Extracting Between-Pathway Models from E-MAP Interactions Using Expected Graph Compression
Extracting Between-Pathway Models from E-MAP Interactions Using Expected Graph Compression
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DOI：
10.1089/cmb.2010.0268
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发表时间：
2011-03-01
影响因子：
1.7
通讯作者：
Kingsford, Carl
中科院分区：
文献类型：
--
作者：
Kelley, David R.;Kingsford, Carl
遗传相互作用（如合成致死相互作用）已经成为可量化的大规模使用上位微型阵列谱（E-MAP）方法。E-MAP允许构建一个大型的加权网络，既可以加重基因之间的相互作用，也可以减轻基因之间的相互作用。通过将基因聚类成模块并建立模块之间的关系，我们可以发现补偿途径。我们介绍了一个将贪婪聚类启发式算法应用于概率图的通用框架。我们使用该框架将一种称为图摘要的图聚类方法应用于以酵母染色体生物学为目标的E-MAP。这就产生了一种聚类E-MAP数据的新方法，我们称之为期望图压缩（EGC）。我们使用丰富的基因本体注释和基于相关基因表达的新方法来验证模块和补偿通路。EGC找到了许多以前的方法找不到的模块来聚类E-MAP数据。EGC还揭示了先前发现的几个模块中包含的核心子模块，这表明EGC可以揭示E-MAP网络的更精细结构。
Genetic interactions (such as synthetic lethal interactions) have become quantifiable on a large-scale using the epistatic miniarray profile (E-MAP) method. An E-MAP allows the construction of a large, weighted network of both aggravating and alleviating genetic interactions between genes. By clustering genes into modules and establishing relationships between those modules, we can discover compensatory pathways. We introduce a general framework for applying greedy clustering heuristics to probabilistic graphs. We use this framework to apply a graph clustering method called graph summarization to an E-MAP that targets yeast chromosome biology. This results in a new method for clustering E-MAP data that we call Expected Graph Compression (EGC). We validate modules and compensatory pathways using enriched Gene Ontology annotations and a novel method based on correlated gene expression. EGC finds a number of modules that are not found by any previous methods to cluster E-MAP data. EGC also uncovers core submodules contained within several previously found modules, suggesting that EGC can reveal the finer structure of E-MAP networks.