Spaco

Spaco provides spatially aware colorization for single-cell-resolution spatially resolved transcriptomics (SRT) data to improve visualization and interpretation of spatial cell-type relationships.

Key Features:

• Spatially Aware Colorization: Uses the Degree of Interlacement metric to construct a weighted graph that evaluates and refines spatial relationships among cell types for distinct coloring.
• Adaptive Palette Selection: Implements an adaptive palette selection strategy that optimizes color assignments to amplify chromatic distinctions reflecting complex tissue spatial interactions.
• Benchmarking and Performance: Demonstrated superior performance when evaluated across four diverse datasets by better capturing intricate spatial relationships and improving visual clarity compared to existing solutions.
• Color-vision Accessibility: Produces color palettes that accommodate color vision deficiencies to ensure interpretability of visual outputs.
• Implementation: Provides code implementations in Python and R.

Scientific Applications:

• Tissue architecture and niche microenvironments: Facilitates visualization and interpretation of tissue architecture and niche-specific microenvironments in SRT data.
• Neurobiology: Supports analysis of complex brain tissue spatial organization where perceptual ambiguity can hinder interpretation.
• Oncology: Aids visualization of tumor microenvironments and spatial interactions among cell types within tumors.
• Developmental biology: Helps resolve spatially dependent cell-type patterns during development from SRT data.

Methodology:

Constructs a weighted graph using the Degree of Interlacement metric to evaluate and refine spatial relationships, applies an adaptive palette selection strategy to optimize color assignments, and was benchmarked across four datasets against existing methods.