A semi-parametric Bayesian model for unsupervised differential co-expression analysis.
A semi-parametric Bayesian model for unsupervised differential co-expression analysis.
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DOI：
10.1186/1471-2105-11-234
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发表时间：
2010-05-07
影响因子：
3
通讯作者：
Medvedovic M
中科院分区：
文献类型：
--
作者：
Freudenberg JM;Sivaganesan S;Wagner M;Medvedovic M
差异共表达分析是表征基因表达调控网络的疾病相关失调的新兴策略。给定预定义的生物样品组，这种分析旨在鉴定在一组样品中共表达但在另一组样品中不共表达的基因。我们开发了一种新的概率框架，用于联合发现具有特定共表达模式的上下文（即样本组），以及在这些上下文中具有不同共表达模式的基因组。与当前的聚类和双聚类程序相比，用于对生物样品进行分组的该模型中的隐式相似性度量是基于每个样品内的基因的聚类结构，而不是基于基因表达水平相似性的传统度量。在此框架内，具有广泛不一致表达模式的生物样品可以被放置在相同的背景下，只要基因的共聚类结构在这些样品内是一致的。据我们所知，这是迄今为止第一种在这种普遍性下进行无监督差分共表达分析的方法。当应用于识别乳腺癌分子亚型的问题时，我们的方法在几个独立的表达数据集中识别了差异共表达的可重复模式。由这些模式引起的样本分组对疾病结果具有高度的信息性。差异共表达基因的表达模式为ERα调控网络的复杂性提供了新的见解。我们证明，使用的共同聚类结构的相似性度量的样本基因表达谱的无监督分析提供了有价值的信息表达调控网络。
Differential co-expression analysis is an emerging strategy for characterizing disease related dysregulation of gene expression regulatory networks. Given pre-defined sets of biological samples, such analysis aims at identifying genes that are co-expressed in one, but not in the other set of samples. We developed a novel probabilistic framework for jointly uncovering contexts (i.e. groups of samples) with specific co-expression patterns, and groups of genes with different co-expression patterns across such contexts. In contrast to current clustering and bi-clustering procedures, the implicit similarity measure in this model used for grouping biological samples is based on the clustering structure of genes within each sample and not on traditional measures of gene expression level similarities. Within this framework, biological samples with widely discordant expression patterns can be placed in the same context as long as the co-clustering structure of genes is concordant within these samples. To the best of our knowledge, this is the first method to date for unsupervised differential co-expression analysis in this generality. When applied to the problem of identifying molecular subtypes of breast cancer, our method identified reproducible patterns of differential co-expression across several independent expression datasets. Sample groupings induced by these patterns were highly informative of the disease outcome. Expression patterns of differentially co-expressed genes provided new insights into the complex nature of the ERα regulatory network. We demonstrated that the use of the co-clustering structure as the similarity measure in the unsupervised analysis of sample gene expression profiles provides valuable information about expression regulatory networks.
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