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Microarray gene expression profiling is performed in many laboratories, resulting in the rapid data accumulation in public repositories. However, due to the existence of different microarray platforms and the lack of standard experimental protocols, systematic variation among data sets often exceeds the capability of statistical normalization. Currently, there is an urgent need for methods and tools to integrate the enormous amount of microarray data. Integrative Array Analyzer, "iArray" in short, is developed to address this need.

iArray is a data mining and visualization software platform for the integrative analysis of multiple cross-platform microarray datasets. Due to the noisy nature of microarray data, identifying recurrent signals across several datasets could enhance signal to noise separation, and allow us to draw biological inference with higher confidence

In brief, we employ a meta-analysis approach to first derive the expression pattern from each individual microarray dataset, then search for patterns frequently occurring across multiple datasets. Typical analyses include: co-expression analysis and differential expression analysis. In Co-expression analysis, we model each data set as a correlation graph, where one vertex represents a gene and two co-expressed genes are connected with an edge. Given k microarray data sets, we will derive k graphs, on which we identify recurrent subnetwork patterns. Although such data mining task is not trivia for many large graphs, our recently developed graph algorithm can efficiently handle the task. In differential expression analysis, we first identify genes differentially expressed in each microarray data set, and then use the frequent item set mining algorithm to identify sets of genes simultaneously differentially expressed across multiple data sets. In addition, iArray can also be used to identify conserved expression patterns across different species.