SpatialCells

SpatialCells performs region-based exploratory analysis and characterizes tumor microenvironments (TMEs) using spatially resolved multiplexed single-cell data.

Key Features:

• Region-Based Analysis: Performs targeted exploratory analysis of specific regions within TMEs to assess localized interactions among malignant and non-malignant cells, including immune and stromal components.
• Automated Feature Extraction: Automates extraction of molecular, cellular, and spatial features from multiplexed single-cell datasets, scaling to large datasets including millions of cells.
• Comprehensive Characterization: Integrates molecular, cellular, and spatial data dimensions to provide a holistic characterization of tumor microenvironments.
• Support for Association Analyses and Machine Learning Predictions: Produces processed feature sets suitable for downstream association analyses and machine learning–based predictive modeling.

Scientific Applications:

• Cancer research: Analyze spatially resolved single-cell data to study tumor growth dynamics and progression across diverse malignancies.
• Tumor–immune interaction studies: Investigate immune evasion mechanisms and spatial interactions between malignant cells and immune populations.
• Stromal interaction characterization: Explore stromal support systems and their spatial relationships with tumor cells within the TME.
• Biomarker discovery and predictive modeling: Enable identification of spatially informed biomarkers and development of predictive models for targeted therapies and personalized medicine.

Methodology:

Implements computational methods for region-based analyses of multiplexed single-cell data, optimized for high-dimensional datasets and automated extraction of molecular, cellular, and spatial features.