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Tick Microbiome Characterization by Next-Generation 16S rRNA Amplicon Sequencing
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Extracting host-specific developmental signatures from longitudinal microbiome data.

Balázs Erdős1, Christos Chatzis1,2, Jonathan Thorsen3,4

  • 1Department of Data Science and Knowledge Discovery, Simula Metropolitan Center for Digital Engineering, Oslo, Norway.

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Summary

This study introduces PARAFAC2 for analyzing longitudinal microbiome data, enabling the capture of subject-specific temporal patterns often missed by traditional methods like CP decomposition. This approach enhances understanding of microbial dynamics in relation to host health.

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Area of Science:

  • Microbiome Research
  • Computational Biology
  • Data Science

Background:

  • Longitudinal microbiome studies are crucial for understanding host health dynamics.
  • Tensor decomposition methods, like CANDECOMP/PARAFAC (CP), offer unsupervised analysis of temporal patterns.
  • Existing CP models assume uniform temporal dynamics, failing to capture individual subject variations.

Purpose of the Study:

  • To develop a novel analytical framework for longitudinal microbiome data that accounts for subject-specific temporal variations.
  • To overcome the limitations of CP models in capturing individual trajectories, shifts, and delays.
  • To introduce replicability as a criterion for robust model component selection.

Main Methods:

  • Introduction of a novel analytical framework based on the PARAFAC2 tensor decomposition model.
  • Explicit modeling of subject-specific variations in temporal patterns.
  • Systematic comparisons using simulated and real-world datasets (infant gut maturation, dietary interventions).

Main Results:

  • PARAFAC2 effectively captures subject-specific temporal trajectories, outperforming the conventional CP model.
  • The new framework identifies biologically relevant microbial patterns missed by CP.
  • Demonstrated improved analysis of infant gut microbiome maturation and dietary intervention effects.

Conclusions:

  • PARAFAC2 provides a more accurate and nuanced analysis of longitudinal microbiome data compared to CP.
  • The framework allows for the discovery of previously overlooked subject-specific microbial dynamics.
  • Replicability serves as a reliable metric for selecting the optimal number of components in tensor decomposition.