Pheno-mapper: an interactive toolbox for the visual exploration of phenomics data
Pheno-mapper: an interactive toolbox for the visual exploration of phenomics data
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DOI：
10.1145/3459930.3469511
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发表时间：
2021-06
期刊：
影响因子：
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通讯作者：
Youjia Zhou;M. Kamruzzaman;P. Schnable;Bala Krishnamoorthy;A. Kalyanaraman;Bei Wang
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文献类型：
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作者：
Youjia Zhou;M. Kamruzzaman;P. Schnable;Bala Krishnamoorthy;A. Kalyanaraman;Bei Wang
收集现场数据的高通量技术使得生命科学多个分支的大规模观测成为可能。收集的数据范围从分子水平（基因型）到生理（表型性状）和环境观察（例如天气、土壤条件）。这些大量数据统称为表型组学数据，代表了有关基础生物系统动力学的关键科学知识的宝库。然而，由于这些复杂数据集的多维性以及缺乏对其复杂结构的先验知识，从这些复杂数据集中提取信息和见解仍然是一个重大挑战。在本文中，我们提出了 Pheno-Mapper，这是一个用于大规模表型组数据探索性分析和可视化的交互式工具箱。我们的方法使用映射器框架对数据进行拓扑分析，然后使用内置数据分析和机器学习功能呈现视觉表示。我们展示了这种新工具在现实世界植物（例如玉米）表型组数据集上的实用性。与现有方法相比，Pheno-Mapper 的主要优势在于它在表型组数据的探索性分析中提供了丰富的交互功能，并且以易于扩展的方式将可视化分析与数据分析和机器学习集成在一起。特别是，Pheno-Mapper 允许在数据的拓扑摘要的指导下交互式选择子群，并将数据挖掘和机器学习应用于这些选定的子群以进行深入探索。
High-throughput technologies to collect field data have made observations possible at scale in several branches of life sciences. The data collected can range from the molecular level (genotypes) to physiological (phenotypic traits) and environmental observations (e.g., weather, soil conditions). These vast swathes of data, collectively referred to as phenomics data, represent a treasure trove of key scientific knowledge on the dynamics of the underlying biological system. However, extracting information and insights from these complex datasets remains a significant challenge owing to their multidimensionality and lack of prior knowledge about their complex structure. In this paper, we present Pheno-Mapper, an interactive toolbox for the exploratory analysis and visualization of large-scale phenomics data. Our approach uses the mapper framework to perform a topological analysis of the data, and subsequently render visual representations with built-in data analysis and machine learning capabilities. We demonstrate the utility of this new tool on real-world plant (e.g., maize) phenomics datasets. In comparison to existing approaches, the main advantage of Pheno-Mapper is that it provides rich, interactive capabilities in the exploratory analysis of phenomics data, and it integrates visual analytics with data analysis and machine learning in an easily extensible way. In particular, Pheno-Mapper allows the interactive selection of subpopulations guided by a topological summary of the data and applies data mining and machine learning to these selected subpopulations for in-depth exploration.