ArticleNature communications2024
Sopa: a technology-invariant pipeline for analyses of image-based spatial omics.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
32 citing papers in PubMed.
- Single-cell spatial immune landscape of ASFV-infected porcine lung microenvironment.Virulence · 2026Article
- Human adult and pediatric multimodal transcriptomic atlas of progression from pressure-loaded to failing right ventricle.Nature cardiovascular research · 2026Article
- A human single-cell atlas identifies OLR1Nature genetics · 2026Article
- STAT3D as a new image-tailored Xenium-based spatial transcriptomics tool for accurately dissecting the tumor microenvironment.Nucleic acids research · 2026Article
- Agentic AI for Spatial Omics.Immunology and cell biology · 2026Review
- Spatialproteomics: an interoperable toolbox for analyzing highly multiplexed fluorescence image data.Nature methods · 2026Article
- Single-cell multi-omic analyses resolve the cellular diversity of ALK/ROS1/MET/NTRK-fused gliomas in infants and older children.bioRxiv : the preprint server for biology · 2026Article
- Heterogeneous viral-host response at single-cell resolution and viral adaptive mutational atlas in birds and mammals upon H5 influenza virus infection.National science review · 2026Article
- Spatial Transcriptomics Recontextualizes the Cellular Environment of Conjunctival Melanoma.medRxiv : the preprint server for health sciences · 2026Article
- Review
- Review
- mosna Reveals Different Types of Cellular Interactions Predictive of Response to Immunotherapies and Survival in Cancer.Molecular & cellular proteomics : MCP · 2026Article
- Charting the transition from in vitro gliogenesis to the in vivo maturation of human glial progenitor cells transplanted into the hypomyelinated mouse brain.Nature communications · 2026Article
- LazySlide: accessible and interoperable whole-slide image analysis.Nature methods · 2026Article
- STHELAR, a multi-tissue dataset linking spatial transcriptomics and histology for cell type annotation.Scientific data · 2026Article
- SCALPEL: A pipeline for processing large-scale spatial transcriptomics data.bioRxiv : the preprint server for biology · 2026Article
- Spatial transcriptomics identifies differentiation, lipid metabolism, and retinoid pathway alterations in acne vulgaris.JCI insight · 2026Article
- RNA2seg: a generalist model for cell segmentation in image-based spatial transcriptomics.Genome biology · 2026Article
- Positioning and reversible suppression of CCR7Immunity · 2026Article
- Spatial omics for profiling the dynamic tumor microenvironment.Clinical & translational immunology · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.