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Composition-on-composition regression analysis for multi-omics integration of metagenomic data.

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We introduce a novel composition-on-composition (COC) regression method for analyzing high-dimensional compositional data, effectively handling zeroes without log-ratio transformations. This approach demonstrates superior performance in multi-omics studies.

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Area of Science:

  • Biostatistics
  • Bioinformatics
  • Computational Biology

Background:

  • Compositional data analysis is crucial in biomedical studies, particularly with next-generation sequencing.
  • Existing regression methods struggle with high-dimensional compositional data, especially when zeroes are present.
  • Log-ratio transformations, common in existing methods, fail to accommodate the zeroes often found in sequencing data.

Purpose of the Study:

  • To develop a robust regression method for analyzing associations between two high-dimensional compositional datasets.
  • To address the limitations of existing methods in handling compositional data with zeroes.
  • To provide a tool for multi-omics compositional data analysis.

Main Methods:

  • Propose a novel composition-on-composition (COC) regression analysis.
  • Utilize penalized estimation equations to handle high dimensionality.
  • Develop inference procedures for the proposed COC regression model.
  • Avoids log-ratio transformations, enabling direct analysis of compositional data including zeroes.

Main Results:

  • The proposed COC regression method effectively analyzes high-dimensional compositional data.
  • The method successfully handles datasets containing zeroes, a common issue in next-generation sequencing.
  • Demonstrated superior performance through extensive numerical simulations and real-world case studies.

Conclusions:

  • The composition-on-composition (COC) regression offers a powerful new tool for high-dimensional compositional data analysis.
  • COC regression overcomes key limitations of existing methods, particularly in handling zero values.
  • This method is well-suited for investigating associations in multi-omics compositional data from biomedical studies.