Microarray analysis using pattern discovery

Abstract

Analysis of gene expression microarray data has traditionally been conducted using hierarchical clustering. However, such analysis has many known disadvantages and pattern discovery (PD) has been proposed as an alternative technique. In this work, three similar but different PD algorithms – Teiresias, Splash and Genes@Work – were benchmarked for time and memory efficiency on a small yeast cell-cycle data set. Teiresias was found to be the fastest, and best over-all program. However, Splash was more memory efficient. This work also investigated the performance of four methods of discretizing microarray data: sign-of-the-derivative, K-means, pre-set value, and Genes@Work stratification. The first three methods were evaluated on their predisposition to group together biologically related genes. On a yeast cell-cycle data set, sign-of-the-derivative method yielded the most biologically significant patterns, followed by the pre-set value and K-means methods. K-means, preset-value, and Genes@Work were also compared on their ability to classify tissue samples from diffuse large b-cell lymphoma (DLBCL) into two subtypes determined by standard techniques. The Genes@Work stratification method produced the best patterns for discriminating between the two subtypes of lymphoma. However, the results from the second-best method, K-means, call into question the accuracy of the classification by the standard technique. Finally, a number of recommendations for improvement of pattern discovery algorithms and discretization techniques are made.