A latent variable approach for meta-analysis of gene expression data from multiple microarray experiments.
A latent variable approach for meta-analysis of gene expression data from multiple microarray experiments.
复制标题
来自多个微阵列实验的基因表达数据的荟萃分析的潜在变量方法。
DOI：
10.1186/1471-2105-8-364
复制
发表时间：
2007-09-27
影响因子：
3
通讯作者：
Ghosh, Debashis
中科院分区：
文献类型：
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作者：
Choi, Hyungwon;Shen, Ronglai;Chinnaiyan, Arul M;Ghosh, Debashis
随着不同研究人员在类似实验中使用微阵列技术产生的数据爆炸式增长，将多个研究的结果结合起来是很有兴趣的。在这篇文章中，我们描述了一个通用的概率框架，结合高通量基因组数据从几个相关的微阵列实验使用混合模型。该模型的一个关键特征是使用潜在变量，这些潜在变量表示可以跨不同平台组合的数量。我们考虑两种方法来估计一个指数称为表达概率（POE）。第一种是作者在之前的工作中报道的，涉及马尔可夫链蒙特卡罗（MCMC）技术。第二种方法是基于期望最大化（EM）算法的快速算法。这些方法的应用说明了数据集的荟萃分析转移性癌症。本文中描述的统计方法可以通过R软件包metaArray 1.8.1获得，该软件包位于Bioconductor，其URL为。
With the explosion in data generated using microarray technology by different investigators working on similar experiments, it is of interest to combine results across multiple studies. In this article, we describe a general probabilistic framework for combining high-throughput genomic data from several related microarray experiments using mixture models. A key feature of the model is the use of latent variables that represent quantities that can be combined across diverse platforms. We consider two methods for estimation of an index termed the probability of expression (POE). The first, reported in previous work by the authors, involves Markov Chain Monte Carlo (MCMC) techniques. The second method is a faster algorithm based on the expectation-maximization (EM) algorithm. The methods are illustrated with application to a meta-analysis of datasets for metastatic cancer. The statistical methods described in the paper are available as an R package, metaArray 1.8.1, which is at Bioconductor, whose URL is .