Viewing the Larger Context of Genomic Data through Horizontal Integration

Matthew A. Hibbs, Grant Wallace, Maitreya J. Dunham, Kai Li, O. Troyanskaya
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引用次数: 17

Abstract

Genomics is an important emerging scientific field that relies on meaningful data visualization as a key step in analysis. Specifically, most investigation of gene expression microarray data is performed using visualization techniques. However, as microarrays become more ubiquitous, researchers must analyze their own data within the context of previously published work in order to gain a more complete understanding. No current method for microarray visualization and analysis enables biology researchers to observe the greater context of data that surrounds their own results, which severely limits the ability of researchers draw novel conclusions. Here we present a system, called HIDRA, that visually integrates the simultaneous display of multiple microarray datasets to identify important parallels and dissimilarities. We demonstrate the power of our approach through examples of real-world biological insights that can be observed using HIDRA that are not apparent using other techniques.
通过横向整合查看基因组数据的更大背景
基因组学是一个重要的新兴科学领域,它依赖于有意义的数据可视化作为分析的关键步骤。具体来说,基因表达微阵列数据的大多数调查是使用可视化技术进行的。然而,随着微阵列变得越来越普遍,研究人员必须在先前发表的工作背景下分析他们自己的数据,以便获得更全面的理解。目前没有一种微阵列可视化和分析方法能够使生物学研究人员观察到围绕他们自己的结果的更大的数据背景,这严重限制了研究人员得出新结论的能力。在这里,我们提出了一个系统,称为HIDRA,视觉集成多个微阵列数据集的同时显示,以识别重要的相似之处和不同之处。我们通过使用HIDRA可以观察到的现实世界生物学见解的例子来展示我们方法的力量,而使用其他技术则不明显。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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