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BPG: Seamless, automated and interactive visualization of scientific data

Authors :
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
Source :
BMC Bioinformatics, Vol 20, Iss 1, Pp 1-5 (2019)
Publication Year :
2019
Publisher :
BMC, 2019.

Abstract

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

Details

Language :
English
ISSN :
14712105 and 55750230
Volume :
20
Issue :
1
Database :
Directory of Open Access Journals
Journal :
BMC Bioinformatics
Publication Type :
Academic Journal
Accession number :
edsdoj.4314e3a47df049aeb55750230ad05c88
Document Type :
article
Full Text :
https://doi.org/10.1186/s12859-019-2610-2