A new measure for gene expression biclustering based on non-parametric correlation
A new measure for gene expression biclustering based on non-parametric correlation
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DOI：
10.1016/j.cmpb.2013.07.025
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发表时间：
2013-12-01
影响因子：
6.1
通讯作者：
Calvo, Borja
中科院分区：
文献类型：
--
作者：
Flores, Jose L.;Inza, Inaki;Calvo, Borja
背景：对DNA微阵列数据进行分析的新兴技术之一称为双聚类，它是搜索一致表达的基因和条件的子集。这些亚群提供了有关主要生物过程的线索。到目前为止，已经提出了不同的方法来解决这个问题。它们大多使用均方残差作为质量度量，但不能检测出相关的、有趣的模式，如平移或缩放模式。此外，最近的文献表明，在不同类型的癌症和肿瘤中存在着新的相干模式，例如基因之间的反向关系无法捕获。结果：所提出的度量被称为Spearman双聚类度量(SBM)，该度量同时基于基因和条件之间的非线性相关性来评估双色的质量。通过使用一种称为分布估计算法的进化技术来执行双簇搜索，该算法使用SBM度量作为适应度函数。这种方法已经通过使用人工和真实的微阵列从不同的角度进行了检查。评估过程涉及使用质量指数、包括新模式在内的一套双色参考模式和一套统计测试。我们还用实际的微阵列测试了它的性能，并与Bimax、CC、OPSM、Played和xMotif等不同的算法方法进行了比较。结论：SBM显示出几个优点，例如能够识别更复杂的相干模式，如移位、缩放和反转，以及能够根据统计意义选择性地将基因和条件边缘化。(C)2013爱思唯尔爱尔兰有限公司。保留所有权利。
Background: One of the emerging techniques for performing the analysis of the DNA microarray data known as biclustering is the search of subsets of genes and conditions which are coherently expressed. These subgroups provide clues about the main biological processes. Until now, different approaches to this problem have been proposed. Most of them use the mean squared residue as quality measure but relevant and interesting patterns can not be detected such as shifting, or scaling patterns. Furthermore, recent papers show that there exist new coherence patterns involved in different kinds of cancer and tumors such as inverse relationships between genes which can not be captured.Results: The proposed measure is called Spearman's biclustering measure (SBM) which performs an estimation of the quality of a bicluster based on the non-linear correlation among genes and conditions simultaneously. The search of biclusters is performed by using a evolutionary technique called estimation of distribution algorithms which uses the SBM measure as fitness function. This approach has been examined from different points of view by using artificial and real microarrays. The assessment process has involved the use of quality indexes, a set of bicluster patterns of reference including new patterns and a set of statistical tests. It has been also examined the performance using real microarrays and comparing to different algorithmic approaches such as Bimax, CC, OPSM, Plaid and xMotifs. Conclusions: SBM shows several advantages such as the ability to recognize more complex coherence patterns such as shifting, scaling and inversion and the capability to selectively marginalize genes and conditions depending on the statistical significance. (C) 2013 Elsevier Ireland Ltd. All rights reserved.