
==== Front
bioRxiv
BIORXIV
bioRxiv
2692-8205
Cold Spring Harbor Laboratory

10.1101/2023.10.05.560733
preprint
2
Article
Characterizing cell-type spatial relationships across length scales in spatially resolved omics data
dos Santos Peixoto Rafael http://orcid.org/0000-0002-7184-2868

Miller Brendan F. http://orcid.org/0000-0002-9559-4045

Brusko Maigan A.
Aihara Gohta http://orcid.org/0000-0002-2492-9610

Atta Lyla http://orcid.org/0000-0002-6113-0082

Anant Manjari http://orcid.org/0000-0002-6600-3410

Atkinson Mark A. http://orcid.org/0000-0001-8489-4782

Brusko Todd M. http://orcid.org/0000-0003-2878-9296

Wasserfall Clive H. http://orcid.org/0000-0002-3522-8932

Fan Jean http://orcid.org/0000-0002-0212-5451

14 9 2024
2023.10.05.560733https://creativecommons.org/licenses/by-nc-nd/4.0/ This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which allows reusers to copy and distribute the material in any medium or format in unadapted form only, for noncommercial purposes only, and only so long as attribution is given to the creator.
http://biorxiv.org/lookup/doi/10.1101/2023.10.05.560733
nihpp-2023.10.05.560733.pdf
Abstract

Spatially resolved omics (SRO) technologies enable the identification of cell types while preserving their organization within tissues. Application of such technologies offers the opportunity to delineate cell-type spatial relationships, particularly across different length scales, and enhance our understanding of tissue organization and function. To quantify such multi-scale cell-type spatial relationships, we developed CRAWDAD, Cell-type Relationship Analysis Workflow Done Across Distances, as an open-source R package with source code and additional documentation at https://jef.works/CRAWDAD/ .

To demonstrate the utility of such multi-scale characterization, recapitulate expected cell-type spatial relationships, and evaluate against other cell-type spatial analyses, we applied CRAWDAD to various simulated and real SRO datasets of diverse tissues assayed by diverse SRO technologies. We further demonstrate how such multi-scale characterization enabled by CRAWDAD can be used to compare cell-type spatial relationships across multiple samples. Finally, we applied CRAWDAD to SRO datasets of the human spleen to identify consistent as well as patient and sample-specific cell-type spatial relationships. In general, we anticipate such multi-scale analysis of SRO data enabled by CRAWDAD will provide useful quantitative metrics to facilitate the identification, characterization, and comparison of cell-type spatial relationships across axes of interest.
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