多块变量对正交投影的影响（MB-VIOP）：增强OnPLS模型中总体、全局、局部和独特变异的解释
Multiblock variable influence on orthogonal projections (MB-VIOP) for enhanced interpretation of total, global, local and unique variations in OnPLS models
AI总结：
本文提出MB-VIOP方法，用于多块OnPLS模型中评估变量重要性，增强总体、全局、局部和独特变异的解释，支持降维和高可解释性，适用于大数据和多组学整合。
摘要：
对于仅涉及两个输入矩阵的多元数据分析，先前发表的变量投影影响方法（如VIPOPLS或VIPO2PLS）被广泛用于变量选择目的，包括（i）变量重要性评估，（ii）大数据降维，以及（iii）增强PLS、OPLS和O2PLS模型的解释。对于多块分析，OnPLS模型通过计算潜变量来发现多个数据矩阵之间的关系；然而，到目前为止，还没有一种通过评估输入变量的重要性来改进这些潜变量解释的方法。本文解释了一种用于多块分析中变量选择的方法，称为多块变量对正交投影的影响（MB-VIOP）。MB-VIOP是一种基于模型的变量选择方法，它利用OnPLS模型的数据矩阵、得分和归一化载荷，根据输入变量对总体多块模型以及独特、局部和全局模型组件的简化和解释的重要性，对两个以上数据矩阵的输入变量进行排序。MB-VIOP已使用三个多块数据集进行了测试。MB-VIOP评估任何类型数据中的变量重要性。MB-VIOP根据输入变量对每个潜变量解释的相关性，连接不同数据矩阵的输入变量，为每个OnPLS模型组件提供增强的可解释性。此外，MB-VIOP可以处理变异类型的强重叠，以及具有非常不同维度的多个数据块。MB-VIOP生成具有高可解释性的降维模型的能力，使该方法非常适合大数据挖掘、多组学数据整合以及任何需要探索和解释大量数据流的研究。
英文摘要：
For multivariate data analysis involving only two input matrices, the previously published methods for variable influence on projection (e.g., VIPOPLS or VIPO2PLS) are widely used for variable selection purposes, including (i) variable importance assessment, (ii) dimensionality reduction of big data and (iii) interpretation enhancement of PLS, OPLS and O2PLS models. For multiblock analysis, the OnPLS models find relationships among multiple data matrices by calculating latent variables; however, a method for improving the interpretation of these latent variables by assessing the importance of the input variables was not available up to now. A method for variable selection in multiblock analysis, called multiblock variable influence on orthogonal projections (MB-VIOP) is explained in this paper. MB-VIOP is a model based variable selection method that uses the data matrices, the scores and the normalized loadings of an OnPLS model in order to sort the input variables of more than two data matrices according to their importance for both simplification and interpretation of the total multiblock model, and also of the unique, local and global model components separately. MB-VIOP has been tested using three multiblock datasets. MB-VIOP assesses the variable importance in any type of data. MB-VIOP connects the input variables of different data matrices according to their relevance for the interpretation of each latent variable, yielding enhanced interpretability for each OnPLS model component. Besides, MB-VIOP can deal with strong overlapping of types of variation, as well as with many data blocks with very different dimensionality. The ability of MB-VIOP for generating dimensionality reduced models with high interpretability makes this method ideal for big data mining, multi-omics data integration and any study that requires exploration and interpretation of large streams of data.
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。
