shu

shu integrates and visualizes high-dimensional biological pathways by consolidating diverse multi-omics datasets into comprehensive metabolic maps to reveal condition-specific molecular distributions.

Key Features:

• Metabolic maps: Metabolic maps developed with contributions from the metabolic modeling community that provide a structured framework for pathway-level visualization.
• Multi-omics integration: Consolidates diverse, condition-specific multi-omics datasets into unified metabolic maps for comparative pathway analysis.
• Visualization of condition-specific distributions: Displays distributions across conditions within metabolic networks to support interpretation of system-level changes.
• Python API (Grammar of Graphics): A Python API implemented on Grammar of Graphics principles for programmable generation and customization of visualizations.

Scientific Applications:

• Systems biology: Pathway-level integration and visualization of multi-omics data to support systems-level analysis.
• Personalized medicine: Mapping condition-specific molecular profiles onto metabolic networks to inform personalized-medicine analyses.
• Molecular research requiring detailed insights: Supporting studies that require comprehensive, pathway-centric interpretation of high-dimensional datasets.

Methodology:

Consolidates diverse multi-omics datasets into comprehensive metabolic maps and exposes a Python API based on Grammar of Graphics for visualization.