Clustering mutational spectra via classification likelihood and markov chain monte carlo algorithms

  • Mario MedvedovicEmail author
  • Paul Succop
  • Rakesh Shukla
  • Kathleen Dixon


We have analyzed a set of 39 mutational spectra of the supF gene that were generated by different mutagenicagents and under different experimental conditions. The clusteranalyses was performed using a newly developed clustering procedure. The clustering criterion used in the procedure was developed by applying the classification likelihood approach to multinomial observations. We also developed a Gibbs sampling-based optimization procedure that outperformed previously developed methods in a comparative simulation study The results of the cluster analysis showed that our clustering procedure was able to recreate natural grouping of the mutational spectra with respect to the characteristics of mutagenic agents used to generate them and with respect to experimental conditions applied in the process of generating spectra. These results are an important confirmation of the relevance of mutational spectra in characterizing mutagenic mechanisms of different carcinogens.

Key Words

Classification expectation maximization algorithm Cluster analysis Gibbs sampling Multinomial distribution Stochastic expectation maximization algorithm 


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Copyright information

© International Biometric Society 2001

Authors and Affiliations

  • Mario Medvedovic
    • 1
    Email author
  • Paul Succop
    • 1
  • Rakesh Shukla
    • 1
  • Kathleen Dixon
  1. 1.Department of Environmental HealthUniversity of Cincinnati Medical CenterCincinnati

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