Visual cohort comparison for spatial single-cell omics-data

Antonios Somarakis, Marieke Ijsselsteijn, Sietse Luk, Boyd Kenkhuis, Noel de Miranda, Boudewijn Lelieveldt, Thomas Höllt

View presentation: 2020-10-28T16:15:00Z GMT-0600 Change your timezone on the schedule page
2020-10-28T16:15:00Z
Exemplar figure
Screenshot of our integrated system including the view for the comparison based on the cell abundance using raincloud plots, the tissue view, showing selected samples of the two cohorts, and the multi-cellular microenvironment comparison view using a difference heatmap and raincloud plots.
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Direct link to video on YouTube: https://youtu.be/xGFBiyBkm38

Keywords

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

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.