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SEURAT:用于微阵列数据分析的集成的可视化分析。

SEURAT: visual analytics for the integrated analysis of microarray data.

机构信息

Department of Computer Oriented Statistics and Data Analysis, University of Augsburg, Universitätsstr, 14, 86159 Augsburg, Germany.

出版信息

BMC Med Genomics. 2010 Jun 3;3:21. doi: 10.1186/1755-8794-3-21.


DOI:10.1186/1755-8794-3-21
PMID:20525257
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC2893446/
Abstract

BACKGROUND: In translational cancer research, gene expression data is collected together with clinical data and genomic data arising from other chip based high throughput technologies. Software tools for the joint analysis of such high dimensional data sets together with clinical data are required. RESULTS: We have developed an open source software tool which provides interactive visualization capability for the integrated analysis of high-dimensional gene expression data together with associated clinical data, array CGH data and SNP array data. The different data types are organized by a comprehensive data manager. Interactive tools are provided for all graphics: heatmaps, dendrograms, barcharts, histograms, eventcharts and a chromosome browser, which displays genetic variations along the genome. All graphics are dynamic and fully linked so that any object selected in a graphic will be highlighted in all other graphics. For exploratory data analysis the software provides unsupervised data analytics like clustering, seriation algorithms and biclustering algorithms. CONCLUSIONS: The SEURAT software meets the growing needs of researchers to perform joint analysis of gene expression, genomical and clinical data.

摘要

背景:在转化癌症研究中,基因表达数据与临床数据以及其他基于芯片的高通量技术产生的基因组数据一起收集。需要用于联合分析此类高维数据集以及临床数据的软件工具。

结果:我们开发了一个开源软件工具,为高维基因表达数据与相关临床数据、阵列 CGH 数据和 SNP 阵列数据的综合分析提供了交互式可视化功能。不同的数据类型由一个综合数据管理器组织。为所有图形提供了交互式工具:热图、聚类树、条形图、直方图、事件图和染色体浏览器,显示基因组上的遗传变异。所有图形都是动态的,完全链接的,因此图形中选择的任何对象都会在所有其他图形中突出显示。对于探索性数据分析,该软件提供了无监督数据分析,如聚类、序列算法和双聚类算法。

结论:SEURAT 软件满足了研究人员联合分析基因表达、基因组和临床数据的需求。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/29ac/2893446/3616a369a904/1755-8794-3-21-3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/29ac/2893446/f2d02d1c8718/1755-8794-3-21-1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/29ac/2893446/be0b6513c512/1755-8794-3-21-2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/29ac/2893446/3616a369a904/1755-8794-3-21-3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/29ac/2893446/f2d02d1c8718/1755-8794-3-21-1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/29ac/2893446/be0b6513c512/1755-8794-3-21-2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/29ac/2893446/3616a369a904/1755-8794-3-21-3.jpg

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[1]
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Bioinformatics. 2009-8-4

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