Robust modeling of differential gene expression data using normal/independent distributions: a Bayesian approach.

ABSTRACT: 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 pu

SUBMITTER: Ganjali M 

PROVIDER: S-EPMC4409222 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature