BMC Bioinformatics (Jan 2019)

BPG: Seamless, automated and interactive visualization of scientific data

  • Christine P’ng,
  • Jeffrey Green,
  • Lauren C. Chong,
  • Daryl Waggott,
  • Stephenie D. Prokopec,
  • Mehrdad Shamsi,
  • Francis Nguyen,
  • Denise Y. F. Mak,
  • Felix Lam,
  • Marco A. Albuquerque,
  • Ying Wu,
  • Esther H. Jung,
  • Maud H. W. Starmans,
  • Michelle A. Chan-Seng-Yue,
  • Cindy Q. Yao,
  • Bianca Liang,
  • Emilie Lalonde,
  • Syed Haider,
  • Nicole A. Simone,
  • Dorota Sendorek,
  • Kenneth C. Chu,
  • Nathalie C. Moon,
  • Natalie S. Fox,
  • Michal R. Grzadkowski,
  • Nicholas J. Harding,
  • Clement Fung,
  • Amanda R. Murdoch,
  • Kathleen E. Houlahan,
  • Jianxin Wang,
  • David R. Garcia,
  • Richard de Borja,
  • Ren X. Sun,
  • Xihui Lin,
  • Gregory M. Chen,
  • Aileen Lu,
  • Yu-Jia Shiah,
  • Amin Zia,
  • Ryan Kearns,
  • Paul C. Boutros

DOI
https://doi.org/10.1186/s12859-019-2610-2
Journal volume & issue
Vol. 20, no. 1
pp. 1 – 5

Abstract

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Abstract Background We introduce BPG, a framework for generating publication-quality, highly-customizable plots in the R statistical environment. Results This open-source package includes multiple methods of displaying high-dimensional datasets and facilitates generation of complex multi-panel figures, making it suitable for complex datasets. A web-based interactive tool allows online figure customization, from which R code can be downloaded for integration with computational pipelines. Conclusion BPG provides a new approach for linking interactive and scripted data visualization and is available at http://labs.oicr.on.ca/boutros-lab/software/bpg or via CRAN at https://cran.r-project.org/web/packages/BoutrosLab.plotting.general

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