Bayesian Estimation of Fold-Changes in the Analysis of Gene Expression: The PFOLD Algorithm

A general and detailed noise model for the DNA microarray measurement of gene expression is presented and used to derive a Bayesian estimation scheme for expression ratios, imple-mented in a program called PFOLD, which provides not only an estimate of the fold-change in gene expression, but also con dence limits for the change and a P-value quantifying the signi cance of the change. Although the focus is on oligonucleotide microarray tech-nologies, the scheme can also be applied to cDNA based technologies if parameters for the noise model are provided. The model uni es estimation for all signals in that it provides a seamless transition from very low to very high signal-to-noise ratios, an essential feature for current microarray technologies for which the median signal-to-noise ratios are always moderate. The dual use, as decision statistics in a two-dimensional space, of the P-value and the fold-change is shown to be effective in the ubiquitous problem of detecting changing genes against a background of unchanging genes, leading to markedly higher sensitivities, at equal selectivity, than detection and selection based on the fold-change alone, a current practice until now. Key words: gene expression data analysis, microarray noise modeling, gene expression pro les, Bayesian estimation and detection