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  3. Dynamic Factor Analysis With Dependent Gaussian Processes For High-dimensional Gene Expression Trajectories.
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  3. Dynamic Factor Analysis With Dependent Gaussian Processes For High-dimensional Gene Expression Trajectories.

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Dynamic factor analysis with dependent Gaussian processes for high-dimensional gene expression trajectories.

Jiachen Cai1, Robert J B Goudie1, Colin Starr1

  • 1MRC Biostatistics Unit, University of Cambridge, Cambridge CB2 0SR, United Kingdom.

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|November 18, 2024

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces a novel Bayesian method using dependent Gaussian processes to analyze gene expression pathways. The approach accurately models pathway correlations and improves gene expression prediction for precision medicine.

Keywords:
Monte Carlo expectation maximizationdependent Gaussian processeshigh-dimensional biomarker expression trajectoriesmultivariate longitudinal datapathwayssparse factor analysis

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Area of Science:

  • Genomics
  • Systems Biology
  • Computational Biology

Background:

  • High-dimensional, longitudinal gene expression data are crucial for understanding biological mechanisms in precision medicine.
  • Complex diseases may be best understood by analyzing interacting biological pathways rather than individual genes.

Purpose of the Study:

  • To develop a Bayesian approach for characterizing correlations among biological pathways using longitudinal gene expression data.
  • To map high-dimensional gene expression trajectories to low-dimensional pathway trajectories, relaxing the assumption of independent factors.

Main Methods:

  • Utilized dependent Gaussian processes (DGP) to model pathway correlations.
  • Employed Bayesian sparse factor analysis to map gene expression to pathway trajectories.
  • Developed a Monte Carlo expectation maximization (MCEM) scheme for model fitting, integrated with Markov Chain Monte Carlo (MCMC) and an R package (GPFDA).

    • Main Results:

    • The proposed method demonstrated superior performance in recovering pathway expression trajectories.
    • Successfully revealed relationships between genes and pathways.
    • Achieved improved gene expression prediction with closer point estimates and narrower predictive intervals compared to existing methods.
    • Validated through simulations and real data analysis.

    Conclusions:

    • The novel Bayesian approach effectively models correlated biological pathways from longitudinal gene expression data.
    • The method enhances understanding of gene-pathway relationships and improves predictive accuracy for precision medicine applications.
    • The associated R package (DGP4LCF) is publicly available, facilitating broader adoption and further research.