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|January 31, 2024
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Multivariate pattern analysis (MVPA) offers a solution for analyzing complex metabolomics data with multicollinear covariates. This new R package and shiny app improve model interpretability and resolution without data loss.

Keywords:
Covariate projectionLatent variable projectionMulticollinear covariatesMultivariate pattern analysisNet association patternsTarget projection

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Area of Science:

  • Statistical modeling
  • Bioinformatics
  • Metabolomics

Background:

  • Multicollinear covariates in metabolomics pose challenges for traditional multivariate regression.
  • Existing methods for handling multicollinearity often reduce data resolution and model interpretability.
  • A need exists for methods that can effectively analyze complex, high-dimensional data without compromising analytical quality.

Purpose of the Study:

  • To implement and demonstrate Multivariate Pattern Analysis (MVPA) for handling multicollinear covariates in regression.
  • To develop an open-source R package and a user-friendly Shiny application for MVPA.
  • To enhance the interpretative potential of models dealing with complex multivariate data structures.

Main Methods:

  • MVPA employs a general projection algorithm designed to manage multicollinear and linearly dependent covariates.
  • The method separates data variance into orthogonal components, distinguishing covariate relationships from net predictive associations.
  • A three-step process involving partial least squares regression, Monte Carlo resampling, and target projection is used for covariate adjustment.

Main Results:

  • The MVPA method has been implemented in an open-source R package (mvpa) and an integrated R Shiny app (mvpaShiny).
  • MVPA effectively handles multicollinear covariates, preserving data resolution and improving model interpretability through variance plots.
  • The approach was illustrated by analyzing the mediation of a metabolomics descriptor on insulin resistance and lifestyle factors.

Conclusions:

  • The developed MVPA method and its R package implementation offer advanced analytical and visualization capabilities for complex multivariate data.
  • The open-source availability of the R packages facilitates broader adoption and application in scientific research.
  • This work provides a valuable tool for researchers working with high-dimensional and multicollinear datasets, particularly in metabolomics.