Exploring and visualizing multidimensional data in translational research platforms.
Exploring and visualizing multidimensional data in translational research platforms.
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DOI：
10.1093/bib/bbw080
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发表时间：
2017-11-01
影响因子：
9.5
通讯作者：
Rance B
中科院分区：
文献类型：
--
作者：
Dunn W Jr;Burgun A;Krebs MO;Rance B
过去几年来，技术和科学研究取得了前所未有的进展，为科学界提供了新的和更复杂的数据形式。从单个组或跨机构联盟收集的大型数据集包含数百个组学和临床变量，对应于数千名患者，在研究环境中变得越来越普遍。在进行任何核心分析之前，可视化通常在研究的初始阶段起着关键作用，特别是对于没有初始假设占主导地位的项目。在高水平上对数据进行适当的可视化有助于研究人员发现趋势，识别异常值和进行质量检查的能力。此外，研究还揭示了可视化在数据分析中的重要作用及其隐含的好处，有助于我们了解疾病并最终改善患者护理。在这项工作中，我们提出了一个审查的现有工具，旨在促进可视化的多维数据在转化研究平台的当前景观。具体来说，我们回顾了翻译平台的生物医学文献，允许可视化和探索临床和组学数据，并确定了11个平台：cBioPortal，交互式基因组学患者分层浏览器，Igloo-Plot，乔治城癌症数据库Plus，transSMART，一种支持异构数据的基于未命名数据立方体的模型，Papilio，Caleydo Domino，Qlucore Omics，Oracle Health Sciences Translational Research Center和OmicsOffice®由TIBCO Spotfire提供支持。在卫生部门，来自各种来源的数据不断增加，用于更好地掌握这些数据的可视化工具将变得越来越重要，我们相信我们的工作将有助于指导类似情况下的调查人员。
The unprecedented advances in technology and scientific research over the past few years have provided the scientific community with new and more complex forms of data. Large data sets collected from single groups or cross-institution consortiums containing hundreds of omic and clinical variables corresponding to thousands of patients are becoming increasingly commonplace in the research setting. Before any core analyses are performed, visualization often plays a key role in the initial phases of research, especially for projects where no initial hypotheses are dominant. Proper visualization of data at a high level facilitates researcher’s abilities to find trends, identify outliers and perform quality checks. In addition, research has uncovered the important role of visualization in data analysis and its implied benefits facilitating our understanding of disease and ultimately improving patient care. In this work, we present a review of the current landscape of existing tools designed to facilitate the visualization of multidimensional data in translational research platforms. Specifically, we reviewed the biomedical literature for translational platforms allowing the visualization and exploration of clinical and omics data, and identified 11 platforms: cBioPortal, interactive genomics patient stratification explorer, Igloo-Plot, The Georgetown Database of Cancer Plus, tranSMART, an unnamed data-cube-based model supporting heterogeneous data, Papilio, Caleydo Domino, Qlucore Omics, Oracle Health Sciences Translational Research Center and OmicsOffice® powered by TIBCO Spotfire. In a health sector continuously witnessing an increase in data from multifarious sources, visualization tools used to better grasp these data will grow in their importance, and we believe our work will be useful in guiding investigators in similar situations.
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影响因子：
7.3
作者：
Gao J;Aksoy BA;Dogrusoz U;Dresdner G;Gross B;Sumer SO;Sun Y;Jacobsen A;Sinha R;Larsson E;Cerami E;Sander C;Schultz N
通讯作者：
Schultz N
DOI：
10.1073/pnas.1424409112
发表时间：
2015-04-28
影响因子：
11.1
作者：
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通讯作者：
Dorrestein, Pieter C.
影响因子：
16.6
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通讯作者：
Gimenez-Roqueplo, Anne-Paule
影响因子：
9.7
作者：
Bates, David W.;Saria, Suchi;Escobar, Gabriel
通讯作者：
Escobar, Gabriel
影响因子：
3.7
作者：
Galanaud P;Galanaud A;Giraudoux P
通讯作者：
Giraudoux P