BPG: Seamless, automated and interactive visualization of scientific data

oleh: 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

Format: Article
Diterbitkan: BMC 2019-01-01

Deskripsi

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