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      VennDiagram: a package for the generation of highly-customizable Venn and Euler diagrams in R

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      1 , 1 ,
      BMC Bioinformatics
      BioMed Central

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          Abstract

          Background

          Visualization of orthogonal (disjoint) or overlapping datasets is a common task in bioinformatics. Few tools exist to automate the generation of extensively-customizable, high-resolution Venn and Euler diagrams in the R statistical environment. To fill this gap we introduce VennDiagram, an R package that enables the automated generation of highly-customizable, high-resolution Venn diagrams with up to four sets and Euler diagrams with up to three sets.

          Results

          The VennDiagram package offers the user the ability to customize essentially all aspects of the generated diagrams, including font sizes, label styles and locations, and the overall rotation of the diagram. We have implemented scaled Venn and Euler diagrams, which increase graphical accuracy and visual appeal. Diagrams are generated as high-definition TIFF files, simplifying the process of creating publication-quality figures and easing integration with established analysis pipelines.

          Conclusions

          The VennDiagram package allows the creation of high quality Venn and Euler diagrams in the R statistical environment.

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          Most cited references7

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          VennMaster: Area-proportional Euler diagrams for functional GO analysis of microarrays

          Background Microarray experiments generate vast amounts of data. The functional context of differentially expressed genes can be assessed by querying the Gene Ontology (GO) database via GoMiner. Directed acyclic graph representations, which are used to depict GO categories enriched with differentially expressed genes, are difficult to interpret and, depending on the particular analysis, may not be well suited for formulating new hypotheses. Additional graphical methods are therefore needed to augment the GO graphical representation. Results We present an alternative visualization approach, area-proportional Euler diagrams, showing set relationships with semi-quantitative size information in a single diagram to support biological hypothesis formulation. The cardinalities of sets and intersection sets are represented by area-proportional Euler diagrams and their corresponding graphical (circular or polygonal) intersection areas. Optimally proportional representations are obtained using swarm and evolutionary optimization algorithms. Conclusion VennMaster's area-proportional Euler diagrams effectively structure and visualize the results of a GO analysis by indicating to what extent flagged genes are shared by different categories. In addition to reducing the complexity of the output, the visualizations facilitate generation of novel hypotheses from the analysis of seemingly unrelated categories that share differentially expressed genes.
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            How to visually interpret biological data using networks.

            Networks in biology can appear complex and difficult to decipher. We illustrate how to interpret biological networks with the help of frequently used visualization and analysis patterns.
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              Unsupervised pattern recognition: an introduction to the whys and wherefores of clustering microarray data.

              Clustering has become an integral part of microarray data analysis and interpretation. The algorithmic basis of clustering -- the application of unsupervised machine-learning techniques to identify the patterns inherent in a data set -- is well established. This review discusses the biological motivations for and applications of these techniques to integrating gene expression data with other biological information, such as functional annotation, promoter data and proteomic data.
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                Author and article information

                Journal
                BMC Bioinformatics
                BMC Bioinformatics
                BioMed Central
                1471-2105
                2011
                26 January 2011
                : 12
                : 35
                Affiliations
                [1 ]Informatics and Biocomputing Platform, Ontario Institute for Cancer Research, MaRS Centre, South Tower, 101 College Street, Suite 800, Toronto, Ontario, M5G 0A3, Canada
                Article
                1471-2105-12-35
                10.1186/1471-2105-12-35
                3041657
                21269502
                e516a657-9a2d-419b-acfc-9c75cbff58c3
                Copyright ©2011 Chen and Boutros; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 22 July 2010
                : 26 January 2011
                Categories
                Software

                Bioinformatics & Computational biology
                Bioinformatics & Computational biology

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