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A fully Bayesian model to cluster gene-expression profiles.

C Vogl1, F Sanchez-Cabo, G Stocker

  • 1Institute of Animal Breeding and Genetics, Veterinärmedizinische Universität Wien, Vienna, Austria. claus.vogl@vu-wien.ac.at

Bioinformatics (Oxford, England)
|October 6, 2005
PubMed
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This study introduces a Bayesian model for gene expression clustering, dynamically adjusting the number of clusters and handling missing data. It successfully identifies biologically related genes in yeast transcriptomes, outperforming traditional methods.

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Gene expression profiling enables simultaneous monitoring of genome-wide gene activity.
  • Clustering co-expressed genes aids in identifying shared regulatory elements, functions, or origins.
  • Existing clustering methods often require pre-specifying the number of clusters and struggle with missing data.

Purpose of the Study:

  • To develop a fully probabilistic Bayesian model for clustering gene expression profiles.
  • To dynamically determine the optimal number of clusters without prior specification.
  • To integrate missing value imputation directly into the clustering model.

Main Methods:

  • A fully probabilistic Bayesian model utilizing a Reversible Jump Markov Chain Monte Carlo (RJMCMC) sampler.

Related Experiment Videos

  • Dynamic adjustment of the number of gene expression clusters.
  • Integrated imputation of missing gene expression values.

    • Main Results:

    • The proposed Bayesian model dynamically adjusts the number of clusters.
    • Missing gene expression values are effectively imputed within the model.
    • The method successfully identified biologically related co-expressed genes in a yeast transcriptome dataset, even with missing data.
    • Simulations demonstrated the sampler's convergence speed and accuracy of inferred variables, showing comparable or superior performance to k-means.

    Conclusions:

    • The Bayesian clustering approach offers a robust method for analyzing gene expression data, particularly in the presence of missing values.
    • Dynamic cluster number determination simplifies the analysis and improves biological relevance.
    • The developed model provides a valuable tool for genomic data analysis and discovery.