Studying a large number of variables measured on the same observations and organized in blocks – denoted multiblock data – is becoming standard in several domains especially in biology. To explore the relationships between all these variables – at the block- and the variable-level – several exploratory multiblock methods were proposed. However, most of them are only designed for numeric variables. In reality, some data sets contain variables of different measurement levels (i.e., numeric, nominal, ordinal). In this article, we focus on exploratory multiblock methods that handle variables at their appropriate measurement level. Multi-Block Principal Component Analysis with Optimal Scaling (MBPCA-OS) is proposed and applied to multiblock data from the CURIE-O-SA French cohort. In this study, variables are of different measurement levels and organized in four blocks. The objective is to study the immune responses according to the SARS-CoV-2 infection and vaccination statuses, the symptoms and the participant’s characteristics.

中文翻译：

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MBPCA-OS：针对不同测量级别的变量的探索性多块方法。应用于研究 SARS-CoV-2 感染和疫苗接种的免疫反应


研究在相同观察中测量并组织成块的大量变量（称为多块数据）正在成为多个领域的标准，尤其是在生物学领域。为了探索所有这些变量之间的关系（在块级别和变量级别），提出了几种探索性多块方法。然而，它们中的大多数仅针对数字变量而设计。实际上，一些数据集包含不同测量级别的变量（即数字、名义、序数）。在本文中，我们重点关注在适当的测量级别处理变量的探索性多块方法。提出了具有最佳缩放比例的多块主成分分析 (MBPCA-OS)，并将其应用于来自 CURIE-O-SA 法国队列的多块数据。在本研究中，变量具有不同的测量水平并分为四个块。目的是根据 SARS-CoV-2 感染和疫苗接种状态、症状和参与者的特征来研究免疫反应。