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A penalized likelihood approach for bivariate conditional normal models for dynamic co-expression analysis.

Jun Chen1, Jichun Xie, Hongzhe Li

  • 1Graduate Group in Genomics and Computational Biology, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania 19104, USA.

Biometrics
|April 9, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces a new model to identify factors like gene expression or genetic variations that influence gene co-expression. The developed methods effectively pinpoint these mediating variables in biological data.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Systems Biology

Background:

  • Gene co-expression analysis is crucial for understanding gene function from microarray data.
  • Co-expression patterns can be influenced by various factors, including cellular states, genetic variations (single nucleotide polymorphisms), and protein kinase activity.
  • Identifying these mediating factors is essential for a comprehensive understanding of gene regulatory networks.

Purpose of the Study:

  • To develop a statistical model for identifying variables that mediate gene co-expression patterns.
  • To introduce a likelihood ratio (LR) test and a penalized likelihood procedure for mediator identification.
  • To provide an efficient computational algorithm for implementing the proposed methods.

Main Methods:

  • A bivariate conditional normal model was developed to capture mediated co-expression.
  • A likelihood ratio (LR) test was formulated for detecting mediating variables.
  • A penalized likelihood procedure with an efficient iterative reweighted least squares and cyclic coordinate descent algorithm was proposed.
  • The oracle property of the penalized likelihood procedure was demonstrated with appropriate tuning parameter selection.

Main Results:

  • The LR-based approach demonstrated comparable or superior performance to existing methods like liquid association.
  • The penalized likelihood procedure proved effective in identifying mediating variables.
  • Simulations confirmed the efficacy of the proposed methods in identifying mediators of gene co-expression.
  • Application to yeast gene expression data successfully identified mediating kinases and single nucleotide polymorphisms.

Conclusions:

  • The proposed bivariate conditional normal model and associated statistical tests provide a robust framework for identifying mediators of gene co-expression.
  • The developed computational methods are efficient and effective for analyzing large-scale gene expression data.
  • These findings advance the understanding of complex gene interactions and regulatory mechanisms in biological systems.