CRAWDAD: An R Package for Analyzing Cell-Type Spatial Relationships
Author Information
Author(s): dos Santos Peixoto Rafael, Miller Brendan F., Brusko Maigan A., Aihara Gohta, Atta Lyla, Anant Manjari, Atkinson Mark A., Brusko Todd M., Wasserfall Clive H., Fan Jean
Primary Institution: Johns Hopkins University
Hypothesis
CRAWDAD can quantify cell-type spatial relationships across different length scales in spatially resolved omics data.
Conclusion
CRAWDAD effectively characterizes and compares cell-type spatial relationships across various tissues and conditions.
Supporting Evidence
- CRAWDAD was validated using simulated and real spatial omics datasets.
- CRAWDAD identified significant spatial relationships in the mouse cerebellum and developing embryo.
- CRAWDAD's results were distinct from other spatial analysis methods.
Takeaway
CRAWDAD is a tool that helps scientists understand how different types of cells are arranged in tissues, which can tell us a lot about how those tissues work.
Methodology
CRAWDAD uses a binomial testing framework to evaluate the statistical significance of cell-type spatial relationships based on cell positions and annotations.
Potential Biases
Potential misannotation of cell types could influence the results.
Limitations
Results depend on user-defined parameters and the quality of cell-type annotations.
Statistical Information
P-Value
0.05
Statistical Significance
p<0.05
Digital Object Identifier (DOI)
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