Comparative analysis of clustering methods for gene expression time course data

Genetics and Molecular Biology

Ivan G. Costa²  Francisco De A. T. De Carvalho²  Marcílio C. P. De Souto¹ 

[1] ,Universidade Federal de Pernambuco Centro de Informática Recife PE ,Brazil

关键词: clustering methods;    gene expression time series;    unsupervised cross-validation;    cluster validation;   

DOI  :  10.1590/S1415-47572004000400025

来源: SciELO

【 摘 要 】

This work performs a data driven comparative study of clustering methods used in the analysis of gene expression time courses (or time series). Five clustering methods found in the literature of gene expression analysis are compared: agglomerative hierarchical clustering, CLICK, dynamical clustering, k-means and self-organizing maps. In order to evaluate the methods, a k-fold cross-validation procedure adapted to unsupervised methods is applied. The accuracy of the results is assessed by the comparison of the partitions obtained in these experiments with gene annotation, such as protein function and series classification.

【 授权许可】

CC BY   
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