实现输出统一排名的差异共表达双聚类算法

Differential co-expression Biclusters Algorithm for Output Unified Ranking Implementation

提出了差异共表达框架和一个差异共表达评分函数,以观察到的一个双聚类基因在所属双聚类的条件下共表达和在其他条件下非共表达为基础,客观量化基因双聚类的质量.此外,还提出了一个评分函数把双聚类分层为三种类型的共表达.在实现双聚类输出统一排名中,使用提出的评分函数对这4个公认的双聚类算法在不同区域的6个实际数据集上的性能和行为进行测试.实验结果表明,在鉴别共表达双聚类方面,差异共表达框架能有效提高共表达基因双聚类质量和双聚类算法的性能.

In this paper, we propose different expression framework and a differential co-expression scoring function to objectively quantify quality or goodness of a bicluster of genes based on the observation that genes in a bicluster are co-expressed in the conditions belonged to the bicluster and not co-expressed in the other conditions. Furthermore, we propose a scoring function to stratify hiclusters into three types of co-expression. We used the proposed scoring functions to understand the performance and behavior of the four well established biclustering algorithms on six real datasets from different domains by combining their output into one unified ranking. Experimental results show that differential co-expression framework is effective to provide quantitative and objective assessment of the goodness of biclusters of co-expressed genes and performance of hi- clustering algorithms in identifying co-expression hiclusters.