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Peer-reviewedCC BY 4.0PLOS Computational Biology

Analysis of multicellular anatomical structures from spatial omics data using sosta

  • Samuel Gunz1
  • Helena L. Crowell2
  • Mark D. Robinson
  1. 1Department of Molecular Life Sciences and SIB Swiss Institute of Bioinformatics, University of Zurich, Zurich, Switzerland
  2. 2Centro Nacional de Análisis Genómico, Barcelona, Spain
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Abstract

Spatial omics technologies enable high-resolution, large-scale quantification of molecular features while preserving the spatial context within tissues. Existing analysis methods largely focus on spatial arrangements of single cells, whereas biological function often emerges from multicellular arrangements. Here, we introduce structure-based analysis of spatial omics data, which focuses on the direct analysis of multicellular, anatomical structures. We illustrate this type of analysis using two publicly available datasets and provide sosta, an open-source Bioconductor package for broad community use.

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Cite

BibTeXRIS
BibTeX
@article{gunz2026analysis,
  title = {{Analysis of multicellular anatomical structures from spatial omics data using sosta}},
  author = {Samuel Gunz and Helena L. Crowell and Mark D. Robinson},
  journal = {PLOS Computational Biology},
  year = {2026},
  month = sep,
  doi = {10.1371/journal.pcbi.1014551},
  url = {https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1014551},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Analysis of multicellular anatomical structures from spatial omics data using sosta
AU  - Samuel Gunz
AU  - Helena L. Crowell
AU  - Mark D. Robinson
PY  - 2026
DA  - 2026/09/10
JO  - PLOS Computational Biology
DO  - 10.1371/journal.pcbi.1014551
UR  - https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1014551
AB  - Spatial omics technologies enable high-resolution, large-scale quantification of molecular features while preserving the spatial context within tissues. Existing analysis methods largely focus on spatial arrangements of single cells, whereas biological function often emerges from multicellular arrangements. Here, we introduce structure-based analysis of spatial omics data, which focuses on the direct analysis of multicellular, anatomical structures. We illustrate this type of analysis using two publicly available datasets and provide sosta, an open-source Bioconductor package for broad community use.
SN  - 1553-7358
ER  -