Bayesian estimation of fold-changes in the analysis of gene expression: The PFOLD algorithm
Bayesian estimation of fold-changes in the analysis of gene expression: The PFOLD algorithm
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DOI：
10.1089/106652701753307502
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发表时间：
2001-01-01
影响因子：
1.7
通讯作者：
Fuchs, R
中科院分区：
文献类型：
--
作者：
Theilhaber, J;Bushnell, S;Fuchs, R
提出了一种用于基因表达的DNA微阵列测量的通用和详细的噪声模型，并用于推导表达比率的贝叶斯估计方案，该方案在一个名为PFOLD的程序中实现，该方案不仅提供了对基因表达折叠变化的估计，而且提供了变化的置信限和量化变化重要性的P值。虽然重点放在寡核苷酸微阵列技术上，但如果提供噪声模型的参数，该方案也可以应用于基于cDNA的技术。该模型统一了对所有信号的估计，因为它提供了从非常低的信噪比到非常高的信噪比的无缝过渡，这是当前微阵列技术的一个基本特征，对于当前的微阵列技术，其中值信噪比总是适中的。作为二维空间中的决策统计，P值和折叠变化的双重使用被证明在普遍存在的问题中是有效的，即在不变的基因背景下检测变化的基因，导致以相同的选择性显著高于仅基于折叠变化的检测和选择，目前的做法是这样的。
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.