School of Computer Science and Technology, Northwestern Polytechnical University, Xi'an 710072, China.
Gene. 2013 Apr 10;518(1):59-69. doi: 10.1016/j.gene.2012.11.085. Epub 2012 Dec 28.
Biclustering algorithm can find a number of co-expressed genes under a set of experimental conditions. Recently, differential co-expression bicluster mining has been used to infer the reasonable patterns in two microarray datasets, such as, normal and cancer cells.
In this paper, we propose an algorithm, DECluster, to mine Differential co-Expression biCluster in two discretized microarray datasets. Firstly, DECluster produces the differential co-expressed genes from each pair of samples in two microarray datasets, and constructs a differential weighted undirected sample-sample relational graph. Secondly, the differential biclusters are generated in the above differential weighted undirected sample-sample relational graph. In order to mine maximal differential co-expression biclusters efficiently, we design several pruning techniques for generating maximal biclusters without candidate maintenance.
The experimental results show that our algorithm is more efficient than existing methods. The performance of DECluster is evaluated by empirical p-value and gene ontology, the results show that our algorithm can find more statistically significant and biological differential co-expression biclusters than other algorithms.
Our proposed algorithm can find more statistically significant and biological biclusters in two microarray datasets than the other two algorithms.
双聚类算法可以在一组实验条件下找到许多共表达的基因。最近，差异共表达双聚类挖掘已被用于推断两个微阵列数据集（例如正常和癌细胞）中的合理模式。
在本文中，我们提出了一种算法 DECluster，用于挖掘两个离散化微阵列数据集中的差异共表达双聚类。首先，DECluster 从两个微阵列数据集中的每对样本中生成差异共表达基因，并构建差异加权无向样本-样本关系图。其次，在上述差异加权无向样本-样本关系图中生成差异双聚类。为了有效地挖掘最大差异共表达双聚类，我们设计了几种剪枝技术，用于在不进行候选维护的情况下生成最大双聚类。
实验结果表明，我们的算法比现有方法更有效。通过经验 p 值和基因本体评估 DECluster 的性能，结果表明，我们的算法可以比其他算法找到更多具有统计学意义和生物学差异的共表达双聚类。
与其他两种算法相比，我们提出的算法可以在两个微阵列数据集中找到更具有统计学意义和生物学意义的双聚类。
