Research Area:  Machine Learning

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. Graphics 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.

Keywords:  
Graphia
Graph-based visualisation
Transcriptomics
Proteomic
Sgenomics data

Author(s) Name:   View ORCID ProfileTom C. Freeman, Sebastian Horsewell, View ORCID ProfileAnirudh Patir, Josh Harling-Lee, Tim Regan, Barbara B. Shih

Publisher name:  bioRxiv

DOI:  10.1101/2020.09.02.279349

Volume Information:  

Paper Link:   https://www.biorxiv.org/content/10.1101/2020.09.02.279349v1.abstract