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Related Experiment Video

Updated: Jul 12, 2026

G2-seq: A High Throughput Sequencing-based Technique for Identifying Late Replicating Regions of the Genome
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Sparse time-varying log-ratios for longitudinal high-throughput sequencing data.

Ruijin Lu1, Guoqi Yu2,3, Cuilin Zhang2,3

  • 1Center for Biostatistics and Data Science, Washington University School of Medicine, St. Louis, MO, United States.

Frontiers in Bioinformatics
|July 11, 2026
PubMed
Summary

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This summary is machine-generated.

We developed LCoDaCoRe, a new method for analyzing longitudinal omics data. It effectively identifies key features in compositional data over time, improving predictions for health outcomes.

Area of Science:

  • Bioinformatics
  • Statistical Genomics
  • Computational Biology

Background:

  • Longitudinal omics data (e.g., metabolomics, microbiome) are high-dimensional, compositional, and irregularly sampled.
  • Existing methods often address compositional or temporal aspects separately, lacking a unified approach for complex longitudinal data.

Purpose of the Study:

  • To introduce LCoDaCoRe, a supervised learning method designed to analyze longitudinal, compositional omics data.
  • To identify sparse, time-varying log-ratio features that capture both compositional and temporal dynamics.

Main Methods:

  • LCoDaCoRe integrates functional data analysis and continuous relaxation for efficient feature selection.
  • It utilizes eigenspace expansion to handle both dense and sparse longitudinal sampling.
Keywords:
compositional datalog-ratiolongitudinal omics datatime-varyingvariable selection

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  • The method focuses on log-ratio transformations of compositional data.

    • Main Results:

    • Simulations show LCoDaCoRe outperforms cross-sectional methods in predictive accuracy, selection sparsity, and precision.
    • The method performs well across different correlation structures and outcome prevalence levels.
    • Application to fetal lipidomics data identified a key triglyceride-to-sphingolipid ratio predicting large-for-gestational-age births.

    Conclusions:

    • LCoDaCoRe offers a robust framework for analyzing complex longitudinal compositional omics data.
    • The identified lipid ratio provides a stable and predictive biomarker for fetal growth outcomes.
    • This approach enhances feature selection and predictive modeling in high-dimensional biological studies.