Models for microarray gene expression data.
Models for microarray gene expression data.
复制标题
DOI：
10.1081/bip-120005737
复制
发表时间：
2002-02-01
影响因子：
1.1
通讯作者：
Beier, David
中科院分区：
文献类型：
--
作者：
Lee, Mei-Ling Ting;Lu, Weining;Beier, David
本文描述了一种基于微阵列数据分析差异基因表达的一般方法。首先，我们通过一个线性统计模型来描述数据的特征，该模型考虑了数据中相关的变化来源，然后我们考虑模型参数的估计。由于微阵列研究通常涉及数千个基因，我们提出了一种两阶段的参数估计方法。该模型中基因和实验条件的相互作用项捕获了微阵列数据中有关差异基因表达的所有相关信息。我们提出了一种由零分量和替代分量组成的微分表达式汇总统计量的混合分布模型。混合模型提出了两种鉴定差异表达基因的方法。一种是频率论方法，用于识别不同的基因，另一种是经验贝叶斯程序，根据观察到的微阵列读数，产生差异表达的估计后验概率。
This paper describes a general methodology for the analysis of differential gene expression based on microarray data. First, we characterize the data by a linear statistical model that accounts for relevant sources of variation in the data and then we consider estimation of the model parameters. Because microarray studies typically involve thousands of genes, we propose a two-stage method for parameter estimation. The interaction terms for genes and experimental conditions in this model capture all relevant information about differential gene expression in the microarray data. We propose a mixture distribution model for a summary statistic of differential expression that consists of null and alternative component distributions. The mixture model suggests two methods for identifying genes exhibiting differential expression. One is a frequentist method that identifies distinguished genes and the other an empirical Bayes procedure that yields estimated posterior probabilities of differential expression, conditional on observed microarray readings.