Assessing agreement of clustering methods with gene expression microarray data

In the rapidly evolving field of genomics, many clustering and classification methods have been developed and employed to explore patterns in gene expression data. Biologists face the choice of which clustering algorithm(s) to use and how to interpret different results from the various clustering algorithms. No clear objective criteria have been developed to assess the agreement and compare the results from different clustering methods. We describe two generally applicable objective measures to quantify agreement between different clustering methods. These two measures are referred to as the local agreement measure, which is defined for each gene/subject, and the global agreement measure, which is defined for the whole gene expression experiment. The agreement measures are based on a probabilistic weighting scheme applied to the number of concordant and discordant pairs from two clustering methods. In the comparison and assessment process, newly-developed concepts are implemented unde...

• Type: Journal
• Year: 2008
• Where: CSDA
• Authors: Xueli Liu, Sheng-Chien Lee, George Casella, Gary F. Peter
• Keywords: Agreement Measure, Clustering Methods, CSDA 2008, Gene Expression