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A Three Component Latent Class Model for Robust Semiparametric Gene Discovery

Author

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  • Alfo' Marco

    (Sapienza - Università di Roma)

  • Farcomeni Alessio

    (Sapienza - Università di Roma)

  • Tardella Luca

    (Sapienza - Università di Roma)

Abstract

We propose a robust model for discovering differentially expressed genes which directly incorporates biological significance, i.e., effect dimension. Using the so-called c-fold rule, we transform the expressions into a nominal observed random variable with three categories: below a fixed lower threshold, above a fixed upper threshold or within the two thresholds. Gene expression data is then transformed into a nominal variable with three levels possibly originated by three different distributions corresponding to under expressed, not differential, and over expressed genes. This leads to a statistical model for a 3-component mixture of trinomial distributions with suitable constraints on the parameter space. In order to obtain the MLE estimates, we show how to implement a constrained EM algorithm with a latent label for the corresponding component of each gene. Different strategies for a statistically significant gene discovery are discussed and compared. We illustrate the method on a little simulation study and a real dataset on multiple sclerosis.

Suggested Citation

  • Alfo' Marco & Farcomeni Alessio & Tardella Luca, 2011. "A Three Component Latent Class Model for Robust Semiparametric Gene Discovery," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 10(1), pages 1-19, January.
    • Handle: RePEc:bpj:sagmbi:v:10:y:2011:i:1:n:7
    DOI: 10.2202/1544-6115.1565
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    References listed on IDEAS

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    1. Mark A. van de Wiel & Kyung In Kim, 2007. "Estimating the False Discovery Rate Using Nonparametric Deconvolution," Biometrics, The International Biometric Society, vol. 63(3), pages 806-815, September.
    2. Alfo, Marco & Farcomeni, Alessio & Tardella, Luca, 2007. "Robust semiparametric mixing for detecting differentially expressed genes in microarray experiments," Computational Statistics & Data Analysis, Elsevier, vol. 51(11), pages 5253-5265, July.
    3. Lewin Alex & Bochkina Natalia & Richardson Sylvia, 2007. "Fully Bayesian Mixture Model for Differential Gene Expression: Simulations and Model Checks," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 6(1), pages 1-28, December.
    4. Ingrassia, Salvatore & Rocci, Roberto, 2007. "Constrained monotone EM algorithms for finite mixture of multivariate Gaussians," Computational Statistics & Data Analysis, Elsevier, vol. 51(11), pages 5339-5351, July.
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    Cited by:

    1. Farcomeni Alessio & Arima Serena, 2012. "A Bayesian autoregressive three-state hidden Markov model for identifying switching monotonic regimes in Microarray time course data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 11(4), pages 1-31, June.

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