Fixed color scales not displaying correctly when symmetry_center was used.

Author: enblacar

NEWS.md

@@ -11,6 +11,8 @@
 -   Whenever a categorical color scale is used, now you can set `colorblind = TRUE`, and it will pull from a collection of different published colorblind-safe palettes. If the length of the classes in the categorical variable matches the length of one of the palettes, that palettes is used. If not, a pool will be selected, with a total maximum of 85 categories allowed. If `colors.use` is used, `colorblind` has no effect (thus, check if your palette is colorblind-safe with `do_ColorBlindCheck()`). For continuous variables, `YlGnBu` is used. For divergent variables, `RdBu` is used. Both `YlGnBu` and `RdBu` are colorblind-safe. Since they are set as default, there is no need for `colorblind` parameter in the functions that only plot continuous/divergent variables.
 -  Added support to show the values as text in most heatmap-based functions. This is achieved by using `values.show = TRUE`.
 -  Aesthetics of the text labels can be tuned with `values.threshold`, `values.size` and `values.round`. 
+-  Fixed an issue where `symmetry_center` would not work as intended. Now it correctly displays the color scale with the provided value in the center.
+
 ## Added functions
 -   `do_WafflePlot()`: This function displays proportions as a pictogram grid of 10x10 tiles. It helps to visually see at a glance the proportions of your data. This fails to correctly convey decimal proportions and completely ignores heavily under-represented classes in your dataset. 
 -   `do_RankedExpressionHeatmap()` to plot expression values as a heatmap along a dimensional reduction component.

R/do_ChordDiagramPlot.R

@@ -42,7 +42,6 @@
 #' }
 #' @param padding_labels \strong{\code{\link[base]{numeric}}} | Number of extra padding (white spaces) of the labels so that they do not overlap with the scales.
 #' @param colors.from,colors.to \strong{\code{\link[SCpubr]{named_vector}}} | Named vector of colors corresponding to the unique values of "from" and "to".
-#' @param ... For internal use only.
 #'
 #' @return A circlize plot.
 #' @export
@@ -116,8 +115,6 @@ do_ChordDiagramPlot <- function(sample = NULL,
   check_parameters(parameter = alpha.highlight, parameter_name = "alpha.highlight")
   circlize::circos.clear()
 
-  extra_params <- list(...)
-
     assertthat::assert_that(!is.null(sample),
                             msg = paste0(add_cross(), crayon_body("Please provide a "),
                                          crayon_key("Seurat object"),

R/do_FeaturePlot.R

@@ -371,7 +371,6 @@ do_FeaturePlot <- function(sample,
                                                   enforce_symmetry = enforce_symmetry)
   }
 
-
   # Generate base layer.
   if (isTRUE(plot_cell_borders)){
     out <- compute_umap_layer(sample = sample,
@@ -465,6 +464,7 @@ do_FeaturePlot <- function(sample,
                                       enforce_symmetry = enforce_symmetry,
                                       center_on_value = if(symmetry.type == "absolute"){FALSE} else {TRUE},
                                       value_center = symmetry.center)
+        
         p <- add_scale(p = p,
                        function_use = ggplot2::scale_color_gradientn(colors = colors.gradient,
                                                                      na.value = na.value,

R/utils.R

@@ -1268,8 +1268,8 @@ compute_scales <- function(sample,
     } else {
       low_end <- value_center - limits[1]
       high_end <- limits[2] - value_center
-      value.use <- max(c(low_end, high_end))
-      limits <- c(1 - value.use, 1 + value.use)
+      value.use <- max(c(abs(low_end), abs(high_end)))
+      limits <- c(value_center - value.use, value_center + value.use)
     }
 
   }

man/do_ChordDiagramPlot.Rd

@@ -20,29 +20,6 @@
                               viridis.direction = 1)
     p
 
-    # Draw quantiles of the distribution.
-    p <- SCpubr::do_RidgePlot(sample = sample,
-                              feature = "nFeature_RNA",
-                              continuous_scale = TRUE,
-                              compute_quantiles = TRUE,
-                              compute_custom_quantiles = TRUE)
-    p
-
-    # Draw probability tails.
-    p <- SCpubr::do_RidgePlot(sample = sample,
-                              feature = "nFeature_RNA",
-                              continuous_scale = TRUE,
-                              compute_quantiles = TRUE,
-                              compute_distribution_tails = TRUE)
-    p
-
-    # Draw probability tails.
-    p <- SCpubr::do_RidgePlot(sample = sample,
-                              feature = "nFeature_RNA",
-                              continuous_scale = TRUE,
-                              compute_quantiles = TRUE,
-                              color_by_probabilities = TRUE)
-    p
   } else if (base::isFALSE(value)){
     message("This function can not be used without its suggested packages.")
     message("Check out which ones are needed using `SCpubr::state_dependencies()`.")