Discovering Connected Patterns in Gene Expression Arrays

Clustering methods have been extensively used for gene expression data analysis to detect groups of related genes. The clusters provide useful information to analyze gene function, gene regulation and cellular patterns. Most existing clustering algorithms, though, discover only coherent gene expression patterns, and do not handle connected patterns. Coherent and connected patterns correspond to globular and arbitrary shaped clusters, respectively, in low dimensional spaces. For high dimensional gene expression data, two connected patterns can be two similar patterns with time lags in a time series data, or in general, two different patterns that are connected by an intermediate pattern that is related to both of them. Discovering such connected patterns has important biological implications not revealed by groups of coherent patterns. In this paper, a novel algorithm that finds connected patterns, in gene expression data, is proposed. Using a novel merge criterion, it can distinguish clusters based on distances between patterns, thus avoiding the effect of noise and outliers. Moreover, the algorithm uses a metric based on Pearson correlation to find neighbours, which renders it a lower complexity than related algorithms. Both time series and non temporal gene expression data sets are used to illustrate the efficiency of the proposed algorithm. Results on the serum and the leukaemia data sets reveal interesting biologically significant information