Bayesian analysis of cell cycle gene expression data, in "Bayesian Inference for Gene Expression and Proteomics", Marina Vannucci, Kim Anh Do and Peter Muller (editors), Cambridge University Press.


The study of the cell-cycle is important in order to aid in our understanding of the basic mechanisms of life, yet progress has been slow due to the complexity of the process and our lack of ability to study it at high resolution. Recent advances in microarray technology have enabled scientists to study the gene expression at the genome-scale with a manageable cost, and there has been an increasing effort to identify cell-cycle regulated genes. In this chapter, we discuss the analysis of cell-cycle gene expression data, focusing on a model-based Bayesian approaches. The majority of the models we describe can be fitted using freely available software.


Bioinformatics | Computational Biology | Microarrays