Partitioning gene expression data by data-driven Markov chain Monte Carlo
Erlandson Ferreira Saraiva, Adriano Kamimura Suzuki, Francisco Louzada, Luís Aparecido Milan · Journal of Applied Statistics · 2015
In this paper we introduce a Bayesian mixture model with an unknown number of components for partitioning gene expression data. Inferences about all the unknown parameters involved are made by using the proposed data-driven Markov chain Monte Carlo. This algorithm is essentially a Metropolis--Hastings within Gibbs sampling. The Metropolis--Hastings is performed to change the number of partitions k in the neighborhood and using a pair of split-merge moves. Our strategy for splitting is based on data in which allocation probabilities are calculated based on marginal likelihood function from the previously allocated observations. Conditional on k , the partitions labels are updated via Gibbs sampling. The two main advantages of the proposed algorithm is that it is easy to be implemented and the acceptance probability for split-merge movements depends only on the observed data. We examine the performance of the proposed algorithm on simulated data and then analyze two publicly available gene expression data sets.