UniBic: Sequential row-based biclustering algorithm for analysis of gene expression data.
UniBic: Sequential row-based biclustering algorithm for analysis of gene expression data.
复制标题
UniBic：用于分析基因表达数据的基于顺序行的双聚类算法
DOI：
10.1038/srep23466
复制
发表时间：
2016-03-22
影响因子：
4.6
通讯作者：
Huang X
中科院分区：
文献类型：
--
作者：
Wang Z;Li G;Robinson RW;Huang X
自Morganet等人首创了将数据矩阵划分为近似常数的子矩阵的工作以来，双聚类算法得到了广泛的发展，该算法旨在通过寻找一组在特定条件下具有保持趋势的表达模式的基因来提供一种有效的方法来分析基因表达数据。然而，识别隐藏在基因表达数据中最有意义的亚结构的一般趋势保持双聚类仍然是一个极具挑战性的问题。我们发现了一种基本的方法，通过这种方法可以很容易地从嘈杂和复杂的大数据中识别出具有生物学意义的趋势保持双聚类。基本思想是将最长公共子序列（LCS）框架应用于从输入数据矩阵派生的索引矩阵中选定的行对，以定位要标识的每个双聚类的种子。我们在合成数据集和真实数据集上对其进行了测试，并将其性能与当前竞争激烈的双聚类工具进行了比较。我们发现，新算法，名为UniBic，在常用的评估场景中优于所有以前的双聚类算法，除了在窄双聚类上的BicSPAM。后者在寻找狭窄的双星团方面稍好一些，这是它专门设计的任务。
Biclustering algorithms, which aim to provide an effective and efficient way to analyze gene expression data by finding a group of genes with trend-preserving expression patterns under certain conditions, have been widely developed since Morganet al.pioneered a work about partitioning a data matrix into submatrices with approximately constant values. However, the identification of general trend-preserving biclusters which are the most meaningful substructures hidden in gene expression data remains a highly challenging problem. We found an elementary method by which biologically meaningful trend-preserving biclusters can be readily identified from noisy and complex large data. The basic idea is to apply the longest common subsequence (LCS) framework to selected pairs of rows in an index matrix derived from an input data matrix to locate a seed for each bicluster to be identified. We tested it on synthetic and real datasets and compared its performance with currently competitive biclustering tools. We found that the new algorithm, named UniBic, outperformed all previous biclustering algorithms in terms of commonly used evaluation scenarios except for BicSPAM on narrow biclusters. The latter was somewhat better at finding narrow biclusters, the task for which it was specifically designed.