SPIAT

SPIAT performs quantitative spatial analysis of single-cell resolution tissue imaging data in R to extract cellular organization, neighbourhood composition, intercellular distances, and phenotype associations.

Key Features:

• Data processing and quality control: Processes large-scale spatial datasets and performs quality control on single-cell resolution tissue imaging data.
• Visualization and data handling: Generates visualizations and manages spatial data for interpretation and downstream analysis.
• Cell cluster identification: Implements algorithms to identify clusters of cells within tissue samples.
• Cell margins and gradients detection: Detects cell margins and gradients to characterize spatial organization.
• Neighbourhood proportions calculation: Calculates neighbourhood proportions to quantify local cellular composition.
• Phenotype prediction: Implements algorithms to predict cell phenotypes from spatial features.
• Cell distances and community detection: Calculates intercellular distances and detects cellular communities to analyze spatial interactions.
• OPAL/HALO/InForm compatibility: Compatible with OPAL multiplex immunohistochemistry image outputs processed by HALO and InForm.

Scientific Applications:

• OPAL multiplex immunohistochemistry analysis: Enables quantitative spatial analysis of OPAL mIHC images processed with HALO and InForm.
• Cancer evolution: Facilitates study of cellular organization and interactions relevant to cancer evolution.
• Developmental biology: Supports analysis of spatial patterning and cell organization in developmental studies.
• Immunology: Enables analysis of immune cell spatial distributions and microenvironmental interactions.

Methodology:

Processing of large-scale spatial datasets, quality control, visualization, identification of cell clusters, detection of cell margins and gradients, calculation of neighbourhood proportions and cell distances, community detection, and phenotype prediction algorithms.