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- W2126926732 abstract "Data analysis and visualization is strongly influenced by and filters. There are multiple sources of noise in microarray data analysis, but signal/noise ratios are rarely optimized, or even considered. Here, we report a analysis of a novel 13 million oligonucleotide dataset - 25 human U133A (/spl sim/500,000 features) profiles of patient muscle biopsies. We use our recently described interactive visualization tool, the hierarchical clustering explorer (HCE) to systemically address the effect of different filters on resolution of arrays into correct biological groups (unsupervised clustering into three patient groups of known diagnosis). We varied probe set interpretation methods (MAS 5.0, RMA), filters, and clustering linkage methods, and investigated the results in HCE. HCE's interactive features enabled us to quickly see the impact of these three variables. Dendrogram displays showed the clustering results systematically, and color mosaic displays provided a visual support for the results. We show that each of these three variables has a strong effect on unsupervised clustering. For this dataset, the strength of the biological variable was maximized, and minimized, using MAS 5.0, 10% present call filter, and average group linkage. We propose a general method of using interactive tools to identify the optimal signal/noise balance or the optimal combination of these three variables to maximize the effect of the desired biological variable on data interpretation." @default.
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- W2126926732 date "2003-01-01" @default.
- W2126926732 modified "2023-09-23" @default.
- W2126926732 title "Interactive color mosaic and dendrogram displays for signal/noise optimization in microarray data analysis" @default.
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- W2126926732 doi "https://doi.org/10.1109/icme.2003.1221348" @default.
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