Mayday--integrative analytics for expression data.
Mayday--integrative analytics for expression data.
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DOI：
10.1186/1471-2105-11-121
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发表时间：
2010-03-09
影响因子：
3
通讯作者：
Nieselt K
中科院分区：
文献类型：
--
作者：
Battke F;Symons S;Nieselt K
DNA 微阵列已成为大规模基因表达和表观基因组分析的标准方法。生成数据的日益复杂性和固有的噪声使得可视化数据探索变得更加重要。新方法的快速部署以及预定义的、易于应用的方法与程序员对数据的访问的组合是任何分析框架的重要要求。 Mayday 是一个专注于可视化数据探索和分析的开源平台。提供了许多用于聚类、机器学习和分类的内置方法来剖析复杂的数据集。可以轻松编写插件，以多种方式扩展 Mayday 的功能。作为Java程序，Mayday是平台无关的，无需任何安装即可用作Java WebStart应用程序。 Mayday 可以从多种文件格式导入数据，包括数据库连接以实现高效的数据组织。许多交互式可视化工具（包括箱线图、剖面图、主成分图和热图）都可以使用元数据进行增强，并导出为出版物质量矢量文件。我们重写了五月天核心的大部分内容，使其更加高效并为未来的发展做好准备。大量新插件包括自动化处理框架、动态过滤、新的高效聚类方法、机器学习模块和数据库连接。可以使用内置的 R 终端和集成的 SQL 查询接口来完成广泛的手动数据分析。我们的可视化框架变得更加强大，添加了新的绘图类型并改进了现有绘图。我们提出了 Mayday 的主要扩展，这是一个非常通用的开源框架，用于为生物学家和生物信息学家设计的高效微阵列数据分析。大多数日常任务已经涵盖。大量可用插件以及使用编译插件和临时脚本进行扩展的可能性使 Mayday 能够快速适应非常专业的数据探索。 Mayday 可在 http://microarray-analysis.org 上获取。
DNA Microarrays have become the standard method for large scale analyses of gene expression and epigenomics. The increasing complexity and inherent noisiness of the generated data makes visual data exploration ever more important. Fast deployment of new methods as well as a combination of predefined, easy to apply methods with programmer's access to the data are important requirements for any analysis framework. Mayday is an open source platform with emphasis on visual data exploration and analysis. Many built-in methods for clustering, machine learning and classification are provided for dissecting complex datasets. Plugins can easily be written to extend Mayday's functionality in a large number of ways. As Java program, Mayday is platform-independent and can be used as Java WebStart application without any installation. Mayday can import data from several file formats, database connectivity is included for efficient data organization. Numerous interactive visualization tools, including box plots, profile plots, principal component plots and a heatmap are available, can be enhanced with metadata and exported as publication quality vector files. We have rewritten large parts of Mayday's core to make it more efficient and ready for future developments. Among the large number of new plugins are an automated processing framework, dynamic filtering, new and efficient clustering methods, a machine learning module and database connectivity. Extensive manual data analysis can be done using an inbuilt R terminal and an integrated SQL querying interface. Our visualization framework has become more powerful, new plot types have been added and existing plots improved. We present a major extension of Mayday, a very versatile open-source framework for efficient micro array data analysis designed for biologists and bioinformaticians. Most everyday tasks are already covered. The large number of available plugins as well as the extension possibilities using compiled plugins and ad-hoc scripting allow for the rapid adaption of Mayday also to very specialized data exploration. Mayday is available at http://microarray-analysis.org.
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影响因子：
3
作者：
Dondrup, Michael;Albaum, Stefan P.;Griebel, Thasso;Henckel, Kolja;Juenemann, Sebastian;Kahlke, Tim;Kleindt, Christiane K.;Kuester, Helge;Linke, Burkhard;Mertens, Dominik;Mittard-Runte, Virginie;Neuweger, Heiko;Runte, Kai J.;Tauch, Andreas;Tille, Felix;Puehler, Alfred;Goesmann, Alexander
通讯作者：
Goesmann, Alexander
影响因子：
3
作者：
Cote, Richard G;Jones, Philip;Martens, Lennart;Kerrien, Samuel;Reisinger, Florian;Lin, Quan;Leinonen, Rasko;Apweiler, Rolf;Hermjakob, Henning
通讯作者：
Hermjakob, Henning
从微阵列数据中检测出差异表达基因的加权平均差异方法。
影响因子：
1
作者：
Kadota, Koji;Nakai, Yuji;Shimizu, Kentaro
通讯作者：
Shimizu, Kentaro
影响因子：
14.9
作者：
Kanehisa M;Araki M;Goto S;Hattori M;Hirakawa M;Itoh M;Katayama T;Kawashima S;Okuda S;Tokimatsu T;Yamanishi Y
通讯作者：
Yamanishi Y
DOI：
10.1073/pnas.091062498
发表时间：
2001-04-24
影响因子：
11.1
作者：
Tusher, VG;Tibshirani, R;Chu, G
通讯作者：
Chu, G