Visual cohort comparison for spatial single-cell omics-data
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by
Antonios Somarakis, Marieke E. Ijsselsteijn, Sietse J. Luk, Boyd Kenkhuis, Noel F.C.C. de Miranda, Boudewijn P.F. Lelieveldt, Thomas Höllt
2020
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.
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