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Pattern discovery on all three data sets produced a number of genes with very high levels of support. the range of support for the top 20 patterns for each data set was 23-35, 2234 and 21-34 for kmax 10, 15 and 20, respectively table a.1 & table a.2 ; . as might be expected, most of the patterns from all 3 data sets were poor classifiers of dlbcl subtypes. none of the supporting tissues were ever compose of more than 87% of either class, and the majority were less than 70%. although these patterns are not likely to be useful as classifiers, these patterns have value in indicating which genes would be poor classifiers of dlbcl subtypes. that is, given a list of candidate genes that could be used to classify subtypes of dlbcl, we would expect some of them to be genes found to have very high support across all tissue types. these genes would then be removed from the candidate list because they have poor discrimination. next the patterns were ordered by length table a.3 & table a.4 ; . the top 20 patterns in terms of the supported tissues for all three data sets were very similar, although the ordering was often different. there are four important notes to make about this data. one, not unexpectedly, the top patterns were supported by only 2 tissues. two, the longest patterns contained 76, 95 and 99 genes, for kmax 10, 15 and 20, respectively. this translates into 40.9%, 32.3% and 26.8% of the maximum possible number of genes. three, the majority 70% ; of the top patterns were supported by abc tissues. this would seem to indicate that the abc subtype is more tightly knit in terms of genetic expression than gcb when using this binning technique. four, it should be expected that all the top 20 longest patterns would have perfect discriminatory power between gcb and abc tissue types because a long pattern means that a large number of genes express similarly across that two or more tissues. however, for all three data sets this was not the case; there were always 3 patterns 15% ; that consisted of 2 tissues, one from each subgroup. these same three patterns were found in each of the three data sets and identifies the following pairs of tissues as cross-clustering: , and . these results will be discussed further below. however, it should be noted that, in data that has been grouped by cluster, tissues that are found at the boundary of a cluster are less correlated with the cluster than tissues that are found at the centre of the cluster.
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