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Shortest path analysis using partial correlations for classifying gene functions from gene expression data.

A Marie Fitch1, M Beatrix Jones

  • 1Institute of Information and Mathematical Sciences, Massey University, Auckland, New Zealand. m.fitch@massey.ac.nz

Bioinformatics (Oxford, England)
|November 6, 2008
PubMed
Summary
This summary is machine-generated.

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This study introduces a novel method using Gaussian graphical models (GGMs) and partial correlations to map gene functions. This approach enhances gene classification accuracy compared to traditional correlation graphs.

Area of Science:

  • Bioinformatics
  • Systems Biology
  • Computational Biology

Background:

  • Gaussian graphical models (GGMs) are widely used for gene association structures.
  • Edge lengths in GGMs can represent gene pair relationships, inversely proportional to partial correlation.
  • Graphical lasso is a method for fitting GGMs and estimating partial correlations.

Purpose of the Study:

  • To develop a method for assigning edge lengths in GGMs based on partial correlations.
  • To use these edge lengths to identify shortest paths between genes.
  • To classify gene functions based on shared functions of genes along these paths.

Main Methods:

  • Fitting GGMs using graphical lasso to obtain partial correlations.
  • Calculating shortest paths between gene pairs within the GGM.

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  • Classifying gene function based on intermediate genes on shortest paths.

    • Main Results:

    • The partial correlation graph approach successfully classified genes of known function in yeast.
    • This method approximately doubled the number of genes classified with comparable accuracy to correlation graphs.
    • When tuned for similar gene classification numbers, partial correlation graphs significantly improved classification accuracy.

    Conclusions:

    • Partial correlation graphs offer a more accurate and efficient method for gene function classification.
    • This approach enhances the biological insights derived from gene expression data.
    • The method provides a robust framework for exploring gene associations and functions.