Robust modeling of differential gene expression data using normal/independent distributions: a Bayesian approach.
Mojtaba Ganjali, Taban Baghfalaki, Damon Berridge
Author Information
Mojtaba Ganjali: School of Biological Science, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran; Department of Statistics, Faculty of Mathematical Sciences, Shahid Beheshti University, Tehran, Iran.
Taban Baghfalaki: School of Biological Science, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran; Department of Statistics, Faculty of Mathematical Sciences, Tarbiat Modares University, Tehran, Iran.
Damon Berridge: Farr Institute-CIPHER, College of Medicine, Swansea University, Swansea, Wales, U.K.
In this paper, the problem of identifying differentially expressed genes under different conditions using gene expression microarray data, in the presence of outliers, is discussed. For this purpose, the robust modeling of gene expression data using some powerful distributions known as normal/independent distributions is considered. These distributions include the Student's t and normal distributions which have been used previously, but also include extensions such as the slash, the contaminated normal and the Laplace distributions. The purpose of this paper is to identify differentially expressed genes by considering these distributional assumptions instead of the normal distribution. A Bayesian approach using the Markov Chain Monte Carlo method is adopted for parameter estimation. Two publicly available gene expression data sets are analyzed using the proposed approach. The use of the robust models for detecting differentially expressed genes is investigated. This investigation shows that the choice of model for differentiating gene expression data is very important. This is due to the small number of replicates for each gene and the existence of outlying data. Comparison of the performance of these models is made using different statistical criteria and the ROC curve. The method is illustrated using some simulation studies. We demonstrate the flexibility of these robust models in identifying differentially expressed genes.
