Graphia

Graphia visualizes and analyzes large-scale biological data as graphs to support network and similarity-matrix based interpretation of genomic, transcriptomic, proteomic, and metabolomic datasets.

Key Features:

• Graph-Based Analysis: Implements graph-based visualization and analysis for large, high-dimensional datasets represented as networks or similarity matrices.
• Data Import Flexibility: Supports importing data defined as networks or similarity matrices and tabular matrices of continuous or discrete values for downstream network generation.
• Visualization Capabilities: Renders very large graphs in 2D and 3D to explore relationships and structure within datasets.
• Extensive Functionality: Provides numerous analysis algorithms and routines for graph transformation and network exploration.
• Customizable Visualization: Encodes and displays node and edge attributes to represent biological metadata and measured values.
• Plugin Architecture: Extensible via plugins and includes a built-in plugin for correlation network analysis that generates correlation graphs from tabular matrices of continuous or discrete values.

Scientific Applications:

• Genomics: Identifying network relationships and modules within genomic datasets.
• Transcriptomics: Constructing and exploring gene co-expression and correlation networks from transcriptomic expression matrices.
• Proteomics: Mapping protein–protein or protein abundance relationships using network representations of proteomic data.
• Metabolomics: Exploring metabolite association networks and similarity-based relationships in metabolomic datasets.

Methodology:

Adopts a graph-based approach by importing data as networks or similarity matrices, generates correlation graphs from tabular matrices of continuous or discrete values, applies numerous graph analysis and transformation algorithms, and renders large graphs in 2D and 3D with extensible plugin-based computation.