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BackgroundWe introduce BPG, a framework for generating publication-quality, highly-customizable plots in the R statistical environment.ResultsThis 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.ConclusionBPG 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.generalElectronic supplementary materialThe online version of this article (10.1186/s12859-019-2610-2) contains supplementary material, which is available to authorized users.

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In order to determine the characteristics of heavy metals' pollution and identify their sources in the soil over the Xiaoqinling gold-mining region, Shaanxi, China, 133 soil samples were collected over the region, one sample was from a close point as background, and concentrations of Hg, Pb, Cu, and As in all samples were analyzed. Results indicated that the soil was polluted by these heavy metals, as their concentrations in the soil over the studied region were higher than that in the background zone. In addition, the result indicated that these metals had their specific spatial characteristics. Among the samples, those with higher concentrations of Hg, Pb, and Cu were mainly collected from the Shuangqiao river alluvial terrace area, the loess ravine tableland, and the piedmont alluvialpluvial inclined tableland, which are the areas of goldmining activities. Meanwhile, the samples with higher concentrations of As were distributed over the loess ravine tableland around Tongguan County, where agricultural activities were conducted intensively. Furthermore, the sources for all the heavy metals Hg, Pb, Cu, and As in the soil over each landscape were determined through analyzing the concentration correlation of these metals and comparing the maps of metal concentration distribution and land-use types. In Yellow river-Weihe river alluvial plain area, these heavy metals were mainly from agricultural activities. In the other landscapes, As was also mainly from agricultural activities; the other metals were contrarily from the gold-related activities. Interestingly, the correlation coefficients of As concentrations with Hg, Pb or Cu concentrations were positive in the samples from Yellow river-Weihe river alluvial plain area, and on the contrary, they were negative in the samples from the other landscapes. This result showed that the correlation can be used as an index to indicate properly the effect of the goldrelated or agricultural activities on the heavy metals in the soil and identify their main sources over each landscape. As human activities resulted in the site-specific characteristics in the concentrations and sources of the heavy metals in each landscape, site-specific control strategies should be selected intensively to remedy landscape soil pollution. In Yellow river-Weihe river plain area, effective control strategies should be aimed at remedying As pollution caused by agricultural activities; in the other landscapes, Hg, Pb and Cu pollution caused by gold-mining activities should be considered.

We introduce BPG, an easy-to-use framework for generating publication-quality, highly-customizable plots in the R statistical environment. This open-source package includes novel methods of displaying high-dimensional datasets and facilitates generation of complex multi-panel figures, making it ideal for complex datasets. A webbased interactive tool allows online figure customization, from which R code can be downloaded for seamless integration with computational pipelines. BPG is available at http://labs.oicr.on.ca/boutros-lab/software/bpg . CC-BY 4.0 International license peer-reviewed) is the author/funder. It is made available under a The copyright holder for this preprint (which was not . http://dx.doi.org/10.1101/156067 doi: bioRxiv preprint first posted online Jun. 26, 2017; BPG P'ng et al. Page 3 of 8Biological 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 datavisualization is growing rapidly 1,2,3,4 . Myriad data visualization tools exist, particularly as web-based interfaces and non-R-based local software packages. Unfortunately these do not integrate easily into R-based statistical pipelines such as the widely used Bioconductor 5 . Within R, visualization packages exist, including base graphics 6 , ggplot2 7 , lattice 8 , Sushi 9 , circlize 10 , multiDimBio 11 , NetBioV 12 , GenomeGraphs 13 and ggbio 14 . These lack publication-quality defaults, contain limited plot types, provide limited scope for automatic generation of multi-panel figures, are constrained to specific data-types and do not allow interactive visualization.Good visualization software must create a wide variety of chart-types in order to match the diversity of datatypes 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 and an application programming interface (API) are required. To date, no existing visualization suite fills these needs.To address this gap, we have created the BPG library, which is implemented in R using the grid graphics system and lattice framework. It generates a broad suite of chart-types, ranging from common plots such as bar charts and box plots to more specialized plots, such as Manhattan plots (Figure 1; code is in Supplementary File 1).

Our visualisation of the IEEE InfoVis citation network is based on 3D graph visualisation techniques. To make effective use of the third dimension we use a layered approach, constraining nodes to lie on parallel planes depending on parameters such as year of publication or link degree. Within the parallel planes nodes are arranged using a fast force-directed layout method. A number of clusters representing different research areas were identified using a self organising map approach.

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