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

Galindo-Prieto, Beatriz and Geladi, Paul and Trygg, Johan (2021). Multiblock variable influence on orthogonal projections (MB-VIOP) for enhanced interpretation of total, global, local and unique variations in OnPLS models. BMC Bioinformatics. 22 , 176
[Research article]

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Abstract

Background For multivariate data analysis involving only two input matrices (e.g., X and Y), 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 (more than two blocks) by calculating latent variables; however, a method for improving the interpretation of these latent variables (model components) by assessing the importance of the input variables was not available up to now. Results 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 datasets: a synthetic four-block dataset, a real three-block omics dataset related to plant sciences, and a real six-block dataset related to the food industry. Conclusions We provide evidence for the usefulness and reliability of MB-VIOP by means of three examples (one synthetic and two real-world cases). MB-VIOP assesses in a trustable and efficient way the importance of both isolated and ranges of variables 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.

Authors/Creators:Galindo-Prieto, Beatriz and Geladi, Paul and Trygg, Johan
Title:Multiblock variable influence on orthogonal projections (MB-VIOP) for enhanced interpretation of total, global, local and unique variations in OnPLS models
Series Name/Journal:BMC Bioinformatics
Year of publishing :2021
Volume:22
Article number:176
Number of Pages:27
Publisher:BMC
ISSN:1471-2105
Language:English
Publication Type:Research article
Article category:Scientific peer reviewed
Version:Published version
Copyright:Creative Commons: Attribution 4.0
Full Text Status:Public
Subjects:(A) Swedish standard research categories 2011 > 1 Natural sciences > 102 Computer and Information Science > 10203 Bioinformatics (Computational Biology) (applications to be 10610)
Keywords:Multiblock variable selection, OnPLS, VIP, MB-VIOP, Variable importance in multiblock regression, Latent variable interpretation, Variable influence on projection, Feature selection
URN:NBN:urn:nbn:se:slu:epsilon-p-111644
Permanent URL:
http://urn.kb.se/resolve?urn=urn:nbn:se:slu:epsilon-p-111644
Additional ID:
Type of IDID
DOI10.1186/s12859-021-04015-9
Web of Science (WoS)000636383900001
ID Code:23380
Faculty:S - Faculty of Forest Sciences
Department:(S) > Department of Forest Biomaterials and Technology
Deposited By: SLUpub Connector
Deposited On:29 Apr 2021 12:23
Metadata Last Modified:29 Apr 2021 12:33

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