Cluster analysis using multivariate normal mixture models to detect differential gene expression with microarray data
Cluster analysis using multivariate normal mixture models to detect differential gene expression with microarray data
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DOI：
10.1016/j.csda.2006.02.012
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发表时间：
2006-11-15
影响因子：
1.8
通讯作者：
Lin, Jizhen
中科院分区：
文献类型：
--
作者：
He, Yi;Pan, Wei;Lin, Jizhen
DNA微阵列使同时研究生物样品中数千个基因的表达成为可能。单变量聚类技术已被用来发现两个实验条件之间的差异表达的靶基因。由于使用单变量汇总统计可能会丢失信息，因此使用多变量统计可能更有效。本文提出了基于多元正态混合模型的聚类分析方法来检测两种条件下基因表达的差异，与一般的混合模型和基于模型的聚类分析不同，我们提出了具有特定均值和协方差结构的混合模型来解释双条件微阵列实验的特殊性。导出了三种模型的EM算法的显式修正公式。该方法被应用到一个真实的数据集，以比较1176个基因的表达水平的大鼠与肺炎球菌中耳感染，以说明这种方法的性能和实用性。在六维模型和双变量模型中分别发现了约10个和20个基因的差异表达。两个模拟研究进行比较的性能单变量和多变量的方法。根据数据，任何一种方法都不能总是支配另一种方法。结果表明，多元正态混合模型可以是有用的替代单变量方法来检测差异基因表达的探索性数据分析。(c)2006 Elsevier B.V.保留所有权利。
DNA microarrays make it possible to study simultaneously the expression of thousands of genes in a biological sample. Univariate clustering techniques have been used to discover target genes with differential expression between two experimental conditions. Because of possible loss of information due to use of univariate summary statistics, it may be more effective to use multivariate statistics. We present multivariate normal mixture model based clustering analyses to detect differential gene expression between two conditions.Deviating from the general mixture model and model-based clustering, we propose mixture models with specific mean and covariance structures that account for special features of two-condition microarray experiments. Explicit updating formulas in the EM algorithm for three such models are derived. The methods are applied to a real dataset to compare the expression levels of 1176 genes of rats with and without pneumococcal middle-ear infection to illustrate the performance and usefulness of this approach. About 10 genes and 20 genes are found to be differentially expressed in a six-dimensional modeling and a bivariate modeling, respectively. Two simulation studies are conducted to compare the performance of univariate and multivariate methods. Depending on data, neither method can always dominate the other. The results suggest that multivariate normal mixture models can be useful alternatives to univariate methods to detect differential gene expression in exploratory data analysis. (c) 2006 Elsevier B.V. All rights reserved.