Abstract
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.
Disciplines
Bioinformatics | Computational Biology | Microarrays
Suggested Citation
Zhou, Chuan; Wakefield, Jon; and Breeden, Linda, "Bayesian Analysis of Cell-Cycle Gene Expression Data" (December 2005). UW Biostatistics Working Paper Series. Working Paper 276.
https://biostats.bepress.com/uwbiostat/paper276
Comments
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.