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bioRxiv · 10.1101/2023.12.22.571863

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

Blampey, Q.·Mulder, K.·Gardet, M.·Dutertre, C.-A.·Andre, F.·Ginhoux, F.·Cournede, P.-H.
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Abstract

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.

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Blampey, Q., Mulder, K., Gardet, M., Dutertre, C.-A., Andre, F., Ginhoux, F., Cournede, P.-H.. 2023-12-23. Sopa: a technology-invariant pipeline for analyses of image-based spatial-omics. https://doi.org/10.1101/2023.12.22.571863

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