Microarray experiments and gene expression data have a number of characteristics that make them attractive but challenging for Bayesian analysis. There are many sources of variability, the variability is structured at different levels array specific, gene specific, ....) and the ratio of signal to noise is low. Typical experiments involve few samples but a large number of genes, so that borrowing information, e.g. across genes, to improve inference becomes essential. Hence embedding the inference in a hierarchical model formulation is natural.
Bayesian models adapted to the level of information processed have been developed to address some of the questions raised that range from modelling the signal to synthesising gene lists across different experiments. In this talk, I shall illustrate their use in variety of contexts: probe level models attempting to quantify uncertainty of the signal, differential expression mixture models and gene list synthesis. Cutting across these developments are important issues of MCMC performance and model checking. These issues will be illustrated on case studies.
This is joint work with colleagues on the BGX project : Marta Blangiardo, Natalia Bochkina, Anne Mette Hein (now at Aarhus), Alex Lewin, Ernest Turro and Peter Green (Bristol).
- http://www.bgx.org.uk - Related papers and technical reports