Browsing by Author "Zhao, Po"
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Item In vivo filtering of in vitro MyoD target data: An approach for identification of biologically relevant novel downstream targets of transcription factors (2003)(2005) Zhao, Po; Seo, Jinwook; Wang, Zuyi; Wang, Yue; Shneiderman, Ben; Hoffman, Eric P.; ISRWe report a novel approach to identification of downstream targets of MyoD, where a published set of candidate targets from a well-controlled in vitro experiment [1] is filtered for relevance to muscle regeneration using a 27 time point in vivo murine regeneration series. Using both interactive hierarchical clustering (HCE) [2], and Bayes soft clustering (VISDA) [3,4]. We show that only a minority of in vitroefined candidates can be confirmed in vivo (~50% of induced transcripts, and none of repressed transcripts). The concordance of the in vitro, in vivo datasets, and both HCE and VISDA analytical techniques showed strong support for 18 targets (13 no vel) of MyoD that are biologically relevant during myoblast differentiation, including Cdh15, L-myc, Hes6, Stam, Tnnt2, Fyn, Rapsn, Nestin, Osp94, Pep4, Mef2a, Sh3glb1 and Rb1.Item Interactive Color Mosaic and Dendogram Displays for Signal/Noise Optimization in Microarray Data Analysis (2003)(2005) Seo, Jinwook; Bakay, Marina; Zhao, Po; Chen, Yi-Wen; Clarkson, Priscilla; Shneiderman, Ben; Hoffman, Eric P.; ISRData analysis and visualization is strongly influenced by noise and noise filters. There are multiple sources of oisein microarray data analysis, but signal/noise ratios are rarely optimized, or even considered. Here, we report a noise analysis of a novel 13 million oligonucleotide dataset - 25 human U133A (~500,000 features) profiles of patient muscle biposies. 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 orrectbiological groups (unsupervised clustering into three patient groups of known diagnosis). We varied probe set interpretation methods (MAS 5.0, RMA), resent callfilters, and clustering linkage methods, and investigated the results in HCE. HCE 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 noise 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.