SVCA

SVCA quantifies contributions of tissue spatial structure and cell–cell interactions to gene and protein expression variance in single-cell multiplexed spatially resolved RNA and protein datasets, including Imaging Mass Cytometry.

Key Features:

• Quantification of Spatial Variation: Quantifies spatial variation in gene and protein expression attributable to tissue organization in spatial molecular datasets.
• Cell-Cell Interaction Analysis: Estimates and isolates the contribution of cell–cell interactions to expression variability at single-cell resolution.
• Variance Component Decomposition: Decomposes total expression variance into components attributable to tissue spatial structure, cell–cell interactions, and other sources.
• Interpretable Spatial Variance Signatures: Derives interpretable spatial variance signatures linking variance components to molecular patterns, demonstrated on breast cancer Imaging Mass Cytometry data.
• Identification of Gene Families: Identifies gene families associated with interaction-driven variance in high-dimensional imaging-derived RNA data.

Scientific Applications:

• Oncology (tumor heterogeneity): Dissects spatial organization and cellular heterogeneity in tumors, as exemplified by breast cancer Imaging Mass Cytometry analyses.
• Spatially resolved molecular profiling: Analyzes multiplexed spatially resolved RNA and protein expression at single-cell resolution to study cellular communication and microenvironmental effects.

Methodology:

Analyzes spatial molecular datasets by decomposing variance components to isolate contributions from tissue spatial structure and cell–cell interactions and derives interpretable spatial variance signatures.