Sopa: a technology-invariant pipeline for analyses of image-based spatial omics

Spatial omics data allow in-depth analysis of tissue architectures, opening new opportunities for biological discovery. In particular, imaging techniques offer single-cell resolutions, providing essential insights into cellular organizations and dynamics. Yet, the complexity of such data presents an...

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Bibliographic Details
Published in:Nature communications 2024-06, Vol.15 (1), p.4981-12
Main Authors: Blampey, Quentin, Mulder, Kevin, Gardet, Margaux, Christodoulidis, Stergios, Dutertre, Charles-Antoine, André, Fabrice, Ginhoux, Florent, Cournède, Paul-Henry
Format: Article
Language:English
Subjects:
631/114/1314
631/114/2397
631/114/794
Adaptation
Animals
Annotations
Complexity
Computation
Computational Biology - methods
Data analysis
Datasets
Humanities and Social Sciences
Humans
Image Processing, Computer-Assisted - methods
Imaging techniques
Invariants
multidisciplinary
Science
Science (multidisciplinary)
Single-Cell Analysis - methods
Software
Spatial analysis
Spatial data
Technology
Tissue analysis
Transcriptome
Transcriptomics
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Summary:Spatial omics data allow in-depth analysis of tissue architectures, opening new opportunities for biological discovery. In particular, imaging techniques offer single-cell resolutions, providing essential insights into cellular organizations and dynamics. Yet, the complexity of such data presents analytical challenges and demands substantial computing resources. Moreover, the proliferation of diverse spatial omics technologies, such as Xenium, MERSCOPE, CosMX in spatial-transcriptomics, and MACSima and PhenoCycler in multiplex imaging, hinders the generality of existing tools. We introduce Sopa ( https://github.com/gustaveroussy/sopa ), a technology-invariant, memory-efficient pipeline with a unified visualizer for all image-based spatial omics. Built upon the universal SpatialData framework, Sopa optimizes tasks like segmentation, transcript/channel aggregation, annotation, and geometric/spatial analysis. Its output includes user-friendly web reports and visualizer files, as well as comprehensive data files for in-depth analysis. Overall, Sopa represents a significant step toward unifying spatial data analysis, enabling a more comprehensive understanding of cellular interactions and tissue organization in biological systems. The complexity of spatial omics data presents analytical challenges and demands substantial computing resources. Here, the authors introduce Sopa, a technology-invariant, memory-efficient pipeline with a unified visualizer for all image-based spatial omics.
ISSN:2041-1723
2041-1723
DOI:10.1038/s41467-024-48981-z