 A method for variable selection in multiblock analysis, called multiblock variable influence on orthogonal projections ( i.e., Multiblock-VIOP or MB-VIOP) is explained in this paper. Multiblock-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 a large number of data matrices according to their importance for both simplification and interpretation of the total OnPLS model, and also of the unique, local and global model components separately. The previously published OnPLS algorithm finds the relationships among multiple datamatrices (blocks) by calculating latent variables ; however, a method for improving the interpretation of these latent variables by assessing the importance of the input variables was missing. In this paper, we provide evidence for the usefulness, the efficiency and the reliability of MB-VIOP  for the abovementioned purposes by means of three examples, (i) a synthetic four-block dataset, (ii) a real three-block omics dataset related to plant sciences, and (iii) a real six-block dataset related to the food industry .
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 .
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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.
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i.e., Multiblock-VIOP or
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Multiblock-VIOP
MB-VIOP
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a large number of
more than two
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OnPLS
multiblock
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The previously published OnPLS algorithm finds the relationships among multiple datamatrices (blocks) by calculating latent variables ; however, a method for improving
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
R
these latent variables by assessing the importance of the input variables was missing. In this paper, we provide evidence for the usefulness, the efficiency and the reliability of
each latent variable, yielding enhanced interpretability for each OnPLS model component. Besides,
A
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
R
the abovementioned purposes by means of three examples, (i) a synthetic four-block dataset, (ii) a real three-block omics dataset related to plant sciences, and (iii) a real six-block dataset related to the food industry
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