BPG: Seamless, automated and interactive visualization of scientific data
We introduce BPG, a framework for generating publication-quality, highly-customizable plots in the R statistical environment.
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.
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
KeywordsData-visualization Interactive plotting Software Web-resources
Application Programming Interface
Copy Number Aberration
Comprehensive R Archive Network
Dots per Inch
Graphical User Interface
Single Nucleotide Variant
Biological experiments are increasingly generating large, multifaceted datasets. Exploring such data and communicating observations is, in turn, growing more difficult and the need for robust scientific data-visualization is accelerating [1, 2, 3, 4]. Myriad data visualization tools exist, particularly as web-based interfaces and local software packages. Unfortunately these often do not integrate easily into R-based statistical pipelines, such as the widely used Bioconductor . Within R, many visualization packages exist, including base graphics , ggplot2 , lattice , Sushi , circlize , multiDimBio , NetBioV , GenomeGraphs  and ggbio . There is also a broad range of activity-specific visualization packages focused on specific tasks or analysis-types [15, 16, 17, 18, 19, 20, 21, 22, 23, 24]. Some of these lack publication-quality defaults such as high-resolution, appropriate label-sizing and default colour palettes appropriate for gray-scale use and visible for those with red-green colour-blindness. Many can require significant parameterization. Others contain limited plot types, provide limited scope for automatic generation of multi-panel figures or are constrained to specific data-types. Few allow interactive visualization, where specific plot elements can be highlighted and the set of parameters available to customize them automatically identified and allowing interactive generation of R code through a GUI interface that visualizes plot changes in real-time. Thus while each of these visualization packages has significant value and user-bases, each lacks some features beneficial for computational biologists and data scientists.
Good visualization software must create a wide variety of chart-types in order to match the diversity of data-types available. It should provide flexible parametrization for highly customized figures and allow for multiple output formats while employing reasonable, publication-appropriate default settings, such as producing high resolution output. In addition, it should integrate seamlessly with existing computational pipelines while also providing an easily intuitive, interactive mode. There should be an ability to transition between pipeline and interactive mode, allowing cyclical development. Finally, good design principles should be encouraged, such as suggesting appropriate color choices and layouts for specific use-cases. To help users quickly gain proficiency, detailed examples, tutorials, an ability for real-time interactive plot-tuning and an application programming interface (API) are required. To date, no existing visualization suite fully fills these needs.
To facilitate rapid graphical prototyping, an online interactive plotting interface was created (http://bpg.oicr.on.ca). This interface allows users to easily and rapidly see the results of adjusting parameter values, thereby encouraging precise improvement of plot aesthetics. The R code generated by this interface is also made available for download, as is a methods paragraph allowing careful reporting of plotting options. A public web-interface is available, and local interfaces can be easily created.
A number of utility functions in BPG assist in plot optimization, such as producing legends and covariate bars, or formatting text with scientific notation for p-values. One difficult step in creating figures is the selection of color schemes that are both pleasing and interpretable [25, 26]. BPG provides a suite of 45 color palettes including qualitative, sequential, and diverging color schemes , shown in Additional file 5: Figure S2. Many optimized color schemes exist for numerous use cases including tissue types, chromosomes and mutation types. The default.colors function produces a warning when a requested color scheme is not grey-scale compatible, a common concern for figures reproduced in black and white. This is determined by converting each color to a grey value between 1 and 100, and indicating differences of < 10 as not grey-scale compatible to approximate a color scheme’s visibility when printed in grey-scale. To facilitate reproducibility, image metadata is automatically generated for all plots, creating descriptors such as software and operating system versions.
Extensive documentation is provided to help new users learn how to use BPG. To assist researchers in determining which chart-type is appropriate for their dataset, we provide plotting examples in the documentation which are derived from a real dataset and a plotting guide is included to explain the intended use-case of each function. This guide also contains explanations of typography, basic color theory and layout design which help to improve the design of figures [28, 29]. In addition, an online API is available with both simple and complex use-case examples for each plot-type to help users quickly learn the range of functionality available.
BPG has been used in over 60 publications to date (Additional file 6: Table S1) [30, 31, 32, 33, 34, 35]. These plotting functions have been integrated into numerous R analysis pipelines for automated figure generation as part of the analysis of large –omic data. The plots created by this package are reproducible and maintain a consistent aesthetic. We believe that BPG will facilitate improved visualization and communication of complex datasets.
The authors thank all members of the Boutros lab for their assistance in testing and improving the software and our many collaborators for their suggestions and support, particular Drs. Robert Bristow, Michael Fraser, Raimo Pohjanvirta, Allan Okey and Linda Penn. We thank the OICR webdev and systems teams for support, particularly Joseph Yamada and Rob Naccarato.
This study was conducted with the support of the Ontario Institute for Cancer Research to PCB through funding provided by the Government of Ontario. This work was supported by Prostate Cancer Canada and is proudly funded by the Movember Foundation - Grant number RS2014–01. PCB was supported by a Terry Fox Research Institute New Investigator Award and a Canadian Institutes of Health Research (CIHR) New Investigator Award. This research is funded by the Canadian Cancer Society (grant number 702528). This work was supported by the Discovery Frontiers: Advancing Big Data Science in Genomics Research program, which is jointly funded by the Natural Sciences and Engineering Research Council (NSERC) of Canada, the Canadian Institutes of Health Research (CIHR), Genome Canada, and the Canada Foundation for Innovation (CFI). This project was supported by Genome Canada through a Large-Scale Applied Project contract to PCB and Drs. Sohrab Shah and Ryan Morin. This study was conducted with the support of the Ontario Genomics Institute (to CP), the Canadian Breast Cancer Foundation (to CQY), the Ontario Graduate Scholarship (to EL), the Oncology Research and Methods Training Program (to YW, NCM and XL), the Canadian Institutes of Health Research (CIHR) (to EL, KEH, NSF and GMC), the Medical Biophysics Excellence University of Toronto Fund Scholarship (to NSF) and the CTMM framework (AIRFORCE project) and EU 7th framework program (ARTFORCE) to MHWS. This work was funded by the Government of Canada through Genome Canada and the Ontario Genomics Institute (OGI-125). The funders played no role in the design of the study, nor in the writing of the manuscript.
Availability of data and materials
PCB conceived of the project. All authors wrote software, documentation and debugged. FL and NAS developed the interactive plotting method, which JG significantly extended. CP wrote the manuscript, which all authors edited and approved.
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The authors declare that they have no competing interests.
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