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For multivariate data analysis involving only two input matrices, thepreviously 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 dataand (iii) interpretation enhancement of PLS, OPLS and O2PLS models. Formultiblock analysis, the OnPLS models find relationships among multiple datamatrices by calculating latent variables; however, a method for improving theinterpretation of these latent variables by assessing the importance of theinput variables was not available up to now.  A method for variable selection in multiblock analysis, called multiblockvariable influence on orthogonal projections (MB-VIOP) is explained in thispaper. MB-VIOP is a model based variable selection method that uses the datamatrices, the scores and the normalized loadings of an OnPLS model in order tosort the input variables of more than two data matrices according to theirimportance for both simplification and interpretation of the total multiblockmodel, 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-VIOPconnects the input variables of different data matrices according to theirrelevance for the interpretation of each latent variable, yielding enhancedinterpretability for each OnPLS model component. Besides, MB-VIOP can deal withstrong overlapping of types of variation, as well as with many data blocks withvery different dimensionality. The ability of MB-VIOP for generatingdimensionality reduced models with high interpretability makes this methodideal for big data mining, multi-omics data integration and any study thatrequires exploration and interpretation of large streams of data.

Multiblock variable influence on orthogonal projections (MB-VIOP) for  enhanced interpretation of total, global, local and unique variations in  OnPLS models