Bayesian estimation of fold-changes in the analysis of gene expression: The PFOLD algorithm

被引:41
作者
Theilhaber, J
Bushnell, S
Jackson, A
Fuchs, R
机构
[1] Aventis Pharmaceut, Cambridge Genom Ctr, Cambridge, MA 02139 USA
[2] CuraGen Corp, New Haven, CT 06511 USA
[3] Biogen Inc, Cambridge, MA 02142 USA
关键词
gene expression data analysis; microarray noise modeling; gene expression profiles; Bayesian estimation and detection;
D O I
10.1089/106652701753307502
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
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, implemented in a program called PFOLD, which provides not only an estimate of the fold-change in gene expression, but also confidence limits for the change and a P-value quantifying the significance of the change. Although the focus is on oligonucleotide microarray technologies, the scheme can also be applied to cDNA based technologies if parameters for the noise model are provided. The model unifies 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.
引用
收藏
页码:585 / 614
页数:30
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