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OnPLS-Based Multi-Block Data Integration: A Multivariate Approach to Interrogating Biological Interactions in Asthma.

Stacey N Reinke1,2, Beatriz Galindo-Prieto3, Tomas Skotare

  • 1Division of Physiological Chemistry 2, Department of Medical Biochemistry and Biophysics , Karolinska Institute , SE-171 77 Stockholm , Sweden.

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Summary

This study introduces a new method combining multiple-block orthogonal projections to latent structures (OnPLS) and multi-block variable influence on orthogonal projections (MB-VIOP) to analyze complex multiomics data. The approach reveals novel insights into asthma, including disease-sex interactions and potential mechanisms of treatment side effects.

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Area of Science:

  • Systems Biology
  • Bioinformatics
  • Genomics

Background:

  • Integrating multiomics data is crucial for systems biology but presents significant challenges.
  • Existing methods often require prior biological knowledge, limiting their scope.
  • Interpreting complex multiomics datasets requires advanced analytical tools.

Purpose of the Study:

  • To develop and apply an integrated analytical framework combining OnPLS and MB-VIOP for multiomics data analysis.
  • To identify joint structures and key variables within a multiomics asthma cohort study.
  • To generate novel hypotheses regarding asthma pathogenesis and treatment effects.

Main Methods:

  • Utilized multiple-block orthogonal projections to latent structures (OnPLS) to model six diverse data blocks (transcriptomics, metabolomics, targeted assays, clinical data).
  • Employed multi-block variable influence on orthogonal projections (MB-VIOP) to identify key variables contributing to the OnPLS model.
  • Integrated interactive visualization techniques, including chord plots, to explore variable interactions.

Main Results:

  • Identified seven components representing joint data structures, with two global and five local.
  • Discovered significant associations, including differentiating healthy controls from asthmatics and a disease-sex interaction.
  • Visualized feature interactions, revealing potential links between gene ATP6 V1G1, metabolites, and inhaled corticosteroid (ICS) treatment side effects.

Conclusions:

  • The combined OnPLS, MB-VIOP, and visualization approach effectively analyzes multiomics data without prior biological assumptions.
  • This framework can generate testable hypotheses for complex diseases like asthma.
  • The study highlights potential mechanisms for ICS-induced bone density loss in asthma patients.