spatialTIME

spatialTIME performs spatial statistical analysis and visualization of multiplex immunofluorescence (mIF) data to characterize the tumor immune microenvironment (TIME).

Key Features:

• Spatial statistical analyses: Computes univariate and bivariate spatial metrics including Ripley's K, Besag's L, Macron's M and G, and nearest neighbor distance to assess spatial distribution and organization of immune cells.
• Visualization of spatial architecture: Generates plots that illustrate the spatial arrangement of cellular components within tissue samples.
• Integration with clinical data: Statistically correlates abundance measures derived from mIF data with patient clinical features.
• Quantitative image analysis: Leverages quantitative digital image analysis techniques applied to mIF staining data to derive abundance and spatial organization measures.

Scientific Applications:

• Prognostic studies: Enables identification of spatially informed prognostic markers related to cancer progression and patient outcomes through detailed immune cell spatial analysis.
• Immunotherapy research: Supports investigation of how spatial patterns of immune cells influence responses to immunotherapies.

Methodology:

Implemented in R; applies quantitative digital image analysis to multiplex immunofluorescence (mIF) staining data and computes spatial statistics including univariate and bivariate metrics such as Ripley's K, Besag's L, Macron's M and G, and nearest neighbor distance.