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Analyzing associations and higher-order effects in multi-omics data with double machine learning.

Julian Hecker1, Dmitry Prokopenko2, Georg Hahn3,4

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|December 22, 2025
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Summary
This summary is machine-generated.

This study introduces the Robust Omics MethodologY (ROMY) framework for integrative omics analysis. ROMY provides robust methods for association testing, variance analysis, and interaction effects in complex biological data.

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Area of Science:

  • Biostatistics
  • Bioinformatics
  • Computational Biology

Background:

  • Integrative omics analyses are crucial for understanding disease mechanisms and identifying biomarkers.
  • These analyses face statistical challenges due to high dimensionality, non-standard data, and complex confounding effects.

Purpose of the Study:

  • To introduce the Robust Omics MethodologY (ROMY) framework and its R package (romy) for advanced multi-omics data integration and analysis.
  • To provide robust and flexible methodologies for association testing, variance analysis, and interaction effects in omics data.

Main Methods:

  • Development of the Robust Omics MethodologY (ROMY) framework.
  • Implementation of ROMY in an R package named 'romy'.
  • Utilizing theoretical statistics and double machine learning for robustness and statistical validity.

Main Results:

  • The ROMY framework enables robust association testing with flexible covariate adjustments.
  • It allows for the examination of effects on measurement variances and covariances (e.g., co-expression, co-abundance).
  • ROMY facilitates rigorous interaction-effect testing.

Conclusions:

  • The ROMY framework offers a robust solution for complex integrative omics analyses.
  • The 'romy' R package provides accessible tools for researchers to apply these advanced statistical methods.
  • This methodology enhances the statistical validity and flexibility of multi-omics data interpretation.