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Analysis of Microarray Data

clearly segregate into either mRNA or aRNA groups. It is important to note, for those genes that do segregate, the gene expression differences (ratios) do not change direction (i.e. RKO>HCT to HCT>RKO), but show greater differences in the aRNA samples compared to the mRNA samples (as determined by the color shade).

Figure 4. Hierarchical Clustering Analysis of Selected Array Elements. Hierarchical clustering analysis of a few select genes (91 of 6912) that are very different between the aRNA and mRNA samples. This analysis was carried out using the weighted average (WPGMA) method.


An alternate methodology used to understand the clustering of microarray data is k-means clustering. This method does not suffer from some of the problems associated with hierarchical clustering such as irrelevance of gene expression data as clustering progresses or spurious results due to errors in assigning clusters initially in the analysis (2). K-means clustering of all the elements of the HCI arrays with 6 clusters was determined (Figure 5). After 45 iterations, a total score of 1.082e+004 was computed. The most similar "similarity value" was 0 and the least similar "similarity value" was 1.798e+308. This grouping of genes to identify sets of genes that appear to be differentially expressed between aRNA and mRNA resulted in two clusters (91 elements among clusters 5 and 6) that have the largest difference between aRNA and mRNA (Figure 6).

Figure 5. K- means cluster analysis of the 6912 elements using a userdefined cluster number of 6. 45 iterations were carried out to group the genes within a given cluster using a data centroid based search. The
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