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

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...

• Type: Journal
• Year: 2004
• Where: BMCBI
• Authors: Alexandre G. de Brevern, Serge A. Hazout, Alain Malpertuy
• Keywords: BMCBI 2004, Gene Cluster, Hierarchical Clustering, Hierarchical Clustering Algorithm