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.
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.
Authors: Robert C Gentleman; Vincent J Carey; Douglas M Bates; Ben Bolstad; Marcel Dettling; Sandrine Dudoit; Byron Ellis; Laurent Gautier; Yongchao Ge; Jeff Gentry; Kurt Hornik; Torsten Hothorn; Wolfgang Huber; Stefano Iacus; Rafael Irizarry; Friedrich Leisch; Cheng Li; Martin Maechler; Anthony J Rossini; Gunther Sawitzki; Colin Smith; Gordon Smyth; Luke Tierney; Jean Y H Yang; Jianhua Zhang Journal: Genome Biol Date: 2004-09-15 Impact factor: 13.583
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Authors: Ashraf Ul Kabir; Madhav Subramanian; Dong Hun Lee; Xiaoli Wang; Karen Krchma; Jun Wu; Teri Naismith; Carmen M Halabi; Ju Young Kim; Fadi E Pulous; Brian G Petrich; Suhyun Kim; Hae-Chul Park; Phyllis I Hanson; Hua Pan; Samuel A Wickline; Daved H Fremont; Changwon Park; Kyunghee Choi Journal: Sci Transl Med Date: 2021-03-03 Impact factor: 17.956
Authors: Juliet Mwirigi; Moeno Kume; Shayne N Hassler; Ayesha Ahmad; Pradipta R Ray; Changyu Jiang; Alexander Chamessian; Nakleh Mseeh; Breya P Ludwig; Benjamin D Rivera; Marvin T Nieman; Thomas Van de Ven; Ru-Rong Ji; Gregory Dussor; Scott Boitano; Josef Vagner; Theodore J Price Journal: J Pain Date: 2021-01-09 Impact factor: 5.383
Authors: Raphael D Isokpehi; Udensi K Udensi; Matthew N Anyanwu; Andreas N Mbah; Matilda O Johnson; Kafui Edusei; Michael A Bauer; Roger A Hall; Omotayo R Awofolu Journal: Biomark Insights Date: 2012-10-15