Characterizing cell-type spatial relationships across length scales in spatially resolved omics data.

Area of Science:

• Spatial biology
• Computational biology
• Bioinformatics

Background:

• Spatially resolved omics (SRO) technologies reveal cell types and their tissue organization.
• Understanding multi-scale cell-type spatial relationships is crucial for tissue function insights.

Purpose of the Study:

• To introduce CRAWDAD, an open-source R package for quantifying multi-scale cell-type spatial relationships.
• To demonstrate CRAWDAD's utility in analyzing diverse SRO datasets and comparing spatial patterns.

Main Methods:

• Development of CRAWDAD, an R package for Cell-type Relationship Analysis Workflow Done Across Distances.
• Application of CRAWDAD to simulated and real SRO datasets from various tissues and technologies.
• Evaluation of CRAWDAD's performance against existing spatial analysis methods.

Main Results:

• CRAWDAD successfully recapitulated expected cell-type spatial relationships in diverse SRO datasets.
• The package enabled multi-scale characterization and comparison of spatial relationships across samples.
• Analysis of human spleen SRO data identified consistent and sample-specific cell-type spatial patterns.

Conclusions:

• CRAWDAD provides quantitative metrics for identifying, characterizing, and comparing cell-type spatial relationships.
• The package facilitates deeper insights into tissue organization and function through multi-scale spatial analysis.
• CRAWDAD is a valuable tool for researchers utilizing spatially resolved omics data.