Bayesian hierarchical model for identifying changes in gene expression from microarray experiments.
Recent developments in microarrays technology enable researchers to study simultaneously the expression of thousands of genes from one cell line or tissue sample. This new technology is often used to assess changes in mRNA expression upon a specified transfection for a cell line in order to identify target genes. For such experiments, the range of differential expression is moderate, and teasing out the modified genes is challenging and calls for detailed modeling. The aim of this paper is to propose a methodological framework for studies that investigate differential gene expression through microarrays technology that is based on a fully Bayesian mixture approach (Richardson and Green, 1997). A case study that investigated those genes that were differentially expressed in two cell lines (normal and modified by a gene transfection) is provided to illustrate the performance and usefulness of this approach.
D001499:Bayes Theorem; D003627:Data Interpretation, Statistical; D020869:Gene Expression Profiling; D006801:Humans; D008957:Models, Genetic; D020411:Oligonucleotide Array Sequence Analysis; D014407:Tumor Cells, Cultured