book.bib

@Book{xie2015,
  title = {Dynamic Documents with {R} and knitr},
  author = {Yihui Xie},
  publisher = {Chapman and Hall/CRC},
  address = {Boca Raton, Florida},
  year = {2015},
  edition = {2nd},
  note = {ISBN 978-1498716963},
  url = {http://yihui.org/knitr/},
}


@article{perez-silvaNVennGeneralizedQuasiproportional2018,
  title = {{{nVenn}}: Generalized, Quasi-Proportional {{Venn}} and {{Euler}} Diagrams},
  shorttitle = {{{nVenn}}},
  author = {{P{\'e}rez-Silva}, Jos{\'e} G. and {Araujo-Voces}, Miguel and Quesada, V{\'i}ctor},
  year = {2018},
  month = jul,
  volume = {34},
  pages = {2322--2324},
  issn = {1367-4811},
  doi = {10.1093/bioinformatics/bty109},
  abstract = {Motivation: Venn and Euler diagrams are extensively used for the visualization of relationships between experiments and datasets. However, representing more than three datasets while keeping the proportions of each region is still not feasible with existing tools. Results: We present an algorithm to render all the regions of a generalized n-dimensional Venn diagram, while keeping the area of each region approximately proportional to the number of elements included. In addition, missing regions in Euler diagrams lead to simplified representations. The algorithm generates an n-dimensional Venn diagram and inserts circles of given areas in each region. Then, the diagram is rearranged with a dynamic, self-correcting simulation in which each set border is contracted until it contacts the circles inside. This algorithm is implemented in a C++\,tool (nVenn) with or without a web interface. The web interface also provides the ability to analyze the regions of the diagram. Availability and implementation: The source code and pre-compiled binaries of nVenn are available at https://github.com/vqf/nVenn. A web interface for up to six sets can be accessed at http://degradome.uniovi.es/cgi-bin/nVenn/nvenn.cgi. Supplementary information: Supplementary data are available at Bioinformatics online.},
  journal = {Bioinformatics (Oxford, England)},
  keywords = {Algorithms,ggVennDiagram引文,Software},
  language = {eng},
  number = {13},
  pmid = {29949954}
}



@article{conwayUpSetRPackageVisualization2017,
  title = {{{UpSetR}}: An {{R}} Package for the Visualization of Intersecting Sets and Their Properties},
  shorttitle = {{{UpSetR}}},
  author = {Conway, Jake R and Lex, Alexander and Gehlenborg, Nils},
  year = {2017},
  month = sep,
  volume = {33},
  pages = {2938--2940},
  issn = {1367-4803},
  doi = {10.1093/bioinformatics/btx364},
  abstract = {Venn and Euler diagrams are a popular yet inadequate solution for quantitative visualization of set intersections. A scalable alternative to Venn and Euler diagrams for visualizing intersecting sets and their properties is needed.We developed UpSetR, an open source R package that employs a scalable matrix-based visualization to show intersections of sets, their size, and other properties.UpSetR is available at https://github.com/hms-dbmi/UpSetR/ and released under the MIT License. A Shiny app is available at https://gehlenborglab.shinyapps.io/upsetr/.Supplementary data are available at Bioinformatics online.},
  journal = {Bioinformatics},
  keywords = {ggVennDiagram引文},
  number = {18}
}

@book{ggplot22016,
  title = {Ggplot2: {{Elegant}} Graphics for Data Analysis},
  author = {Wickham, Hadley},
  year = {2016},
  publisher = {{Springer-Verlag New York}},
  isbn = {978-3-319-24277-4},
  keywords = {ggVennDiagram引文}
}


@article{lexUpSetVisualizationIntersecting2014,
  title = {{{UpSet}}: {{Visualization}} of {{Intersecting Sets}}},
  shorttitle = {{{UpSet}}},
  author = {Lex, A. and Gehlenborg, N. and Strobelt, H. and Vuillemot, R. and Pfister, H.},
  year = {2014},
  month = dec,
  volume = {20},
  pages = {1983--1992},
  issn = {1941-0506},
  doi = {10.1109/TVCG.2014.2346248},
  abstract = {Understanding relationships between sets is an important analysis task that has received widespread attention in the visualization community. The major challenge in this context is the combinatorial explosion of the number of set intersections if the number of sets exceeds a trivial threshold. In this paper we introduce UpSet, a novel visualization technique for the quantitative analysis of sets, their intersections, and aggregates of intersections. UpSet is focused on creating task-driven aggregates, communicating the size and properties of aggregates and intersections, and a duality between the visualization of the elements in a dataset and their set membership. UpSet visualizes set intersections in a matrix layout and introduces aggregates based on groupings and queries. The matrix layout enables the effective representation of associated data, such as the number of elements in the aggregates and intersections, as well as additional summary statistics derived from subset or element attributes. Sorting according to various measures enables a task-driven analysis of relevant intersections and aggregates. The elements represented in the sets and their associated attributes are visualized in a separate view. Queries based on containment in specific intersections, aggregates or driven by attribute filters are propagated between both views. We also introduce several advanced visual encodings and interaction methods to overcome the problems of varying scales and to address scalability. UpSet is web-based and open source. We demonstrate its general utility in multiple use cases from various domains.},
  journal = {IEEE Transactions on Visualization and Computer Graphics},
  keywords = {ggVennDiagram引文},
  number = {12}
}