An adaptive approach for integration analysis of multiple gene expression datasets
AIMSA'10 Proceedings of the 14th international conference on Artificial intelligence: methodology, systems, and applications
Clustering of multiple microarray experiments using information integration
ITBAM'11 Proceedings of the Second international conference on Information technology in bio- and medical informatics
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Gene expression microarrays are the most commonly available source of high-throughput biological data. Each microarray experiment is supposed to measure the gene expression levels of a set of genes in a number of different experimental conditions or time points. Integration of results from different microarray experiments to the specific analysis is an important and yet challenging problem. Direct integration of microarrays is often ineffective because of the diverse types of experiment specific variations. In this paper, we propose a new hybrid method, which is specially suited for integration analysis of time series expression data across different experiments. The proposed algorithm utilizes Dynamic Time Warping (DTW) distance in order to measure the similarity between time expression profiles. First for each considered time series dataset a quadratic distance matrix that contains the DTW distances calculated between the expression profiles of each gene pair is built. Then using a hybrid aggregation algorithm the obtained DTW distance matrices are transformed into a single matrix, consisting of one overall DTW distance per each gene pair. The values of the resulting matrix can be interpreted as the consensus DTW distances supported by all the experiments. These may be further analyzed and help find the relationship among the genes. The proposed method is validated on gene expression time series data coming from two independent studies examining the global cell-cycle control of gene expression in fission yeast Schizosaccharomyces pombe.