?he first two authors contributed equally to this manuscript Data analysis and visualization is strongly influenced by noise and noise filters. There are multiple sources of “noise ” in microarray data analysis, but signallnoise ratios are rarely optimized, or even considered. Here, we report a noise analysis of a novel 13 million oligonucleotide dataset- 25 human U 133A (-500,000 features) profiles of patient muscle hiposies. We use our recently described interactive visualization tool, the Hierarchical Clustering Explorer (HCE) to systemically address the effect of different noise filters on resolution of arrays into ’bxrect ” biological groups (unsupervised clustering into three patient groups of known diagnosis). We varied probe set interpretation methods (MAS 5.0, RMA), “present call ” filters, and clustering linkage methods, and investigated the results in HCE. HCE’s interactive feature
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