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ACMTF-R: Supervised multi-omics data integration uncovering shared and distinct outcome-associated variation.

Geert Roelof van der Ploeg1, Fred T G White1, Rasmus Riemer Jakobsen2

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Summary

We developed ACMTF-R, a new data fusion method for multi-omics data. It identifies shared and distinct biological patterns linked to specific outcomes, outperforming existing techniques in simulations and a real-world study.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Data Science

Background:

  • High-dimensional biological data requires advanced fusion techniques.
  • Current methods like ACMTF lack outcome association, while NPLS cannot identify shared structures.

Purpose of the Study:

  • Introduce ACMTF-R, a novel method combining data exploration and prediction for multi-way data fusion.
  • Simultaneously decompose multi-way data and capture outcome-associated variation.

Main Methods:

  • Developed ACMTF-R, an extension of ACMTF with an integrated regression step.
  • Formulated the mathematical basis, optimization algorithm, and implementation.
  • Evaluated performance via simulations and a real-world multi-omics dataset.

Main Results:

  • ACMTF-R robustly identifies outcome-associated shared and distinct variation.
  • Successfully recovered a small outcome-related component in simulations, outperforming NPLS and ACMTF.
  • Identified novel mother-infant relationships associated with maternal BMI in a real-world dataset.

Conclusions:

  • ACMTF-R is a versatile tool for multi-way data fusion in multi-omics research.
  • Effectively integrates common, local, and distinct variation in the context of a dependent variable.
  • Offers new insights into complex biological systems and inter-generational effects.