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timeOmics: an R package for longitudinal multi-omics data integration.

Antoine Bodein1, Marie-Pier Scott-Boyer1, Olivier Perin2

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Summary
This summary is machine-generated.

We developed timeOmics, an R package for integrating longitudinal multi-omics data. This tool helps uncover dynamic molecular patterns over time, offering new biological insights.

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Area of Science:

  • Computational Biology
  • Systems Biology
  • Bioinformatics

Background:

  • Multi-omics data integration advances biological system analysis and insight discovery.
  • Longitudinal multi-omics studies capture dynamic molecular relationships but lack robust analytical methods.

Purpose of the Study:

  • Introduce timeOmics, a novel R package for longitudinal multi-omics data integration.
  • Provide a comprehensive framework for pre-processing, modeling, and clustering time-series multi-omics data.
  • Identify molecular features exhibiting significant temporal associations.

Main Methods:

  • Developed a generic analytical framework within the R package timeOmics.
  • Implemented pre-processing, modeling, and clustering algorithms for longitudinal multi-omics data.
  • Applied the framework to a case study involving the integrative Human Microbiome Project data.

Main Results:

  • Successfully integrated longitudinal multi-omics data including mRNA, metabolites, gut taxa, and clinical variables.
  • Detected seasonal patterns in molecular and clinical data from diabetes mellitus patients.
  • Demonstrated the utility of timeOmics in uncovering dynamic biological relationships.

Conclusions:

  • timeOmics offers a powerful and versatile framework for analyzing longitudinal multi-omics data.
  • The package facilitates the identification of time-associated molecular features and dynamic biological patterns.
  • timeOmics is a valuable resource for researchers studying dynamic biological systems.