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Open Access Research article

Influence of microarrays experiments missing values on the stability of gene groups by hierarchical clustering

Alexandre G de Brevern1*, Serge Hazout1 and Alain Malpertuy2

Author Affiliations

1 Equipe de Bioinformatique Génomique et Moléculaire (EBGM), INSERM E0346, Université Denis DIDEROT-Paris 7, case 7113, 2, place Jussieu, 75251 Paris Cedex 05, France

2 Atragene Bioinformatics, 4 Rue Pierre Fontaine, 91000 Evry, France

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BMC Bioinformatics 2004, 5:114  doi:10.1186/1471-2105-5-114

Published: 23 August 2004

Abstract

Background

Microarray technologies produced large amount of data. The hierarchical clustering is commonly used to identify clusters of co-expressed genes. However, microarray datasets often contain missing values (MVs) representing a major drawback for the use of the clustering methods. Usually the MVs are not treated, or replaced by zero or estimated by the k-Nearest Neighbor (kNN) approach. The topic of the paper is to study the stability of gene clusters, defined by various hierarchical clustering algorithms, of microarrays experiments including or not MVs.

Results

In this study, we show that the MVs have important effects on the stability of the gene clusters. Moreover, the magnitude of the gene misallocations is depending on the aggregation algorithm. The most appropriate aggregation methods (e.g. complete-linkage and Ward) are highly sensitive to MVs, and surprisingly, for a very tiny proportion of MVs (e.g. 1%). In most of the case, the MVs must be replaced by expected values. The MVs replacement by the kNN approach clearly improves the identification of co-expressed gene clusters. Nevertheless, we observe that kNN approach is less suitable for the extreme values of gene expression.

Conclusion

The presence of MVs (even at a low rate) is a major factor of gene cluster instability. In addition, the impact depends on the hierarchical clustering algorithm used. Some methods should be used carefully. Nevertheless, the kNN approach constitutes one efficient method for restoring the missing expression gene values, with a low error level. Our study highlights the need of statistical treatments in microarray data to avoid misinterpretation.