# BRAID Heatmaps Summarize and plot measurements of two inputs as a discrete raster or "stained-glass" plot ```r geom_braid( mapping = NULL, data = NULL, stat = "braid", position = "identity", ..., space = 1.5, trim = TRUE, shared = FALSE, na.rm = FALSE, show.legend = NA, inherit.aes = TRUE ) stat_braid( mapping = NULL, data = NULL, geom = "tile", position = "identity", ..., space = 1.5, trim = TRUE, shared = FALSE, na.rm = FALSE, show.legend = NA, inherit.aes = TRUE ) geom_braid_glass( mapping = NULL, data = NULL, stat = "braid_glass", position = "identity", ..., space = 1.5, trim = TRUE, shared = FALSE, na.rm = FALSE, show.legend = NA, inherit.aes = TRUE ) stat_braid_glass( mapping = NULL, data = NULL, geom = "polygon", position = "identity", ..., space = 1.5, trim = TRUE, shared = FALSE, na.rm = FALSE, show.legend = NA, inherit.aes = TRUE ) ``` ## Arguments - `mapping`: Set of aesthetic mappings created by `aes()`. If specified and `inherit.aes = TRUE` (the default), it is combined with the default mapping at the top level of the plot. You must supply `mapping` if there is no plot mapping. - `data`: The data to be displayed in this layer. There are three options: If `NULL`, the default, the data is inherited from the plot data as specified in the call to `ggplot()`. A `data.frame`, or other object, will override the plot data. All objects will be fortified to produce a data frame. See `fortify()` for which variables will be created. A `function` will be called with a single argument, the plot data. The return value must be a `data.frame`, and will be used as the layer data. A `function` can be created from a `formula` (e.g. `~ head(.x, 10)`). - `stat`: The statistical transformation to use on the data for this layer. When using a `geom_*()` function to construct a layer, the `stat` argument can be used the override the default coupling between geoms and stats. The `stat` argument accepts the following: * A `Stat` ggproto subclass, for example `StatCount`. * A string naming the stat. To give the stat as a string, strip the function name of the `stat_` prefix. For example, to use `stat_count()`, give the stat as `"count"`. * For more information and other ways to specify the stat, see the layer stat documentation. - `position`: A position adjustment to use on the data for this layer. This can be used in various ways, including to prevent overplotting and improving the display. The `position` argument accepts the following: * The result of calling a position function, such as `position_jitter()`. This method allows for passing extra arguments to the position. * A string naming the position adjustment. To give the position as a string, strip the function name of the `position_` prefix. For example, to use `position_jitter()`, give the position as `"jitter"`. * For more information and other ways to specify the position, see the layer position documentation. - `...`: Additional parameters to be passed to `ggplot2::geom_tile()` - `space`: Parameter specifying the separation between marginal tiles and the main grid. Describes the distance from the center of the marginal tile to the center of the nearest main grid tile, divided by the width or height of the tile. If a single value is provided, it is used for both left-right and top-bottom margin tiles. If two values are provided, the first is used for left-right margin tiles and the second is used for top-bottom margin tiles. - `trim`: Should values that are finite in one dimension be dropped if their finite coordinates lie outside the bounds of the main grid? - `shared`: Should marginal offsets and trimming be calculated separately for each facet if plots are faceted. If `FALSE`, the default, each facet will have its own bounds and marginal offsets; if `TRUE`, offsets will be calculated for the full data and shared across all facets. - `na.rm`: If `FALSE`, the default, missing values are removed with a warning. If `TRUE`, missing values are silently removed. - `show.legend`: logical. Should this layer be included in the legends? `NA`, the default, includes if any aesthetics are mapped. `FALSE` never includes, and `TRUE` always includes. It can also be a named logical vector to finely select the aesthetics to display. - `inherit.aes`: If `FALSE`, overrides the default aesthetics, rather than combining with them. This is most useful for helper functions that define both data and aesthetics and shouldn't inherit behaviour from the default plot specification, e.g. `borders()`. - `geom`: The geometric object to use to display the data for this layer. When using a `stat_*()` function to construct a layer, the `geom` argument can be used to override the default coupling between stats and geoms. The `geom` argument accepts the following: * A `Geom` ggproto subclass, for example `GeomPoint`. * A string naming the geom. To give the geom as a string, strip the function name of the `geom_` prefix. For example, to use `geom_point()`, give the geom as `"point"`. * For more information and other ways to specify the geom, see the layer geom documentation. ## Details While the existing `ggplot2` package includes several functions that are extremely effective and versatile for visualizing two-dimensional responses, including `ggplot2::geom_raster()`, `ggplot2::geom_tile()`, and `ggplot2::geom_contour()`, a number of considerations particular to combination data make these functions, as is, somewhat difficult to use. First, these functions are not designed for data in which pairs of x- and y-coordinates are duplicated; yet this is very common in experimental data. While such duplications can be handled prior to calling a visualization function, handling them automatically reduces the barrier to plotting. A second, and much more challenging consideration, is that for many drug combination studies, drug concentrations are measured as a series of equal ratio dilutions; visualizing such doses is most intuitive on a logarithmic scale. But when inputs are scaled logarithmically, zeros become infinite and are automatically removed by nearly all `ggplot2` functions. This makes it very difficult to plot measurements of drugs in isolation and in combination in the same plot. `geom_braid` addresses this by automatically offsetting any measurements whose transformed coordinates are infinite to margins within the plotted space, so that all values can be plotted together. While `geom_braid` is suitable for most response surfaces, some surfaces feature measurements that are not arranged in a evenly spaced checkerboard. For such surfaces, `geom_braid_glass` produces a set of Voronoi polygons centered on the available transformed coordinates, creating what we call a "stained glass" plot. Marginal points are still represented by rectangles, but with width and height such that boundaries are equidistant between adjacent points. `stat_braid` and `stat_braid_glass` are simply the corresponding `stat_` functions for these two functions. ## Examples ```r concentrations <- c(0,2^(-3:3)) surface <- data.frame( concA = rep(rep(concentrations,each=length(concentrations)),each=3), concB = rep(rep(concentrations,times=length(concentrations)),each=3), replicate = rep(c(1,2,3),times=(length(concentrations)^2)) ) surface$actual <- evalBraidModel( surface$concA, surface$concB, c(1, 1, 3, 3, 2, 0, 100, 100, 100) ) surface$measure <- surface$actual + rnorm(nrow(surface),sd=7) ggplot(surface,aes(x=concA,y=concB))+ geom_braid(aes(fill=measure))+ scale_x_log10()+ scale_y_log10()+ scale_fill_distiller(palette="RdYlBu")+ coord_equal()+ labs(x="Drug A",y="Drug B",fill="Effect") glassSurface <- surface glassSurface$concA[glassSurface$replicate==2] <- glassSurface$concA[glassSurface$replicate==2]*1.25 glassSurface$concB[glassSurface$replicate==3] <- glassSurface$concB[glassSurface$replicate==3]*1.25 glassSurface$actual <- evalBraidModel( glassSurface$concA, glassSurface$concB, c(1, 1, 3, 3, -0.5, 0, 60, 100, 100) ) glassSurface$measure <- glassSurface$actual+rnorm(nrow(glassSurface),sd=7) ggplot(glassSurface,aes(x=concA,y=concB))+ geom_braid_glass(aes(fill=measure))+ scale_x_log10("Drug A")+ scale_y_log10("Drug B")+ scale_fill_distiller("Effect",palette="RdYlBu")+ coord_equal() glassSurface$tilewidth <- log10(2)*0.9 glassSurface$tilewidth[glassSurface$concA==0] <- log10(2)/2 glassSurface$tileheight <- log10(2)*0.9 glassSurface$tileheight[glassSurface$concB==0] <- log10(2)/2 ggplot(glassSurface,aes(x=concA,y=concB))+ geom_braid_glass(aes(fill=measure,width=tilewidth,height=tileheight),space=2)+ scale_x_log10("Drug A")+ scale_y_log10("Drug B")+ scale_fill_distiller("Effect",palette="RdYlBu")+ coord_equal() ```

geom_braid function