A novel gene-centric clustering algorithm for standardization of time series expression data

E. Tsiporkova, V. Boeva
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Abstract

This paper proposes a novel data transformation method aiming at multi-purpose data standardization and inspired by gene-centric clustering approaches. The idea is to perform data standardization via template matching of each expression profile with the rest of the expression profiles employing dynamic time warping (DTW) alignment algorithm to measure the similarity between the expression profiles. This algorithm facilitates the identification of a cluster of genes whose expression profiles are related, possibly with a nonlinear time shift, to the profile of the gene supplied as a template. Consequently, for each gene profile a varying number (based on the degree of similarity) of neighboring gene profiles is identified to be used in the subsequent standardization phase. The latter uses a recursive aggregation algorithm in order to reduce the set of neighboring expression profiles into a singe profile representing the standardized version of the profile in question. The proposed data transformation method is evaluated and demonstrated on gene expression time series data coming from a study examining the global cell-cycle control of gene expression in fission yeast Schizosaccharomyces pombe.
一种新的时间序列表达数据标准化的以基因为中心的聚类算法
本文在以基因为中心的聚类方法的启发下,提出了一种针对多用途数据标准化的新型数据转换方法。其思想是通过模板匹配每个表达谱与其他表达谱,使用动态时间规整(DTW)对齐算法来衡量表达谱之间的相似性,从而执行数据标准化。该算法有助于识别一组基因,这些基因的表达谱可能与作为模板提供的基因的谱有关,可能具有非线性时移。因此,对于每个基因谱,一个不同数量(基于相似性程度)的邻近基因谱被确定用于随后的标准化阶段。后者使用递归聚合算法,以便将相邻的表达式概要文件集减少为表示所讨论的概要文件的标准化版本的单个概要文件。通过对裂糖酵母(Schizosaccharomyces pombe)基因表达的整体细胞周期控制的研究,对所提出的数据转换方法进行了评估和验证。
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