SPIAT

SPIAT analyzes spatial patterns of cells in tissue microenvironments using a suite of spatial analysis algorithms compatible with single-cell spatial proteomics platforms.

Key Features:

• Data Processing and Quality Control: SPIAT reads spatial data in the form of X and Y coordinates of cells, marker intensities and cell phenotypes.
• Visualization: SPIAT provides visualization tools for spatial data.
• Colocalization Analysis: SPIAT calculates cell colocalization metrics.
• Immune Microenvironment Categorization: SPIAT categorizes the immune microenvironment relative to tumor areas.
• Cellular Neighborhood Analysis: SPIAT analyzes cellular neighborhoods.
• Spatial Heterogeneity Quantification: SPIAT quantifies spatial heterogeneity.
• Spatial Metrics Benchmarking: Ten spatial metrics are benchmarked using simulated data.

Scientific Applications:

• Cancer Immune Subtyping: SPIAT uncovers cancer immune subtypes correlated with prognosis in cancer.
• Cell Dysfunction Characterization: SPIAT characterizes cell dysfunction in diabetes.
• Spatial Pattern Quantification: SPIAT quantifies spatial patterns to identify and validate correlates of clinical outcomes.
• Method Development Support: SPIAT supports the development of downstream analysis methods and benchmarking tools.

Methodology:

SPIAT reads spatial data as X and Y coordinates of cells, marker intensities and cell phenotypes, and computes multiple colocalization, neighborhood and spatial heterogeneity metrics. Ten spatial metrics are benchmarked using simulated data generated with spaSim.