Understanding and making the most of complex data collected from multiple sources is a challenging task. Data integration is the procedure of describing the main features in multiple data blocks, and several methods for multiblock analysis have been previously developed, including OnPLS and JIVE. One of the main challenges is how to visualize and interpret the results of multiblock analyses because of the increased model complexity and sheer size of data. In this paper, we present novel visualization tools that simplify interpretation and overview of multiblock analysis. We introduce a correlation matrix plot that provides an overview of the relationships between blocks found by multiblock models. We also present a multiblock scatter plot, a metadata correlation plot, and a variation distribution plot, that simplify the interpretation of multiblock models. We demonstrate our visualizations on an industrial case study in vibration spectroscopy (NIR, UV, and Raman datasets) as well as a multiomics integration study (transcript, metabolite, and protein datasets). We conclude that our visualizations provide useful tools to harness the complexity of multiblock analysis and enable better understanding of the investigated system.

中文翻译：

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描述性多块分析的可视化

理解并充分利用从多个来源收集的复杂数据是一项具有挑战性的任务。数据集成是在多个数据块中描述主要特征的过程，之前已经开发了多种多块分析方法，包括 OnPLS 和 JIVE。主要挑战之一是如何可视化和解释多块分析的结果，因为模型复杂性增加且数据量庞大。在本文中，我们提出了新颖的可视化工具，可简化多块分析的解释和概述。我们引入了一个相关矩阵图，它提供了多块模型发现的块之间关系的概述。我们还提供了多块散点图、元数据相关图和变异分布图，简化多块模型的解释。我们在振动光谱（NIR、UV 和拉曼数据集）以及多组学集成研究（转录本、代谢物和蛋白质数据集）的工业案例研究中展示了我们的可视化。我们得出的结论是，我们的可视化提供了有用的工具来利用多块分析的复杂性并更好地理解所研究的系统。