A Bayesian framework for the analysis of microarray expression data: regularized t-test and statistical inferences of gene changes
A Bayesian framework for the analysis of microarray expression data: regularized t-test and statistical inferences of gene changes
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DOI：
10.1093/bioinformatics/17.6.509
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发表时间：
2001-06-01
期刊：
影响因子：
5.8
通讯作者：
Long, AD
中科院分区：
文献类型：
--
作者：
Baldi, P;Long, AD
动机：DNA微阵列现在能够在许多不同的条件下提供基因表达的全基因组模式。这些模式的第一级分析需要确定观察到的表达差异是否显著。目前的方法是不能令人满意的，由于缺乏一个系统的框架，可以容纳噪音，变异性，和低复制通常典型的microarray data.Results：我们开发了一个贝叶斯概率框架的微阵列数据分析。在最简单的层面上，我们通过独立的正态分布对对数表达值进行建模，并通过具有分层先验分布的相应均值和方差进行参数化。我们得到的点估计参数和超参数，和正则化的表达式，每个基因的方差相结合的经验方差与当地的背景方差与相邻的基因。一个额外的超参数，与经验观测的数量成反比，决定了背景方差的强度。模拟结果表明，这些点估计，结合t检验，提供了一个系统的推理方法，相比简单的t检验或折叠的方法，并部分弥补了缺乏复制。
Motivation: DNA microarrays are now capable of providing genome-wide patterns of gene expression across many different conditions. The first level of analysis of these patterns requires determining whether observed differences in expression are significant or not. Current methods are unsatisfactory due to the lack of a systematic framework that can accommodate noise, variability, and low replication often typical of microarray data.Results: We develop a Bayesian probabilistic framework for microarray data analysis. At the simplest level, we model log-expression values by independent normal distributions, parameterized by corresponding means and variances with hierarchical prior distributions. We derive point estimates for both parameters and hyperparameters, and regularized expressions for the variance of each gene by combining the empirical variance with a local background variance associated with neighboring genes. An additional hyperparameter, inversely related to the number of empirical observations, determines the strength of the background variance. Simulations show that these point estimates, combined with a t-test, provide a systematic inference approach that compares favorably with simple t-test or fold methods, and partly compensate for the lack of replication.