GraphCompass

GraphCompass provides omics-adapted graph analysis methods for quantitatively evaluating and comparing the spatial arrangement of cells across different biological conditions.

Key Features:

• Cross-condition statistical analyses: Performs comparative analyses at the level of individual cell types, niches, and samples using various statistical approaches.
• Spatial organization metrics: Quantitatively evaluates cell spatial organization to identify how cellular organization and tissue architecture differ across disease states.
• Spatial pattern correlation: Identifies spatial patterns that correlate with pathological conditions.
• Visualization functions: Provides custom visualization functions for effective communication of comparative spatial analysis results.
• Multi-omics compatibility: Supports spatial proteomics (MIBI-TOF), spot-based transcriptomics (10× Genomics Visium), and single-cell resolved transcriptomics (Stereo-seq).

Scientific Applications:

• Disease state comparison: Compares cellular organization and tissue architecture across various disease states.
• Pathological correlation: Identifies which spatial patterns correlate with given pathological conditions.
• Spatial niche analysis: Analyzes spatial organization at the level of cellular niches across conditions.
• Cell type spatial evaluation: Evaluates spatial arrangement differences for individual cell types across samples.

Methodology:

GraphCompass builds upon the Squidpy spatial omics toolbox and implements graph analysis methods adapted for omics data.