Analysis of gene expression data of the NCI 60 cancer cell lines using Bayesian hierarchical effects model

From the end of the last decade, NCI has been performing large screening of anticancer drug compounds and molecular targets on a pool of 60 cell lines of various types of cancer. In particular, a complete set of cDNA expression array data on the 60 cell lines are now available (Scherf et al., 2000; Ross et al., 2000). To discover differentially-expressed genes in each type of cancer cell lines, we need to estimate a large number of genetic parameters. especially interaction effects for all combinations of cancer types and genes, by decomposing the total variance into biological and array instrumental components. This error decomposition is important to identify subtle genes with low biological variability. An innovative statistical method is required for simultaneously estimating more than 100,000 parameters of interaction effects and error components. We propose a Bayesian statistical approach based on the construction of a hierarchical model adopting parametrization of a liner effects model. The estimation of the model parameters is performed by Markov Chain Monte Carlo, a recent computer-intensive statistical resampling technique. We have identified novel genes whose effects have not been revealed by the previous clustering approaches to the gene expression data.