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Facilitating the Analysis of Immunological Data with Visual Analytic Techniques

机译:利用视觉分析技术促进免疫数据的分析

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摘要

Visual analytics (VA) has emerged as a new way to analyze large dataset through interactive visual display. We demonstrated the utility and the flexibility of a VA approach in the analysis of biological datasets. Examples of these datasets in immunology include flow cytometry, Luminex data, and genotyping (e.g., single nucleotide polymorphism) data. Contrary to the traditional information visualization approach, VA restores the analysis power in the hands of analyst by allowing the analyst to engage in real-time data exploration process. We selected the VA software called Tableau after evaluating several VA tools. Two types of analysis tasks analysis within and between datasets were demonstrated in the video presentation using an approach called paired analysis. Paired analysis, as defined in VA, is an analysis approach in which a VA tool expert works side-by-side with a domain expert during the analysis. The domain expert is the one who understands the significance of the data, and asks the questions that the collected data might address. The tool expert then creates visualizations to help find patterns in the data that might answer these questions. The short lag-time between the hypothesis generation and the rapid visual display of the data is the main advantage of a VA approach.
机译:视觉分析(VA)已成为一种通过交互式视觉显示分析大型数据集的新方法。我们证明了VA方法在生物学数据集分析中的实用性和灵活性。免疫学中这些数据集的例子包括流式细胞术,Luminex数据和基因分型(例如单核苷酸多态性)数据。与传统的信息可视化方法相反,VA通过允许分析师参与实时数据探查过程来恢复分析师的分析能力。在评估了几种VA工具之后,我们选择了称为Tableau的VA软件。视频演示中使用称为配对分析的方法演示了数据集内部和数据集之间的两种类型的分析任务分析。 VA中定义的配对分析是一种分析方法,其中VA工具专家在分析过程中与领域专家并肩工作。领域专家是了解数据重要性的人员,并提出收集的数据可能解决的问题。然后,工具专家会创建可视化效果,以帮助查找可能回答这些问题的数据模式。假设生成和数据的快速视觉显示之间的短时滞是VA方法的主要优势。

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