Pheno-Mapper：一个用于表型组学数据可视化探索的交互式平台
Pheno-Mapper: An Interactive Toolbox for the Visual Exploration of Phenomics Data
摘要：
收集现场数据的高通量技术使得在生命科学的几个分支进行大规模观测成为可能。收集的数据可以从分子水平（基因型）到生理（表型性状）和环境观察（例如，天气、土壤条件）。这些大量的数据，统称为表型组学数据，代表了关于潜在生物系统动态的关键科学知识的宝库。然而，从这些复杂的数据集中提取信息和见解仍然是一个重大挑战，因为它们是多维的，而且缺乏关于其复杂结构的先验知识。在本文中，我们提出了Pheno-Mapper，一个交互式工具箱，用于探索性分析和可视化大规模表型组学数据。我们的方法使用映射器框架来执行数据的拓扑分析，并随后使用内置的数据分析和机器学习功能来呈现视觉表示。我们展示了这种新工具在现实世界的工厂（例如，玉米）表型组学数据集。与现有方法相比，Pheno-Mapper的主要优势在于它在表型组学数据的探索性分析中提供了丰富的交互式功能，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允许通过数据的拓扑摘要指导交互式选择亚群，并将数据采掘和机器学习应用于这些选定的亚群进行深入探索。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
