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Related Experiment Video

Updated: May 9, 2025

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
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A framework for predictive modeling of microbiome multi-omics data: latent interacting variable-effects (LIVE)

Javier Munoz Briones1,2, Douglas K Brubaker3,4,5

  • 1Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, USA.

BMC Bioinformatics
|April 29, 2025
PubMed
Summary
This summary is machine-generated.

Latent Interacting Variable Effects (LIVE) modeling integrates multi-omics data to identify key microbial and metabolic features associated with disease. This computational framework aids in interpreting complex host-microbiome interactions for better disease prediction and understanding.

Keywords:
GLMIBDLatent variablesMicrobiomeMultiomicssPLS-DA

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Area of Science:

  • Microbiome Research
  • Computational Biology
  • Systems Biology

Background:

  • The increasing volume of multi-omics host-microbiome data necessitates advanced computational tools for interpretation.
  • Integrating diverse omics datasets is crucial for understanding complex biological systems and disease mechanisms.
  • Latent Interacting Variable Effects (LIVE) modeling offers a novel framework for microbiome multi-omics data integration.

Purpose of the Study:

  • To present and validate the Latent Interacting Variable Effects (LIVE) modeling framework for microbiome multi-omics data integration.
  • To develop supervised and unsupervised versions of LIVE capable of incorporating covariate awareness.
  • To assess the performance of LIVE in predicting disease status using real-world microbiome and metabolomic datasets.

Main Methods:

  • Developed a supervised LIVE model using sparse Partial Least Squares Discriminant Analysis (sPLS-DA) latent variables.
  • Developed an unsupervised LIVE model using sparse Principal Component Analysis (sPCA) principal components.
  • Incorporated covariate awareness into both supervised and unsupervised LIVE models.
  • Applied and benchmarked LIVE on metagenomic and metabolomic data from Crohn's Disease and Ulcerative Colitis patients (PRISM and LLDeep cohorts).

Main Results:

  • LIVE demonstrated consistent and comparable performance against existing multi-omics integration methods on benchmarking datasets.
  • LIVE significantly reduced the complexity of feature interactions from millions to under 20,000 for Crohn's Disease and Ulcerative Colitis datasets.
  • The framework successfully conditioned the disease-predictive power of microbial and metabolic features on clinical variables.

Conclusions:

  • LIVE provides a distinct and complementary approach to existing microbiome multi-omics integration methods.
  • LIVE offers significant advantages in interpretable integration of multi-omics data with clinical variables for disease outcome prediction.
  • The framework facilitates the identification of microbiome-associated mechanisms underlying disease pathogenesis.