【摘要】 随着近年来高通量基因表达谱数据的涌现,集成多个不同实验条件的表达谱数据,并挖掘在多数据源都保守的基因共表达团,成为预测基因功能或者调控关系的方法之一.但是,常用的方法通常仅简单地集成不同表达谱数据并推导保守基因共表达团,这样可能会导致结果中出现并非真正在多数据源保守的共表达团.提出一种结合最小哈希与局部敏感哈希的新方法,可以高效地寻找在多表达谱数据源中真正保守的基因共表达团.结果分析证明,相比过去的方法,现提出的方法可以获得更加功能相关和调控相关的基因共表达团.更多还原

【Abstract】 A number of recent studies have focused on discovering genetic functional or transcriptional modules by integrating information from the rapidly accumulating large-scale microarray expression datasets. Such studies commonly model each microarray as a co-expression network, and detect the conserved gene co-expression clusters from these co-expression networks. Currently, the commonly used method is mining conserved co-expression clusters directly from a "summary network" , which is obtained by aggregating all the co-expression networks derived from different microarrays. However, this method may generate false conserved clusters, which never occur in any of the original individual co-expression networks. Here a scalable and efficient method were proposed to detect the truly conserved gene co-expression clusters from multiple microarrays. This problem is formulated as mining frequently occurring subgraphs across multiple co-expression networks, and involves three steps: (1) Translating each microarray into co-expression network; (2) Clustering edges which occur in the similar co-expression networks by min-hashing and locality-sensitive hashing techniques to obtain the candidate clusters; (3) Applying graph clustering method to the candidate clusters to detect the conserved co-expressed clusters. This method was applied to yeast microarrays and the results demonstrate that, compared to the previous study, the conserved co-expressed clusters detected by the method were more likely to be functionally homogeneous entities or potential transcriptional modules.更多还原