diff --git a/R/plot_cpdb.R b/R/plot_cpdb.R
index adbb5f6..3388779 100644
--- a/R/plot_cpdb.R
+++ b/R/plot_cpdb.R
@@ -35,8 +35,8 @@
 #' \donttest{
 #' data(kidneyimmune)
 #' data(cpdb_output)
-#' plot_cpdb(kidneyimmune, "B cell", "CD4T cell", "celltype", means, pvals, splitby_key = "Experiment", genes = c("CXCL13", "CD274", "CXCR5"))
-#' plot_cpdb(kidneyimmune, "B cell", "CD4T cell", "celltype", means, pvals, splitby_key = "Experiment", gene_family = "chemokines")
+#' plot_cpdb(kidneyimmune, 'B cell', 'CD4T cell', 'celltype', means, pvals, splitby_key = 'Experiment', genes = c('CXCL13', 'CD274', 'CXCR5'))
+#' plot_cpdb(kidneyimmune, 'B cell', 'CD4T cell', 'celltype', means, pvals, splitby_key = 'Experiment', gene_family = 'chemokines')
 #' }
 #' @include utils.R
 #' @import dplyr
@@ -45,15 +45,14 @@
 #' @import reshape2
 #' @export
 
-plot_cpdb <- function(
-    scdata, cell_type1, cell_type2, celltype_key, means, pvals,
-    interaction_scores = NULL, cellsign = NULL, max_size = 8, keep_significant_only = TRUE,
-    splitby_key = NULL, gene_family = NULL, custom_gene_family = NULL, genes = NULL,
-    standard_scale = TRUE, cluster_rows = TRUE, col_option = viridis::viridis(50),
-    default_style = TRUE, highlight_col = "red", highlight_size = NULL, max_highlight_size = 2,
-    special_character_regex_pattern = NULL, degs_analysis = FALSE, return_table = FALSE,
-    exclude_interactions = NULL, min_interaction_score = 0, scale_alpha_by_interaction_scores = FALSE,
-    scale_alpha_by_cellsign = FALSE, filter_by_cellsign = FALSE, title = "", ...) {
+plot_cpdb <- function(scdata, cell_type1, cell_type2, celltype_key, means, pvals,
+  interaction_scores = NULL, cellsign = NULL, max_size = 8, keep_significant_only = TRUE,
+  splitby_key = NULL, gene_family = NULL, custom_gene_family = NULL, genes = NULL,
+  standard_scale = TRUE, cluster_rows = TRUE, col_option = viridis::viridis(50),
+  default_style = TRUE, highlight_col = "red", highlight_size = NULL, max_highlight_size = 2,
+  special_character_regex_pattern = NULL, degs_analysis = FALSE, return_table = FALSE,
+  exclude_interactions = NULL, min_interaction_score = 0, scale_alpha_by_interaction_scores = FALSE,
+  scale_alpha_by_cellsign = FALSE, filter_by_cellsign = FALSE, title = "", ...) {
   requireNamespace("SingleCellExperiment")
   requireNamespace("grDevices")
   if (is.null(special_character_regex_pattern)) {
@@ -72,8 +71,7 @@ plot_cpdb <- function(
     cellsign_mat <- .prep_table(cellsign)
   }
   col_start <- ifelse(colnames(pvals_mat)[DEFAULT_CLASS_COL] == "classification",
-    DEFAULT_V5_COL_START, DEFAULT_COL_START
-  )
+    DEFAULT_V5_COL_START, DEFAULT_COL_START)
   if (degs_analysis) {
     pvals_mat[, col_start:ncol(pvals_mat)] <- 1 - pvals_mat[, col_start:ncol(pvals_mat)]
   }
@@ -81,28 +79,22 @@ plot_cpdb <- function(
   if (col_start == DEFAULT_V5_COL_START) {
     v5tmp <- reshape2::melt(means_mat, id.vars = colnames(means_mat)[1:col_start])
     special_sep <- paste0(rep(DEFAULT_SEP, 3), collapse = "")
-    row.names(v5tmp) <- paste0(
-      gsub("_", "-", v5tmp$interacting_pair), special_sep,
-      v5tmp$variable
-    )
+    row.names(v5tmp) <- paste0(gsub("_", "-", v5tmp$interacting_pair), special_sep,
+      v5tmp$variable)
     v5tmp <- v5tmp[, c("is_integrin", "directionality", "classification")]
   }
   cell_type1 <- .sub_pattern(cell_type = cell_type1, pattern = special_character_regex_pattern)
   cell_type2 <- .sub_pattern(cell_type = cell_type2, pattern = special_character_regex_pattern)
-  query_list <- .prep_query_group(
-    data = means_mat, genes = genes, gene_family = gene_family,
-    custom_gene_family = custom_gene_family
-  )
+  query_list <- .prep_query_group(data = means_mat, genes = genes, gene_family = gene_family,
+    custom_gene_family = custom_gene_family)
   query <- query_list[["query"]]
   query_group <- query_list[["query_group"]]
   # prepare the cell_type query
   if (!is.null(splitby_key)) {
     labels <- paste0(metadata[[splitby_key]], "_", metadata[[celltype_key]])
     if (is.factor(metadata[[splitby_key]]) & is.factor(metadata[[celltype_key]])) {
-      labels <- factor(labels, levels = paste0(
-        levels(metadata[[splitby_key]]),
-        "_", rep(levels(metadata[[celltype_key]]), each = length(levels(metadata[[splitby_key]])))
-      ))
+      labels <- factor(labels, levels = paste0(levels(metadata[[splitby_key]]),
+        "_", rep(levels(metadata[[celltype_key]]), each = length(levels(metadata[[splitby_key]])))))
     } else {
       labels <- factor(labels)
     }
@@ -113,10 +105,8 @@ plot_cpdb <- function(
       # the purpose for this step is to allow for special characters to be used
       # in the celltype grepping
       if (length(groups) > 1) {
-        labels2 <- gsub(
-          paste0(paste0(groups, "_"), collapse = "|"), "",
-          labels
-        )
+        labels2 <- gsub(paste0(paste0(groups, "_"), collapse = "|"), "",
+          labels)
       } else {
         labels2 <- gsub(paste0(groups, "_"), "", labels)
       }
@@ -130,14 +120,10 @@ plot_cpdb <- function(
       grp <- as.list(groups)
       celltype <- list()
       for (i in 1:length(c_type1)) {
-        celltype[[i]] <- .create_celltype_query(
-          ctype1 = c_type1[[i]], ctype2 = c_type2,
-          sep = DEFAULT_SEP
-        )
-        celltype[[i]] <- lapply(grp, .keep_interested_groups,
-          ct = celltype[[i]],
-          sep = DEFAULT_SEP
-        )
+        celltype[[i]] <- .create_celltype_query(ctype1 = c_type1[[i]], ctype2 = c_type2,
+          sep = DEFAULT_SEP)
+        celltype[[i]] <- lapply(grp, .keep_interested_groups, ct = celltype[[i]],
+          sep = DEFAULT_SEP)
       }
       for (i in 1:length(celltype)) {
         celltype[[i]] <- celltype[[i]][-which(celltype[[i]] == "")]
@@ -158,10 +144,8 @@ plot_cpdb <- function(
       c_type2 <- lapply(c_type2, .sub_pattern, pattern = special_character_regex_pattern)
       celltype <- list()
       for (i in 1:length(c_type1)) {
-        celltype[[i]] <- .create_celltype_query(
-          ctype1 = c_type1[[i]], ctype2 = c_type2,
-          sep = DEFAULT_SEP
-        )
+        celltype[[i]] <- .create_celltype_query(ctype1 = c_type1[[i]], ctype2 = c_type2,
+          sep = DEFAULT_SEP)
       }
       cell_type <- do.call(paste0, list(celltype, collapse = "|"))
     }
@@ -175,82 +159,56 @@ plot_cpdb <- function(
     c_type2 <- lapply(c_type2, .sub_pattern, pattern = special_character_regex_pattern)
     celltype <- list()
     for (i in 1:length(c_type1)) {
-      celltype[[i]] <- .create_celltype_query(
-        ctype1 = c_type1[[i]], ctype2 = c_type2,
-        sep = DEFAULT_SEP
-      )
+      celltype[[i]] <- .create_celltype_query(ctype1 = c_type1[[i]], ctype2 = c_type2,
+        sep = DEFAULT_SEP)
     }
     cell_type <- do.call(paste0, list(celltype, collapse = "|"))
   }
-  if (!is.null(gene_family) & is.null(genes)) {
+    if (!is.null(gene_family) & is.null(genes)) {
     if (length(gene_family) == 1) {
-      means_mat <- .prep_data_querygroup_celltype1(
-        .data = means_mat, .query_group = query_group,
+      means_mat <- .prep_data_querygroup_celltype1(.data = means_mat, .query_group = query_group,
         .gene_family = gene_family, .cell_type = cell_type, .celltype = celltype,
-        ...
-      )
-      pvals_mat <- .prep_data_querygroup_celltype1(
-        .data = pvals_mat, .query_group = query_group,
+        ...)
+      pvals_mat <- .prep_data_querygroup_celltype1(.data = pvals_mat, .query_group = query_group,
         .gene_family = gene_family, .cell_type = cell_type, .celltype = celltype,
-        ...
-      )
+        ...)
       if (!is.null(interaction_scores)) {
-        interaction_scores_mat <- .prep_data_querygroup_celltype1(
-          .data = interaction_scores_mat,
+        interaction_scores_mat <- .prep_data_querygroup_celltype1(.data = interaction_scores_mat,
           .query_group = query_group, .gene_family = gene_family, .cell_type = cell_type,
-          .celltype = celltype, ...
-        )
+          .celltype = celltype, ...)
       } else if (!is.null(cellsign)) {
-        cellsign_mat <- .prep_data_querygroup_celltype1(
-          .data = cellsign_mat,
+        cellsign_mat <- .prep_data_querygroup_celltype1(.data = cellsign_mat,
           .query_group = query_group, .gene_family = gene_family, .cell_type = cell_type,
-          .celltype = celltype, ...
-        )
+          .celltype = celltype, ...)
       }
     } else if (length(gene_family) > 1) {
-      means_mat <- .prep_data_querygroup_celltype2(
-        .data = means_mat, .query_group = query_group,
+      means_mat <- .prep_data_querygroup_celltype2(.data = means_mat, .query_group = query_group,
         .gene_family = gene_family, .cell_type = cell_type, .celltype = celltype,
-        ...
-      )
-      pvals_mat <- .prep_data_querygroup_celltype2(
-        .data = pvals_mat, .query_group = query_group,
+        ...)
+      pvals_mat <- .prep_data_querygroup_celltype2(.data = pvals_mat, .query_group = query_group,
         .gene_family = gene_family, .cell_type = cell_type, .celltype = celltype,
-        ...
-      )
+        ...)
       if (!is.null(interaction_scores)) {
-        interaction_scores_mat <- .prep_data_querygroup_celltype2(
-          .data = interaction_scores_mat,
+        interaction_scores_mat <- .prep_data_querygroup_celltype2(.data = interaction_scores_mat,
           .query_group = query_group, .gene_family = gene_family, .cell_type = cell_type,
-          .celltype = celltype, ...
-        )
+          .celltype = celltype, ...)
       } else if (!is.null(cellsign)) {
-        cellsign_mat <- .prep_data_querygroup_celltype2(
-          .data = cellsign_mat,
+        cellsign_mat <- .prep_data_querygroup_celltype2(.data = cellsign_mat,
           .query_group = query_group, .gene_family = gene_family, .cell_type = cell_type,
-          .celltype = celltype, ...
-        )
+          .celltype = celltype, ...)
       }
     }
   } else if (is.null(gene_family) & !is.null(genes) | is.null(gene_family) & is.null(genes)) {
-    means_mat <- .prep_data_query_celltype(
-      .data = means_mat, .query = query,
-      .cell_type = cell_type, .celltype = celltype, ...
-    )
-    pvals_mat <- .prep_data_query_celltype(
-      .data = pvals_mat, .query = query,
-      .cell_type = cell_type, .celltype = celltype, ...
-    )
+    means_mat <- .prep_data_query_celltype(.data = means_mat, .query = query, .cell_type = cell_type,
+      .celltype = celltype, ...)
+    pvals_mat <- .prep_data_query_celltype(.data = pvals_mat, .query = query, .cell_type = cell_type,
+      .celltype = celltype, ...)
     if (!is.null(interaction_scores)) {
-      interaction_scores_mat <- .prep_data_query_celltype(
-        .data = interaction_scores_mat,
-        .cell_type = cell_type, .celltype = celltype, ...
-      )
+      interaction_scores_mat <- .prep_data_query_celltype(.data = interaction_scores_mat,
+        .query = query, .cell_type = cell_type, .celltype = celltype, ...)
     } else if (!is.null(cellsign)) {
-      cellsign_mat <- .prep_data_query_celltype(
-        .data = cellsign_mat, .cell_type = cell_type,
-        .celltype = celltype, ...
-      )
+      cellsign_mat <- .prep_data_query_celltype(.data = cellsign_mat, .query = query,
+        .cell_type = cell_type, .celltype = celltype, ...)
     }
   }
   if (length(means_mat) == 0) {
@@ -259,15 +217,11 @@ plot_cpdb <- function(
     if (!all(dim(pvals_mat) == dim(means_mat))) {
       pvals_mat <- .prep_dimensions(pvals_mat, means_mat)
     }
-    # if (!is.null(interaction_scores)) {
-    #   if (!all(dim(interaction_scores_mat) == dim(means_mat))) {
-    #     interaction_scores_mat <- .prep_dimensions(interaction_scores_mat, means_mat)
-    #   }
-    # } else if (!is.null(cellsign)) {
-    #   if (!all(dim(cellsign_mat) == dim(means_mat))) {
-    #     cellsign_mat <- .prep_dimensions(cellsign_mat, means_mat)
-    #   }
-    # }
+    # if (!is.null(interaction_scores)) { if (!all(dim(interaction_scores_mat)
+    # == dim(means_mat))) { interaction_scores_mat <-
+    # .prep_dimensions(interaction_scores_mat, means_mat) } } else if
+    # (!is.null(cellsign)) { if (!all(dim(cellsign_mat) == dim(means_mat))) {
+    # cellsign_mat <- .prep_dimensions(cellsign_mat, means_mat) } }
   }
   # rearrange the columns so that it interleaves the two groups
   if (!is.null(splitby_key)) {
@@ -377,10 +331,8 @@ plot_cpdb <- function(
       }
     }
   }
-  row.names(df) <- paste0(
-    df$Var1, paste0(rep(DEFAULT_SEP, 3), collapse = ""),
-    df$Var2
-  )
+  row.names(df) <- paste0(df$Var1, paste0(rep(DEFAULT_SEP, 3), collapse = ""),
+    df$Var2)
   df$Var2 <- gsub(DEFAULT_SEP, "-", df$Var2)
   final_levels <- unique(df$Var2)
   df$Var2 <- factor(df$Var2, unique(df$Var2))
@@ -423,117 +375,85 @@ plot_cpdb <- function(
       if (scale_alpha_by_interaction_scores == TRUE) {
         if (default_style) {
           if (standard_scale) {
-            g <- ggplot(df, aes(
-              x = Var2, y = Var1, color = significant,
-              fill = scaled_means, size = scaled_means, alpha = interaction_scores
-            ))
+          g <- ggplot(df, aes(x = Var2, y = Var1, color = significant,
+            fill = scaled_means, size = scaled_means, alpha = interaction_scores))
           } else {
-            g <- ggplot(df, aes(
-              x = Var2, y = Var1, color = significant,
-              fill = means, size = means, alpha = interaction_scores
-            ))
+          g <- ggplot(df, aes(x = Var2, y = Var1, color = significant,
+            fill = means, size = means, alpha = interaction_scores))
           }
         } else {
           if (all(df$significant == "no")) {
-            if (standard_scale) {
-              g <- ggplot(df, aes(
-                x = Var2, y = Var1, color = significant,
-                fill = scaled_means, size = scaled_means, alpha = interaction_scores
-              ))
+          if (standard_scale) {
+            g <- ggplot(df, aes(x = Var2, y = Var1, color = significant,
+            fill = scaled_means, size = scaled_means, alpha = interaction_scores))
+          } else {
+            g <- ggplot(df, aes(x = Var2, y = Var1, color = significant,
+            fill = means, size = means, alpha = interaction_scores))
+          }
+          default_style <- TRUE
+          } else {
+          highlight_col <- "#FFFFFF"  # enforce this
+          if (standard_scale) {
+            if (!is.null(highlight_size)) {
+            g <- ggplot(df, aes(x = Var2, y = Var1, fill = significant,
+              colour = scaled_means, size = scaled_means, stroke = highlight_size,
+              alpha = interaction_scores))
             } else {
-              g <- ggplot(df, aes(
-                x = Var2, y = Var1, color = significant,
-                fill = means, size = means, alpha = interaction_scores
-              ))
+            g <- ggplot(df, aes(x = Var2, y = Var1, fill = significant,
+              colour = scaled_means, size = scaled_means, stroke = x_stroke,
+              alpha = interaction_scores))
             }
-            default_style <- TRUE
           } else {
-            highlight_col <- "#FFFFFF" # enforce this
-            if (standard_scale) {
-              if (!is.null(highlight_size)) {
-                g <- ggplot(df, aes(
-                  x = Var2, y = Var1, fill = significant,
-                  colour = scaled_means, size = scaled_means, stroke = highlight_size,
-                  alpha = interaction_scores
-                ))
-              } else {
-                g <- ggplot(df, aes(
-                  x = Var2, y = Var1, fill = significant,
-                  colour = scaled_means, size = scaled_means, stroke = x_stroke,
-                  alpha = interaction_scores
-                ))
-              }
+            if (!is.null(highlight_size)) {
+            g <- ggplot(df, aes(x = Var2, y = Var1, fill = significant,
+              colour = means, size = means, stroke = highlight_size,
+              alpha = interaction_scores))
             } else {
-              if (!is.null(highlight_size)) {
-                g <- ggplot(df, aes(
-                  x = Var2, y = Var1, fill = significant,
-                  colour = means, size = means, stroke = highlight_size,
-                  alpha = interaction_scores
-                ))
-              } else {
-                g <- ggplot(df, aes(
-                  x = Var2, y = Var1, fill = significant,
-                  colour = means, size = means, stroke = x_stroke, alpha = interaction_scores
-                ))
-              }
+            g <- ggplot(df, aes(x = Var2, y = Var1, fill = significant,
+              colour = means, size = means, stroke = x_stroke, alpha = interaction_scores))
             }
           }
+          }
         }
       } else {
         if (default_style) {
           if (standard_scale) {
-            g <- ggplot(df, aes(
-              x = Var2, y = Var1, color = significant,
-              fill = scaled_means, size = scaled_means
-            ))
+          g <- ggplot(df, aes(x = Var2, y = Var1, color = significant,
+            fill = scaled_means, size = scaled_means))
           } else {
-            g <- ggplot(df, aes(
-              x = Var2, y = Var1, color = significant,
-              fill = means, size = means
-            ))
+          g <- ggplot(df, aes(x = Var2, y = Var1, color = significant,
+            fill = means, size = means))
           }
         } else {
           if (all(df$significant == "no")) {
-            if (standard_scale) {
-              g <- ggplot(df, aes(
-                x = Var2, y = Var1, color = significant,
-                fill = scaled_means, size = scaled_means
-              ))
+          if (standard_scale) {
+            g <- ggplot(df, aes(x = Var2, y = Var1, color = significant,
+            fill = scaled_means, size = scaled_means))
+          } else {
+            g <- ggplot(df, aes(x = Var2, y = Var1, color = significant,
+            fill = means, size = means))
+          }
+          default_style <- TRUE
+          } else {
+          highlight_col <- "#FFFFFF"  # enforce this
+          if (standard_scale) {
+            if (!is.null(highlight_size)) {
+            g <- ggplot(df, aes(x = Var2, y = Var1, fill = significant,
+              colour = scaled_means, size = scaled_means, stroke = highlight_size))
             } else {
-              g <- ggplot(df, aes(
-                x = Var2, y = Var1, color = significant,
-                fill = means, size = means
-              ))
+            g <- ggplot(df, aes(x = Var2, y = Var1, fill = significant,
+              colour = scaled_means, size = scaled_means, stroke = x_stroke))
             }
-            default_style <- TRUE
           } else {
-            highlight_col <- "#FFFFFF" # enforce this
-            if (standard_scale) {
-              if (!is.null(highlight_size)) {
-                g <- ggplot(df, aes(
-                  x = Var2, y = Var1, fill = significant,
-                  colour = scaled_means, size = scaled_means, stroke = highlight_size
-                ))
-              } else {
-                g <- ggplot(df, aes(
-                  x = Var2, y = Var1, fill = significant,
-                  colour = scaled_means, size = scaled_means, stroke = x_stroke
-                ))
-              }
+            if (!is.null(highlight_size)) {
+            g <- ggplot(df, aes(x = Var2, y = Var1, fill = significant,
+              colour = means, size = means, stroke = highlight_size))
             } else {
-              if (!is.null(highlight_size)) {
-                g <- ggplot(df, aes(
-                  x = Var2, y = Var1, fill = significant,
-                  colour = means, size = means, stroke = highlight_size
-                ))
-              } else {
-                g <- ggplot(df, aes(
-                  x = Var2, y = Var1, fill = significant,
-                  colour = means, size = means, stroke = x_stroke
-                ))
-              }
+            g <- ggplot(df, aes(x = Var2, y = Var1, fill = significant,
+              colour = means, size = means, stroke = x_stroke))
             }
           }
+          }
         }
       }
     } else if (!is.null(cellsign)) {
@@ -545,249 +465,168 @@ plot_cpdb <- function(
       if (scale_alpha_by_cellsign == TRUE) {
         if (default_style) {
           if (standard_scale) {
-            g <- ggplot(df, aes(
-              x = Var2, y = Var1, color = significant,
-              fill = scaled_means, size = scaled_means, alpha = cellsign
-            ))
+          g <- ggplot(df, aes(x = Var2, y = Var1, color = significant,
+            fill = scaled_means, size = scaled_means, alpha = cellsign))
           } else {
-            g <- ggplot(df, aes(
-              x = Var2, y = Var1, color = significant,
-              fill = means, size = means, alpha = cellsign
-            ))
+          g <- ggplot(df, aes(x = Var2, y = Var1, color = significant,
+            fill = means, size = means, alpha = cellsign))
           }
         } else {
           if (all(df$significant == "no")) {
-            if (standard_scale) {
-              g <- ggplot(df, aes(
-                x = Var2, y = Var1, color = significant,
-                fill = scaled_means, size = scaled_means, alpha = cellsign
-              ))
+          if (standard_scale) {
+            g <- ggplot(df, aes(x = Var2, y = Var1, color = significant,
+            fill = scaled_means, size = scaled_means, alpha = cellsign))
+          } else {
+            g <- ggplot(df, aes(x = Var2, y = Var1, color = significant,
+            fill = means, size = means, alpha = cellsign))
+          }
+          default_style <- TRUE
+          } else {
+          highlight_col <- "#FFFFFF"  # enforce this
+          if (standard_scale) {
+            if (!is.null(highlight_size)) {
+            g <- ggplot(df, aes(x = Var2, y = Var1, fill = significant,
+              colour = scaled_means, size = scaled_means, stroke = highlight_size,
+              alpha = cellsign))
             } else {
-              g <- ggplot(df, aes(
-                x = Var2, y = Var1, color = significant,
-                fill = means, size = means, alpha = cellsign
-              ))
+            g <- ggplot(df, aes(x = Var2, y = Var1, fill = significant,
+              colour = scaled_means, size = scaled_means, stroke = x_stroke,
+              alpha = cellsign))
             }
-            default_style <- TRUE
           } else {
-            highlight_col <- "#FFFFFF" # enforce this
-            if (standard_scale) {
-              if (!is.null(highlight_size)) {
-                g <- ggplot(df, aes(
-                  x = Var2, y = Var1, fill = significant,
-                  colour = scaled_means, size = scaled_means, stroke = highlight_size,
-                  alpha = cellsign
-                ))
-              } else {
-                g <- ggplot(df, aes(
-                  x = Var2, y = Var1, fill = significant,
-                  colour = scaled_means, size = scaled_means, stroke = x_stroke,
-                  alpha = cellsign
-                ))
-              }
+            if (!is.null(highlight_size)) {
+            g <- ggplot(df, aes(x = Var2, y = Var1, fill = significant,
+              colour = means, size = means, stroke = highlight_size,
+              alpha = cellsign))
             } else {
-              if (!is.null(highlight_size)) {
-                g <- ggplot(df, aes(
-                  x = Var2, y = Var1, fill = significant,
-                  colour = means, size = means, stroke = highlight_size,
-                  alpha = cellsign
-                ))
-              } else {
-                g <- ggplot(df, aes(
-                  x = Var2, y = Var1, fill = significant,
-                  colour = means, size = means, stroke = x_stroke, alpha = cellsign
-                ))
-              }
+            g <- ggplot(df, aes(x = Var2, y = Var1, fill = significant,
+              colour = means, size = means, stroke = x_stroke, alpha = cellsign))
             }
           }
+          }
         }
       } else {
         if (default_style) {
           if (standard_scale) {
-            g <- ggplot(df, aes(
-              x = Var2, y = Var1, color = significant,
-              fill = scaled_means, size = scaled_means
-            ))
+          g <- ggplot(df, aes(x = Var2, y = Var1, color = significant,
+            fill = scaled_means, size = scaled_means))
           } else {
-            g <- ggplot(df, aes(
-              x = Var2, y = Var1, color = significant,
-              fill = means, size = means
-            ))
+          g <- ggplot(df, aes(x = Var2, y = Var1, color = significant,
+            fill = means, size = means))
           }
         } else {
           if (all(df$significant == "no")) {
-            if (standard_scale) {
-              g <- ggplot(df, aes(
-                x = Var2, y = Var1, color = significant,
-                fill = scaled_means, size = scaled_means
-              ))
+          if (standard_scale) {
+            g <- ggplot(df, aes(x = Var2, y = Var1, color = significant,
+            fill = scaled_means, size = scaled_means))
+          } else {
+            g <- ggplot(df, aes(x = Var2, y = Var1, color = significant,
+            fill = means, size = means))
+          }
+          default_style <- TRUE
+          } else {
+          highlight_col <- "#FFFFFF"  # enforce this
+          if (standard_scale) {
+            if (!is.null(highlight_size)) {
+            g <- ggplot(df, aes(x = Var2, y = Var1, fill = significant,
+              colour = scaled_means, size = scaled_means, stroke = highlight_size))
             } else {
-              g <- ggplot(df, aes(
-                x = Var2, y = Var1, color = significant,
-                fill = means, size = means
-              ))
+            g <- ggplot(df, aes(x = Var2, y = Var1, fill = significant,
+              colour = scaled_means, size = scaled_means, stroke = x_stroke))
             }
-            default_style <- TRUE
           } else {
-            highlight_col <- "#FFFFFF" # enforce this
-            if (standard_scale) {
-              if (!is.null(highlight_size)) {
-                g <- ggplot(df, aes(
-                  x = Var2, y = Var1, fill = significant,
-                  colour = scaled_means, size = scaled_means, stroke = highlight_size
-                ))
-              } else {
-                g <- ggplot(df, aes(
-                  x = Var2, y = Var1, fill = significant,
-                  colour = scaled_means, size = scaled_means, stroke = x_stroke
-                ))
-              }
+            if (!is.null(highlight_size)) {
+            g <- ggplot(df, aes(x = Var2, y = Var1, fill = significant,
+              colour = means, size = means, stroke = highlight_size))
             } else {
-              if (!is.null(highlight_size)) {
-                g <- ggplot(df, aes(
-                  x = Var2, y = Var1, fill = significant,
-                  colour = means, size = means, stroke = highlight_size
-                ))
-              } else {
-                g <- ggplot(df, aes(
-                  x = Var2, y = Var1, fill = significant,
-                  colour = means, size = means, stroke = x_stroke
-                ))
-              }
+            g <- ggplot(df, aes(x = Var2, y = Var1, fill = significant,
+              colour = means, size = means, stroke = x_stroke))
             }
           }
+          }
         }
       }
     } else {
       if (default_style) {
         if (standard_scale) {
-          g <- ggplot(df, aes(
-            x = Var2, y = Var1, color = significant, fill = scaled_means,
-            size = scaled_means
-          ))
+          g <- ggplot(df, aes(x = Var2, y = Var1, color = significant, fill = scaled_means,
+          size = scaled_means))
         } else {
-          g <- ggplot(df, aes(
-            x = Var2, y = Var1, color = significant, fill = means,
-            size = means
-          ))
+          g <- ggplot(df, aes(x = Var2, y = Var1, color = significant, fill = means,
+          size = means))
         }
       } else {
         if (all(df$significant == "no")) {
           if (standard_scale) {
-            g <- ggplot(df, aes(
-              x = Var2, y = Var1, color = significant,
-              fill = scaled_means, size = scaled_means
-            ))
+          g <- ggplot(df, aes(x = Var2, y = Var1, color = significant,
+            fill = scaled_means, size = scaled_means))
           } else {
-            g <- ggplot(df, aes(
-              x = Var2, y = Var1, color = significant,
-              fill = means, size = means
-            ))
+          g <- ggplot(df, aes(x = Var2, y = Var1, color = significant,
+            fill = means, size = means))
           }
           default_style <- TRUE
         } else {
-          highlight_col <- "#FFFFFF" # enforce this
+          highlight_col <- "#FFFFFF"  # enforce this
           if (standard_scale) {
-            if (!is.null(highlight_size)) {
-              g <- ggplot(df, aes(
-                x = Var2, y = Var1, fill = significant,
-                colour = scaled_means, size = scaled_means, stroke = highlight_size
-              ))
-            } else {
-              g <- ggplot(df, aes(
-                x = Var2, y = Var1, fill = significant,
-                colour = scaled_means, size = scaled_means, stroke = x_stroke
-              ))
-            }
+          if (!is.null(highlight_size)) {
+            g <- ggplot(df, aes(x = Var2, y = Var1, fill = significant,
+            colour = scaled_means, size = scaled_means, stroke = highlight_size))
           } else {
-            if (!is.null(highlight_size)) {
-              g <- ggplot(df, aes(
-                x = Var2, y = Var1, fill = significant,
-                colour = means, size = means, stroke = highlight_size
-              ))
-            } else {
-              g <- ggplot(df, aes(
-                x = Var2, y = Var1, fill = significant,
-                colour = means, size = means, stroke = x_stroke
-              ))
-            }
+            g <- ggplot(df, aes(x = Var2, y = Var1, fill = significant,
+            colour = scaled_means, size = scaled_means, stroke = x_stroke))
+          }
+          } else {
+          if (!is.null(highlight_size)) {
+            g <- ggplot(df, aes(x = Var2, y = Var1, fill = significant,
+            colour = means, size = means, stroke = highlight_size))
+          } else {
+            g <- ggplot(df, aes(x = Var2, y = Var1, fill = significant,
+            colour = means, size = means, stroke = x_stroke))
+          }
           }
         }
       }
     }
-    g <- g + geom_point(pch = 21, na.rm = TRUE) + theme_bw() + theme(
-      axis.text.x = element_text(
-        angle = 45,
-        hjust = 0, color = "#000000"
-      ), axis.text.y = element_text(color = "#000000"),
+    g <- g + geom_point(pch = 21, na.rm = TRUE) + theme_bw() + theme(axis.text.x = element_text(angle = 45,
+      hjust = 0, color = "#000000"), axis.text.y = element_text(color = "#000000"),
       axis.title.x = element_blank(), axis.title.y = element_blank(), legend.direction = "vertical",
-      legend.box = "horizontal"
-    ) + scale_x_discrete(position = "top") + scale_radius(range = c(
-      0,
-      max_size
-    )) + scale_linewidth(range = c(0, max_highlight_size))
+      legend.box = "horizontal") + scale_x_discrete(position = "top") + scale_radius(range = c(0,
+      max_size)) + scale_linewidth(range = c(0, max_highlight_size))
     if (default_style) {
-      g <- g + scale_colour_manual(
-        values = c(yes = highlight_col, no = "#ffffff"),
-        na.value = NA, na.translate = FALSE
-      ) + guides(
-        fill = guide_colourbar(
-          barwidth = 4,
-          label = TRUE, ticks = TRUE, draw.ulim = TRUE, draw.llim = TRUE, order = 1
-        ),
-        size = guide_legend(reverse = TRUE, order = 2), stroke = guide_legend(
-          reverse = TRUE,
-          order = 3
-        )
-      )
+      g <- g + scale_colour_manual(values = c(yes = highlight_col, no = "#ffffff"),
+        na.value = NA, na.translate = FALSE) + guides(fill = guide_colourbar(barwidth = 4,
+        label = TRUE, ticks = TRUE, draw.ulim = TRUE, draw.llim = TRUE, order = 1),
+        size = guide_legend(reverse = TRUE, order = 2), stroke = guide_legend(reverse = TRUE,
+          order = 3))
       if (length(col_option) == 1) {
-        g <- g + scale_fill_gradientn(colors = (grDevices::colorRampPalette(c(
-          "white",
-          col_option
-        )))(100), na.value = "white")
+        g <- g + scale_fill_gradientn(colors = (grDevices::colorRampPalette(c("white",
+          col_option)))(100), na.value = "white")
       } else {
-        g <- g + scale_fill_gradientn(
-          colors = c("white", (grDevices::colorRampPalette(col_option))(99)),
-          na.value = "white"
-        )
+        g <- g + scale_fill_gradientn(colors = c("white", (grDevices::colorRampPalette(col_option))(99)),
+          na.value = "white")
       }
     } else {
-      g <- g + scale_fill_manual(
-        values = highlight_col, na.value = "#ffffff",
-        na.translate = TRUE
-      ) + guides(
-        colour = guide_colourbar(
-          barwidth = 4,
-          label = TRUE, ticks = TRUE, draw.ulim = TRUE, draw.llim = TRUE, order = 1
-        ),
-        size = guide_legend(reverse = TRUE, order = 2), stroke = guide_legend(
-          reverse = TRUE,
-          order = 3
-        )
-      )
+      g <- g + scale_fill_manual(values = highlight_col, na.value = "#ffffff",
+        na.translate = TRUE) + guides(colour = guide_colourbar(barwidth = 4,
+        label = TRUE, ticks = TRUE, draw.ulim = TRUE, draw.llim = TRUE, order = 1),
+        size = guide_legend(reverse = TRUE, order = 2), stroke = guide_legend(reverse = TRUE,
+          order = 3))
       df2 <- df
       if (standard_scale) {
         df2$scaled_means[df$pvals < 0.05] <- NA
-        g <- g + geom_point(aes(
-          x = Var2, y = Var1, colour = scaled_means,
-          size = scaled_means
-        ), data = df2, inherit_aes = FALSE, na_rm = TRUE)
+        g <- g + geom_point(aes(x = Var2, y = Var1, colour = scaled_means,
+          size = scaled_means), data = df2, inherit_aes = FALSE, na_rm = TRUE)
       } else {
         df2$means[df$pvals < 0.05] <- NA
         g <- g + geom_point(aes(x = Var2, y = Var1, colour = means, size = means),
-          data = df2, inherit_aes = FALSE, na_rm = TRUE
-        )
+          data = df2, inherit_aes = FALSE, na_rm = TRUE)
       }
       if (length(col_option) == 1) {
-        g <- g + scale_colour_gradientn(colors = (grDevices::colorRampPalette(c(
-          "white",
-          col_option
-        )))(100), na.value = "white")
+        g <- g + scale_colour_gradientn(colors = (grDevices::colorRampPalette(c("white",
+          col_option)))(100), na.value = "white")
       } else {
-        g <- g + scale_colour_gradientn(
-          colors = c("white", (grDevices::colorRampPalette(col_option))(99)),
-          na.value = "white"
-        )
+        g <- g + scale_colour_gradientn(colors = c("white", (grDevices::colorRampPalette(col_option))(99)),
+          na.value = "white")
       }
     }
     if (!is.null(interaction_scores) & (scale_alpha_by_interaction_scores ==
@@ -810,4 +649,4 @@ plot_cpdb <- function(
     }
     return(g)
   }
-}
+}
\ No newline at end of file