# Load libraries and datasets library ( "plotgardener" ) library ( "org.Hs.eg.db" ) library ( "19.knownGene" ) library ( "plotgardenerData" ) library ( "AnnotationHub" ) data ( "GM12878_HiC_10kb" ) data ( "IMR90_HiC_10kb" ) data ( "GM12878_ChIP_CTCF_signal" ) data ( "IMR90_ChIP_CTCF_signal" ) data ( "GM12878_ChIP_H3K27ac_signal" ) data ( "IMR90_ChIP_H3K27ac_signal" ) # Create a plotgardener page pageCreate (width = 7, height = 4.25, default.units = "inches" ) # Panel A # Text section label plotText (label = "A", fontsize = 12, x = 0.25, y = 0.25, just = "left", default.units = "inches" ) # Set genomic and dimension parameters in a `params` object params_a <- pgParams (chrom = "chr21", chromstart = 28000000, chromend = 30300000, assembly = "hg19", x = 0.25, width = 2.75, just = c ( "left", "top" ), default.units = "inches" ) # Double-sided Hi-C Plot hicPlot_top <- plotHicSquare (data = GM12878_HiC_10kb, params = params_a, zrange = c ( 0, 200 ), resolution = 10000, half = "top", y = 0.5, height = 2.75 ) hicPlot_bottom <- plotHicSquare (data = IMR90_HiC_10kb, params = params_a, zrange = c ( 0, 70 ), resolution = 10000, half = "bottom", y = 0.5, height = 2.75 ) # Annotate Hi-C heatmap legends annoHeatmapLegend (plot = hicPlot_bottom, fontsize = 7, x = 3.05, y = 0.5, width = 0.07, height = 0.5, just = c ( "left", "top" ), default.units = "inches" ) annoHeatmapLegend (plot = hicPlot_top, fontsize = 7, x =. Check out our vignettes for detailed examples and suggested use cases! plotgardener can address an endless number of use cases, including: dynamic exploration of genomic data, arrangment into multi-omic layouts, and survey plotting for quickly viewing data across the genome. These functions are integrated with Bioconductor packages to flexibly accommodate a large variety of genomic assemblies. Specialized for genomic data, plotgardener also contains functions to read and plot multi-omic data quickly and easily. For more information about plotgardenerās philosophy and design, check out the Our Philosophy page. This allows users to stack plots with confidence that vertically aligned data will correspond to the same regions. Its edge-to-edge plotting functions preserve the mapping between user-specified containers and the represented data. The coordinate-based plotting system grants users precise control over the size, position, and arrangement of plots. plotgardener accomplishes these goals by utilizing 1) a coordinate-based plotting system, and 2) edge-to-edge containerized data visualization. Using grid graphics, plotgardener empowers users to programmatically and flexibly generate multi-panel figures. Plotgardener is a genomic data visualization package for R.
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