Visual Cohort Comparison for Spatial Single-Cell Omics-Data

Antonios Somarakis, Marieke E Ijsselsteijn, Sietse J Luk, Boyd Kenkhuis, Noel de Miranda, Boudewijn P.F. Lelieveldt, Thomas Höllt
IEEE Transactions on Visualization and Computer Graphics (Proceedings of VAST 2020), Volume 27, Number 1 - 2021
Download the publication : 2020_vis_cohort.pdf [16.5Mo]  
Spatially-resolved omics-data enable researchers to precisely distinguish cell types in tissue and explore their spatial interactions, enabling deep understanding of tissue functionality. To understand what causes or deteriorates a disease and identify related biomarkers, clinical researchers regularly perform large-scale cohort studies, requiring the comparison of such data at cellular level. In such studies, with little a-priori knowledge of what to expect in the data, explorative data analysis is a necessity. Here, we present an interactive visual analysis workflow for the comparison of cohorts of spatially-resolved omics-data. Our workflow allows the comparative analysis of two cohorts based on multiple levels-of-detail, from simple abundance of contained cell types over complex co-localization patterns to individual comparison of complete tissue images. As a result, the workflow enables the identification of cohort-differentiating features, as well as outlier samples at any stage of the workflow. During the development of the workflow, we continuously consulted with domain experts. To show the effectiveness of the workflow we conducted multiple case studies with domain experts from different application areas and with different data modalities.

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BibTex references

@Article { SILKMLH21,
  author       = "Somarakis, Antonios and Ijsselsteijn, Marieke E and Luk, Sietse J and Kenkhuis, Boyd and Miranda, Noel de and
                  Lelieveldt, Boudewijn P.F. and H\öllt, Thomas",
  title        = "Visual Cohort Comparison for Spatial Single-Cell Omics-Data",
  journal      = "IEEE Transactions on Visualization and Computer Graphics (Proceedings of VAST 2020)",
  number       = "1",
  volume       = "27",
  year         = "2021",
  doi          = "10.1109/TVCG.2020.3030336",
  url          = ""

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