Division of Hematology/Oncology, Department of Pediatric Oncology, Dana-Farber Cancer Institute, Children's Hospital Boston and Harvard Medical School, Boston, Massachusetts, United States of America.
PLoS One. 2012;7(2):e31690. doi: 10.1371/journal.pone.0031690. Epub 2012 Feb 29.
Increasing use of high throughput genomic scale assays requires effective visualization and analysis techniques to facilitate data interpretation. Moreover, existing tools often require programming skills, which discourages bench scientists from examining their own data. We have created iCanPlot, a compelling platform for visual data exploration based on the latest technologies. Using the recently adopted HTML5 Canvas element, we have developed a highly interactive tool to visualize tabular data and identify interesting patterns in an intuitive fashion without the need of any specialized computing skills. A module for geneset overlap analysis has been implemented on the Google App Engine platform: when the user selects a region of interest in the plot, the genes in the region are analyzed on the fly. The visualization and analysis are amalgamated for a seamless experience. Further, users can easily upload their data for analysis--which also makes it simple to share the analysis with collaborators. We illustrate the power of iCanPlot by showing an example of how it can be used to interpret histone modifications in the context of gene expression.
高通量基因组规模分析的应用越来越广泛,这就需要有效的可视化和分析技术来帮助解释数据。此外,现有的工具通常需要编程技能,这使得实验科学家不愿意去检查自己的数据。我们开发了 iCanPlot,这是一个基于最新技术的引人注目的可视化数据探索平台。我们使用了最近采用的 HTML5 Canvas 元素,开发了一个高度交互的工具,可以直观地可视化表格数据并识别有趣的模式,而无需任何专门的计算技能。在 Google App Engine 平台上实现了基因集重叠分析模块:当用户在绘图中选择感兴趣的区域时,会实时分析该区域中的基因。可视化和分析融合在一起,提供了无缝的体验。此外,用户可以轻松地上传他们的数据进行分析——这也使得与合作者共享分析变得简单。我们通过展示一个如何在基因表达背景下解释组蛋白修饰的例子来说明 iCanPlot 的强大功能。