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Mixed-model coexpression: calculating gene coexpression while accounting for expression heterogeneity.

Nicholas A Furlotte1, Hyun Min Kang, Chun Ye

  • 1Department of Computer Science, University of California, Los Angeles, CA 90024, USA. nfurlott@cs.ucla.edu

Bioinformatics (Oxford, England)
|June 21, 2011
PubMed
Summary
This summary is machine-generated.

A new method, mixed model coexpression (MMC), accurately calculates gene coexpression by accounting for confounding effects. MMC reduces spurious correlations, improving the reliability of gene coexpression analysis in genetic studies.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Statistical Genetics

Background:

  • Gene coexpression analysis is crucial for genetic studies.
  • Traditional methods like Pearson's correlation can be inflated by unobserved confounding effects, leading to spurious correlations.
  • Existing methods may not fully address residual confounding, necessitating specialized approaches.

Purpose of the Study:

  • To introduce a novel statistical model, mixed model coexpression (MMC), for calculating gene coexpression.
  • To address the challenge of confounding effects in gene expression data analysis.
  • To improve the accuracy and reliability of gene coexpression identification.

Main Methods:

  • Developed a mixed model coexpression (MMC) framework.
  • Modeled gene coexpression within a mixed model.
  • Conditioned on the inter-sample correlation matrix to account for global confounding effects.

Main Results:

  • MMC effectively reduces spurious coexpressions by accounting for confounding.
  • Applied MMC to human and yeast datasets, demonstrating its superiority over Pearson's correlation.
  • MMC outperformed Pearson's correlation with surrogate variable analysis (SVA) in prioritizing strong coexpressions.

Conclusions:

  • MMC provides a robust method for gene coexpression analysis.
  • The approach significantly enhances the accuracy of identifying true gene coexpressions.
  • MMC is a valuable tool for genetic research, offering improved prioritization of gene interactions.