Identification of Robust Clustering Methods in Gene Expression Data Analysis

Background: Cluster analysis techniques of gene expression microarray data is of increasing interest in the field of current bioinformatics. One of the reasons for this is the need for molecular-based refinement of broadly defined biological classes, with implications in cancer diagnosis, prognosis and treatment. And many algorithms have been developed for this problem. Objective: However microarray data frequently include outliers, and how to treat these outlier's effects in the subsequent analysis-clustering. Method: In this paper, we present the large-scale analysis of seven different agglomerative hierarchical clustering methods and five proximity measures for the analysis of 33 cancer gene expression datasets. As a case study, we used two experimental datasets: Affymetrix and cDNA, and different percent outliers were artificially added to these datasets. Results: We found that ward method gives the highest corrected Rand index value with respect to the spearman proximity measures when datasets contain with and without outliers. Conclusion: This study proves that ward method is more robust clustering methods in gene expression data analysis among other methods.