Mixture models for assessing differential expression in complex tissues using microarray data
Mixture models for assessing differential expression in complex tissues using microarray data
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DOI：
10.1093/bioinformatics/bth139
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发表时间：
2004-07-22
期刊：
影响因子：
5.8
通讯作者：
Ghosh, D
中科院分区：
文献类型：
--
作者：
Ghosh, D
动机：DNA 微阵列的使用在许多科学和医学学科中已经变得相当流行，例如在癌症研究中。这些研究的一个共同目标是确定哪些基因在癌症和健康组织之间，或者更一般地说，在两种实验条件之间存在差异表达。使用基因表达数据对肿瘤进行分子分析的一个主要并发症是该数据代表肿瘤和正常细胞的组合。用于利用微阵列数据评估差异表达而开发的许多方法都假设组织样本是同质的。结果：在本文中，我们概述了在存在混合细胞群的情况下确定差异表达的一般框架。我们考虑使用配对组织和未配对组织的研究设计。使用分层混合模型对数据进行建模；使用矩量程序和期望最大化算法的组合来估计模型参数。在模拟研究中评估该方法的有限样本特性；它们被应用于癌症研究的两个微阵列数据集。 R 语言的命令可以从 URL http://www.sph.umich.edu/similar toghoshd/COMPBIO/COMPMIX/ 下载。
Motivation: The use of DNA microarrays has become quite popular in many scientific and medical disciplines, such as in cancer research. One common goal of these studies is to determine which genes are differentially expressed between cancer and healthy tissue, or more generally, between two experimental conditions. A major complication in the molecular profiling of tumors using gene expression data is that the data represent a combination of tumor and normal cells. Much of the methodology developed for assessing differential expression with microarray data has assumed that tissue samples are homogeneous.Results: In this paper, we outline a general framework for determining differential expression in the presence of mixed cell populations. We consider study designs in which paired tissues and unpaired tissues are available. A hierarchical mixture model is used for modeling the data; a combination of methods of moments procedures and the expectation-maximization algorithm are used to estimate the model parameters. The finite-sample properties of the methods are assessed in simulation studies; they are applied to two microarray datasets from cancer studies. Commands in the R language can be downloaded from the URL http://www.sph.umich.edu/similar toghoshd/COMPBIO/COMPMIX/.