![]() ![]() ![]() Furthermore, an ever-increasing array of programmatic libraries and browser APIs now allow code-based, integrated data analysis and construction of browser tracks, which has improved reproducibility and automation 13– 17. Recently, more specialized browsers like Juicebox 9 and HiGlass 10 have increased the ability to visualize non-linear data types, such as 3D chromatin contact frequency 11, 12. Towards this end, genome browsers, including the UCSC Genome Browser and IGV, have revolutionized our ability to investigate genomic data in a rapid and intuitive fashion, using a stacked linear representation of a wide variety of data types and annotations 1– 8. The increasing size, complexity, and sheer volume of multi-omic data sets has created a dire need for tools to efficiently visualize, interpret, and communicate the underlying biological signals present in these data. ![]() Plotgardener also allows precise placement and sizing of ggplot2 plots, making it an invaluable tool for R users and data scientists from virtually any discipline. Plotgardener allows precise, programmatic control over the placement, aesthetics, and arrangements of plots while maximizing user experience through fast and memory-efficient data access, support for a wide variety of data and file types, and tight integration with the Bioconductor environment. We present plotgardener, a coordinate-based genomic data visualization package that offers a new paradigm for multi-plot figure generation in R. ![]() However, existing plotting packages rely on relative positioning and sizing of plots, which is often sufficient for exploratory analysis but is poorly suited for the creation of publication-quality multi-panel images inherent to scientific manuscript preparation. Cow_final <- ggdraw() + draw_plot(cow_A2F) + # Hummingbird heads draw_image( image = icon_hb, x = -0.445, y = 0.445, scale = 0.060) + # Panel A draw_image( image = icon_hb, x = -0.350, y = 0.305, scale = 0.060) + # Panel B draw_image( image = icon_hb, x = 0.445, y = 0.280, scale = 0.060) + # Panel D draw_image( image = icon_hb, x = -0.280, y = -0.073, scale = 0.060) + # Panel E draw_image( image = icon_hb, x = -0.280, y = -0.313, scale = 0.060) + # Panel F # Zebra finch heads draw_image( image = icon_zb, x = -0.360, y = 0.135, scale = 0.052) + # Panel C draw_image( image = icon_zb, x = 0.435, y = 0.250, scale = 0.052) + # Panel D draw_image( image = icon_zb, x = -0.290, y = -0.100, scale = 0.052) + # Panel E draw_image( image = icon_zb, x = -0.290, y = -0.340, scale = 0.052) + # Panel F # Pigeon head draw_image( image = icon_pg, x = -0.290, y = -0.130, scale = 0.055) + # Panel E # Boxes draw_grob( grob = rect_hb_t, x = 0.390, y = 0.280, scale = 0.010) + draw_grob( grob = rect_zb_t, x = 0.390, y = 0.253, scale = 0.010) + draw_grob( grob = rect_hb, x = -0.340, y = -0.073, scale = 0.010) + draw_grob( grob = rect_zb, x = -0.340, y = -0.099, scale = 0.010) + draw_grob( grob = rect_pg, x = -0.340, y = -0.125, scale = 0.010) + draw_grob( grob = rect_hb, x = -0.340, y = -0.313, scale = 0.010) + draw_grob( grob = rect_zb, x = -0.340, y = -0.337, scale = 0.010) + # Points draw_grob( grob = point_hb, x = 0.390, y = 0.280, scale = 0.001) + draw_grob( grob = point_zb, x = 0.390, y = 0.253, scale = 0.001) cow_finalĪnd there you have it! We have recreated the entire figure – including annotations, icons, and inserted images – all through code.The R programming language is one of the most widely used programming languages for transforming raw genomic data sets into meaningful biological conclusions through analysis and visualization, which has been largely facilitated by infrastructure and tools developed by the Bioconductor project. ![]()
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