Somarakis Antonios, Ijsselsteijn Marieke E, Luk Sietse J, Kenkhuis Boyd, de Miranda Noel F C C, Lelieveldt Boudewijn P F, Hollt Thomas
IEEE Trans Vis Comput Graph. 2021 Feb;27(2):733-743. doi: 10.1109/TVCG.2020.3030336. Epub 2021 Jan 28.
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.
空间分辨组学数据使研究人员能够精确区分组织中的细胞类型，并探索它们的空间相互作用，从而深入了解组织功能。为了了解疾病的病因或恶化因素并识别相关生物标志物，临床研究人员经常进行大规模队列研究，这需要在细胞水平上比较此类数据。在此类研究中，由于对数据中预期结果的先验知识很少，探索性数据分析是必要的。在这里，我们提出了一种用于比较空间分辨组学数据队列的交互式视觉分析工作流程。我们的工作流程允许基于多个细节级别对两个队列进行比较分析，从所含细胞类型的简单丰度到复杂的共定位模式，再到完整组织图像的个体比较。结果，该工作流程能够识别队列区分特征以及工作流程任何阶段的异常样本。在工作流程的开发过程中，我们不断与领域专家进行咨询。为了展示该工作流程的有效性，我们与来自不同应用领域和不同数据模式的领域专家进行了多个案例研究。
