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Identification of Regulatory Modules in Time Series Gene Expression Data Using a Linear Time Biclustering Algorithm

机译:使用线性时间平衡算法识别时序基因表达数据中的调控模块

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摘要

Although most biclustering formulations are NP-hard, in time series expression data analysis, it is reasonable to restrict the problem to the identification of maximal biclusters with contiguous columns, which correspond to coherent expression patterns shared by a group of genes in consecutive time points. This restriction leads to a tractable problem. We propose an algorithm that finds and reports all maximal contiguous column coherent biclusters in time linear in the size of the expression matrix. The linear time complexity of CCC-Biclustering relies on the use of a discretized matrix and efficient string processing techniques based on suffix trees. We also propose a method for ranking biclusters based on their statistical significance and a methodology for filtering highly overlapping and, therefore, redundant biclusters. We report results in synthetic and real data showing the effectiveness of the approach and its relevance in the discovery of regulatory modules. Results obtained using the transcriptomic expression patterns occurring in Saccharomyces cerevisiae in response to heat stress show not only the ability of the proposed methodology to extract relevant information compatible with documented biological knowledge but also the utility of using this algorithm in the study of other environmental stresses and of regulatory modules in general.
机译:尽管大多数双簇公式都是NP难解的,但在时间序列表达数据分析中,将问题限制在具有连续列的最大双簇的识别上是合理的,这与一组基因在连续时间点共享的一致表达模式相对应。这种限制导致一个棘手的问题。我们提出了一种算法,该算法在表达式矩阵的大小上以时间线性查找并报告所有最大的连续列相干双簇。 CCC编组的线性时间复杂度依赖于离散矩阵的使用以及基于后缀树的有效字符串处理技术。我们还根据其统计显着性,提出了一种对双聚类进行排名的方法,以及一种用于过滤高度重叠的双聚类的方法。我们以合成和真实数据报告结果,这些结果显示了该方法的有效性及其在发现监管模块中的相关性。使用酿酒酵母响应热应激而产生的转录组表达模式所获得的结果不仅显示了所提出方法学提取与文献记载的生物学知识兼容的相关信息的能力,而且还显示了使用该算法研究其他环境胁迫和环境的实用性。一般的监管模块。

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