A new geometric biclustering algorithm based on the Hough transform for analysis of large-scale microarray data

Biclustering is an important tool in microarray analysis when only a subset of genes co-regulates in a subset of conditions. Different from standard clustering analyses, biclustering performs simultaneous classification in both gene and condition directions in a microarray data matrix. However, the biclustering problem is inherently intractable and computationally complex. In this paper, we present a new biclustering algorithm based on the geometrical viewpoint of coherent gene expression profiles. In this method, we perform pattern identification based on the Hough transform in a column-pair space. The algorithm is especially suitable for the biclustering analysis of large-scale microarray data. Our studies show that the approach can discover significant biclusters with respect to the increased noise level and regulatory complexity. Furthermore, we also test the ability of our method to locate biologically verifiable biclusters within an annotated set of genes.     

Genetic algorithm based two-mode clustering of metabolomics data

Metabolomics and other omics tools are generally characterized by large data sets with many variables obtained under different environmental conditions. Clustering methods and more specifically two-mode clustering methods are excellent tools for analyzing this type of data. Two-mode clustering methods allow for analysis of the behavior of subsets of metabolites under different experimental conditions. In addition, the results are easily visualized. In this paper we introduce a two-mode clustering method based on a genetic algorithm that uses a criterion that searches for homogeneous clusters. Furthermore we introduce a cluster stability criterion to validate the clusters and we provide an extended knee plot to select the optimal number of clusters in both experimental and metabolite modes. The genetic algorithm-based two-mode clustering gave biological relevant results when it was applied to two real life metabolomics data sets. It was, for instance, able to identify a catabolic pathway for growth on several of the carbon sources. Electronic supplementary material The online version of this article (doi:) contains supplementary material, which is available to authorized users. J. A. Hageman and R. A. van den Berg contributed equally to this paper.     

微阵列数据的多目标免疫优化双聚类

DNA微阵列技术的发展为基因表达研究提供更有效的工具。分析这些大规模基因数据主要应用聚类方法。最近,提出双聚类技术来发现子矩阵以揭示各种生物模式。多目标优化算法可以同时优化多个相互冲突的目标,因而是求解基因表达矩阵的双聚类的一种很好的方法。本文基于克隆选择原理提出了一个新奇的多目标免疫优化双聚类算法,来挖掘微阵列数据的双聚类。在两个真实数据集上的实验结果表明该方法比其他多目标进化双聚娄算法表现出更优越的性能。     

革兰阴性细菌耐药特性的双聚类分析

目的通过双聚类分析了解临床分离的革兰阴性菌的耐药特征。方法采用K-B法对临床分离的113株细菌进行药物敏感试验,用软件WHONET 5.6和MATLAB对药敏试验结果进行统计分析。结果传统耐药分析方法表明113株细菌总体耐药率较低,其中大肠埃希菌、铜绿假单胞菌和鲍曼不动杆菌的耐药率则较高,而肺炎克雷伯菌耐药率较低。通过双聚类分析,所有菌株被聚为三大类,I类菌株占23.0%,耐药率最高,几乎对18种药物都耐药,细菌种类以肺炎克雷伯菌、铜绿假单胞菌、鲍曼不动杆菌为主;Ⅱ类菌株占56.6%,耐药率普遍较低,以肺炎克雷伯菌为主;Ⅲ类菌株占20.4%,菌株的耐药率介于I和Ⅱ类之间,包括肺炎克雷伯菌、铜绿假单胞菌和大肠埃希菌。其中Ⅱ大类,根据所耐抗生素的不同又分为Ⅱ-A、Ⅱ-B、Ⅱ-C三个亚类,每一亚类都具有相似的耐药特点。结论本实验收集的革兰阴性菌株根据耐药特征被聚为Ⅰ、Ⅱ、Ⅲ类,耐药程度为IⅢⅡ,双聚类分析法有利于快速找到具有相同耐药特征的菌株以及不同菌株之间的耐药差别。     

Biclustering via Sparse Singular Value Decomposition

Summary Sparse singular value decomposition (SSVD) is proposed as a new exploratory analysis tool for biclustering or identifying interpretable row–column associations within high‐dimensional data matrices. SSVD seeks a low‐rank, checkerboard structured matrix approximation to data matrices. The desired checkerboard structure is achieved by forcing both the left‐ and right‐singular vectors to be sparse, that is, having many zero entries. By interpreting singular vectors as regression coefficient vectors for certain linear regressions, sparsity‐inducing regularization penalties are imposed to the least squares regression to produce sparse singular vectors. An efficient iterative algorithm is proposed for computing the sparse singular vectors, along with some discussion of penalty parameter selection. A lung cancer microarray dataset and a food nutrition dataset are used to illustrate SSVD as a biclustering method. SSVD is also compared with some existing biclustering methods using simulated datasets.