Markov model for defining genomic changes using gene expression profiling.

摘要

With the advent and recent proliferation of genomic technologies such as gene expression arrays, researchers are now able to explore gene expression patterns for the majority of the genes in the genome. One active research area using gene expression profiles is the study of the transcriptome map. Several studies from various genomes have revealed unexpected gene clusters within which the gene expression levels are highly correlated.; To facilitate the search for such clusters in gene expression data, we propose a type of hidden Markov model (HMM) that will infer expression status for genes along the chromosomes. We construct a HMM in which gene expression can be categorized into five states: region of overexpression, singleton of overexpression, normal expression, region of underexpression, and singleton of underexpression. We first develop a discrete-time HMM (DTHMM) from which a continuous-time HMM (CTHMM) can be extended to account for the variation in base-pair distances between genes. The statistical properties of the CTHMM are studied through a Monte Carlo simulation. We also compare the performance of the CTHMM to moving median techniques to see how well they can recover the abnormal expression regions.; Both models are applied to a lung cancer gene expression data set to search for abnormal expression regions. We also assess the global impact of those regions on the patients' clinical variables, such as tumor stage, tumor differentiation, and patient survival. We finally point out some areas for future research.