Biclustering Analysis and Comparison for Gene Expression Data

Abstract

The advance of the high throughput technology allows to quantitative measure the level of expression of thousands of genes under several experimental conditions at one time which quickly produce many gene expression data. For efficiency processing the above data, many biclustering algorithms and many criterions are proposed. Even so, different biclustering algorithms generate different type of biclusters and draw various conclusions because the definition of the bicluster and its evaluation criterions used indifferent biclustering algorithms are similar but not the same. But there are only a few literatures forcomparing the biclustering algorithms and its evaluationcriterions. In this paper, the biclusters obtained by 11 biclustering algorithms are evaluated according to 7 criterions. In addition, the weight enrichment score and the proportion of the enriched biclusters by gene ontology are used for evaluating the biological significance of the biclustering algorithms.By analyzing the results of the chose biclustering algorithms, this study suggest that the biclustering algorithms provide a flexible solution for analyzing multiple types of gene expression data, and manyinteresting results are observedwhich facilitate exploratory data mining of gene expression data.