Computational Biology

JF - PLoS Computational Biology

SN - 1553-734X

IS - 7

M1 - e1010310

ER -