Visual cohort comparison for spatial single-cell omics-data

Antonios Somarakis, Marieke E. Ijsselsteijn, Sietse J. Luk, Boyd Kenkhuis, Noel F. C. C. de Miranda, B.P.F. Lelieveldt, T. Höllt

Research output: Contribution to journalArticleScientificpeer-review

1 Citation (Scopus)
2 Downloads (Pure)

Abstract

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.
Original languageEnglish
Article number9241732
Pages (from-to) 733 - 743
Number of pages11
JournalIEEE Transactions on Visualization and Computer Graphics
Volume27
Issue number2
DOIs
Publication statusPublished - 2021

Keywords

  • Imaging Mass Cytometry
  • Vectra
  • Visual analytics
  • Visual comparison
  • single-cell omics-data
  • spatially-resolved data

Fingerprint

Dive into the research topics of 'Visual cohort comparison for spatial single-cell omics-data'. Together they form a unique fingerprint.

Cite this