diff --git a/R/plot_cpdb.R b/R/plot_cpdb.R
index 9e47ce4e..c2c3aa3b 100644
--- a/R/plot_cpdb.R
+++ b/R/plot_cpdb.R
@@ -9,7 +9,7 @@
 #' @param interaction_scores Data frame corresponding to `interaction_scores.txt` from CellPhoneDB version 5 onwards.
 #' @param cellsign Data frame corresponding to `CellSign.txt` from CellPhoneDB version 5 onwards.
 #' @param max_size max size of points.
-#' @param keep_significant_only logical. Default is FALSE. Switch to TRUE if you only want to plot the significant hits from cpdb.
+#' @param keep_significant_only logical. Default is TRUE. Switch to FALSE if you want to plot all the results from cpdb.
 #' @param splitby_key column name in the metadata/coldata table to split the spots by. Can only take columns with binary options. If specified, name to split by MUST be specified in the meta file provided to cpdb prior to analysis.
 #' @param gene_family default = NULL. some predefined group of genes. can take one (or several) of these default options: 'chemokines', 'Th1', 'Th2', 'Th17', 'Treg', 'costimulatory', 'coinhibitory', 'niche'. Also accepts name(s) of custom gene families.
 #' @param custom_gene_family default = NULL. If provided, will update the gene_family function with this custom entry. Both `gene_family` (name of the custom family) and `custom_gene_family` must be specified for this to work. Provide either a data.frame with column names as name of family and genes in rows or a named likes like : list('customfamily' = c('genea', 'geneb', 'genec'))
@@ -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
@@ -45,14 +45,15 @@
 #' @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)) {
@@ -71,7 +72,8 @@ plot_cpdb <- function(scdata, cell_type1, cell_type2, celltype_key, means, pvals
     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)]
   }
@@ -79,22 +81,28 @@ plot_cpdb <- function(scdata, cell_type1, cell_type2, celltype_key, means, pvals
   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)
     }
@@ -105,8 +113,10 @@ plot_cpdb <- function(scdata, cell_type1, cell_type2, celltype_key, means, pvals
       # 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)
       }
@@ -120,10 +130,14 @@ plot_cpdb <- function(scdata, cell_type1, cell_type2, celltype_key, means, pvals
       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]] == "")]
@@ -144,8 +158,10 @@ plot_cpdb <- function(scdata, cell_type1, cell_type2, celltype_key, means, pvals
       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 = "|"))
     }
@@ -159,60 +175,93 @@ plot_cpdb <- function(scdata, cell_type1, cell_type2, celltype_key, means, pvals
     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 (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 <- cellsign_mat[, col_start:ncol(cellsign_mat)] # too difficult to code is properly?
     }
+    # } else if (!is.null(cellsign)) {
+    #   cellsign_mat <- .prep_data_query_celltype(
+    #     .data = cellsign_mat, .query = query,
+    #     .cell_type = cell_type, .celltype = celltype, ...
+    #   )
+    # }
   }
   if (length(means_mat) == 0) {
     stop("Please check your options for splitby_key and your celltypes.")
+  } else {
+    if (!all(dim(pvals_mat) == dim(means_mat))) {
+      pvals_mat <- .prep_dimensions(pvals_mat, means_mat)
+    }
   }
   # rearrange the columns so that it interleaves the two groups
   if (!is.null(splitby_key)) {
@@ -322,8 +371,10 @@ plot_cpdb <- function(scdata, cell_type1, cell_type2, celltype_key, means, pvals
       }
     }
   }
-  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))
@@ -366,85 +417,117 @@ plot_cpdb <- function(scdata, cell_type1, cell_type2, celltype_key, means, pvals
       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))
-          } 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))
+            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, fill = significant,
-              colour = scaled_means, size = scaled_means, stroke = x_stroke,
-              alpha = interaction_scores))
+              g <- ggplot(df, aes(
+                x = Var2, y = Var1, color = significant,
+                fill = means, size = means, alpha = interaction_scores
+              ))
             }
+            default_style <- TRUE
           } 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))
+            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
+                ))
+              }
             } else {
-            g <- ggplot(df, aes(x = Var2, y = Var1, fill = significant,
-              colour = means, size = 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 {
+                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))
-          } 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))
+            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, fill = significant,
-              colour = scaled_means, size = scaled_means, stroke = x_stroke))
+              g <- ggplot(df, aes(
+                x = Var2, y = Var1, color = significant,
+                fill = means, size = means
+              ))
             }
+            default_style <- TRUE
           } else {
-            if (!is.null(highlight_size)) {
-            g <- ggplot(df, aes(x = Var2, y = Var1, fill = significant,
-              colour = means, size = means, stroke = highlight_size))
+            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
+                ))
+              }
             } else {
-            g <- ggplot(df, aes(x = Var2, y = Var1, fill = significant,
-              colour = means, size = 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 {
+                g <- ggplot(df, aes(
+                  x = Var2, y = Var1, fill = significant,
+                  colour = means, size = means, stroke = x_stroke
+                ))
+              }
             }
           }
-          }
         }
       }
     } else if (!is.null(cellsign)) {
@@ -456,168 +539,249 @@ plot_cpdb <- function(scdata, cell_type1, cell_type2, celltype_key, means, pvals
       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))
-          } 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))
+            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, fill = significant,
-              colour = scaled_means, size = scaled_means, stroke = x_stroke,
-              alpha = cellsign))
+              g <- ggplot(df, aes(
+                x = Var2, y = Var1, color = significant,
+                fill = means, size = means, alpha = cellsign
+              ))
             }
+            default_style <- TRUE
           } 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))
+            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
+                ))
+              }
             } else {
-            g <- ggplot(df, aes(x = Var2, y = Var1, fill = significant,
-              colour = means, size = 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 {
+                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))
-          } 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))
+            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, fill = significant,
-              colour = scaled_means, size = scaled_means, stroke = x_stroke))
+              g <- ggplot(df, aes(
+                x = Var2, y = Var1, color = significant,
+                fill = means, size = means
+              ))
             }
+            default_style <- TRUE
           } else {
-            if (!is.null(highlight_size)) {
-            g <- ggplot(df, aes(x = Var2, y = Var1, fill = significant,
-              colour = means, size = means, stroke = highlight_size))
+            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
+                ))
+              }
             } else {
-            g <- ggplot(df, aes(x = Var2, y = Var1, fill = significant,
-              colour = means, size = 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 {
+                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))
-          }
-          } else {
-          if (!is.null(highlight_size)) {
-            g <- ggplot(df, aes(x = Var2, y = Var1, fill = significant,
-            colour = means, size = means, stroke = highlight_size))
+            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
+              ))
+            }
           } else {
-            g <- ggplot(df, aes(x = Var2, y = Var1, fill = significant,
-            colour = means, size = 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 {
+              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 ==
@@ -640,4 +804,4 @@ plot_cpdb <- function(scdata, cell_type1, cell_type2, celltype_key, means, pvals
     }
     return(g)
   }
-}
\ No newline at end of file
+}
diff --git a/R/utils.R b/R/utils.R
index 08d99ce0..b195271d 100644
--- a/R/utils.R
+++ b/R/utils.R
@@ -10,6 +10,22 @@ DEFAULT_CLASS_COL <- 13
 DEFAULT_COL_START <- 12
 
 
+.prep_dimensions <- function(input, reference) {
+    requireNamespace("reshape2")
+    tmp_mat <- reference
+    tmp_mat <- (tmp_mat * 0) + 1
+    melted_A <- reshape2::melt(as.matrix(tmp_mat))
+    melted_B <- reshape2::melt(as.matrix(input))
+    rownames(melted_A) <- paste0(melted_A$Var1, DEFAULT_SEP, melted_A$Var2)
+    rownames(melted_B) <- paste0(melted_B$Var1, DEFAULT_SEP, melted_B$Var2)
+    melted_A[row.names(melted_B), ] <- melted_B
+    tmp_mat <- reshape2::dcast(melted_A, Var1 ~ Var2, value.var = "value")
+    rownames(tmp_mat) <- tmp_mat$Var1
+    tmp_mat <- tmp_mat[, -1]
+    tmp_mat <- tmp_mat[rownames(reference), colnames(reference)]
+    return(tmp_mat)
+}
+
 .prep_table <- function(data) {
     dat <- data
     rownames(dat) <- make.names(dat$interacting_pair, unique = TRUE)
@@ -36,18 +52,18 @@ DEFAULT_COL_START <- 12
 .prep_query_group <- function(data, genes = NULL, gene_family = NULL, custom_gene_family = NULL) {
     if (is.null(gene_family) & is.null(genes)) {
         query_group <- NULL
-        query_id <- grep("", data$interacting_pair)
-        query <- row.names(data)[query_id]
+        query_id <- grep("", data$interacting_pair, value = TRUE)
+        query <- row.names(data[data$interacting_pair %in% query_id, ])
     } else if (!is.null(gene_family) & !is.null(genes)) {
         stop("Please specify either genes or gene_family, not both")
     } else if (!is.null(gene_family) & is.null(genes)) {
-        chemokines <- grep("^CXC|CCL|CCR|CX3|XCL|XCR", data$interacting_pair)
-        th1 <- grep("IL2|IL12|IL18|IL27|IFNG|IL10|TNF$|TNF |LTA|LTB|STAT1|CCR5|CXCR3|IL12RB1|IFNGR1|TBX21|STAT4", data$interacting_pair)
-        th2 <- grep("IL4|IL5|IL25|IL10|IL13|AREG|STAT6|GATA3|IL4R", data$interacting_pair)
-        th17 <- grep("IL21|IL22|IL24|IL26|IL17A|IL17A|IL17F|IL17RA|IL10|RORC|RORA|STAT3|CCR4|CCR6|IL23RA|TGFB", data$interacting_pair)
-        treg <- grep("IL35|IL10|FOXP3|IL2RA|TGFB", data$interacting_pair)
-        costimulatory <- grep("CD86|CD80|CD48|LILRB2|LILRB4|TNF|CD2|ICAM|SLAM|LT[AB]|NECTIN2|CD40|CD70|CD27|CD28|CD58|TSLP|PVR|CD44|CD55|CD[1-9]", data$interacting_pair)
-        coinhibitory <- grep("SIRP|CD47|ICOS|TIGIT|CTLA4|PDCD1|CD274|LAG3|HAVCR|VSIR", data$interacting_pair)
+        chemokines <- grep("^CXC|CCL|CCR|CX3|XCL|XCR", data$interacting_pair, value = TRUE)
+        th1 <- grep("IL2|IL12|IL18|IL27|IFNG|IL10|TNF$|TNF |LTA|LTB|STAT1|CCR5|CXCR3|IL12RB1|IFNGR1|TBX21|STAT4", data$interacting_pair, value = TRUE)
+        th2 <- grep("IL4|IL5|IL25|IL10|IL13|AREG|STAT6|GATA3|IL4R", data$interacting_pair, value = TRUE)
+        th17 <- grep("IL21|IL22|IL24|IL26|IL17A|IL17A|IL17F|IL17RA|IL10|RORC|RORA|STAT3|CCR4|CCR6|IL23RA|TGFB", data$interacting_pair, value = TRUE)
+        treg <- grep("IL35|IL10|FOXP3|IL2RA|TGFB", data$interacting_pair, value = TRUE)
+        costimulatory <- grep("CD86|CD80|CD48|LILRB2|LILRB4|TNF|CD2|ICAM|SLAM|LT[AB]|NECTIN2|CD40|CD70|CD27|CD28|CD58|TSLP|PVR|CD44|CD55|CD[1-9]", data$interacting_pair, value = TRUE)
+        coinhibitory <- grep("SIRP|CD47|ICOS|TIGIT|CTLA4|PDCD1|CD274|LAG3|HAVCR|VSIR", data$interacting_pair, value = TRUE)
         query_group <- list(
             chemokines = chemokines,
             chemokine = chemokines,
@@ -60,11 +76,12 @@ DEFAULT_COL_START <- 12
             costimulation = costimulatory,
             coinhibition = coinhibitory
         )
+        query_group <- lapply(query_group, function(x) row.names(data[data$interacting_pair %in% x, ]))
         if (!is.null(custom_gene_family)) {
             cgf <- as.list(custom_gene_family)
             cgf <- lapply(cgf, function(x) {
-                q_id <- grep(paste(x, collapse = "|"), data$interacting_pair)
-                q <- row.names(data)[q_id]
+                q_id <- grep(paste(x, collapse = "|"), data$interacting_pair, value = TRUE)
+                q <- row.names(data[data$interacting_pair %in% q_id, ])
                 return(q)
             })
             query_group <- c(query_group, cgf)
@@ -72,8 +89,8 @@ DEFAULT_COL_START <- 12
         query <- NULL
     } else if (is.null(gene_family) & !is.null(genes)) {
         query_group <- NULL
-        query_id <- grep(paste(genes, collapse = "|"), data$interacting_pair)
-        query <- row.names(data)[query_id]
+        query_id <- grep(paste(genes, collapse = "|"), data$interacting_pair, value = TRUE)
+        query <- row.names(data[data$interacting_pair %in% query_id, ])
     }
     out <- list("query_group" = query_group, "query" = query)
     return(out)
@@ -99,7 +116,7 @@ DEFAULT_COL_START <- 12
 
 .prep_data_querygroup_celltype1 <- function(.data, .query_group, .gene_family, .cell_type, .celltype, ...) {
     dat <- suppressWarnings(tryCatch(
-        .data[.query_group[[tolower(.gene_family)]],
+        .data[rownames(.data) %in% .query_group[[tolower(.gene_family)]],
             grep(.cell_type, colnames(.data), useBytes = TRUE, ...),
             drop = FALSE,
         ],
@@ -110,7 +127,7 @@ DEFAULT_COL_START <- 12
                 )
             })
             colidx <- unique(do.call(c, colidx))
-            tmpm <- .data[.query_group[[tolower(.gene_family)]], colidx, drop = FALSE]
+            tmpm <- .data[rownames(.data) %in% .query_group[[tolower(.gene_family)]], colidx, drop = FALSE]
             return(tmpm)
         }
     ))
@@ -120,7 +137,7 @@ DEFAULT_COL_START <- 12
 
 .prep_data_querygroup_celltype2 <- function(.data, .query_group, .gene_family, .cell_type, .celltype, ...) {
     dat <- suppressWarnings(tryCatch(
-        .data[unlist(.query_group[c(tolower(.gene_family))], use.names = FALSE),
+        .data[rownames(.data) %in% unlist(.query_group[c(tolower(.gene_family))], use.names = FALSE),
             grep(.cell_type, colnames(.data), useBytes = TRUE, ...),
             drop = FALSE
         ],
@@ -131,7 +148,7 @@ DEFAULT_COL_START <- 12
                 )
             })
             colidx <- unique(do.call(c, colidx))
-            tmpm <- .data[unlist(.query_group[c(tolower(.gene_family))], use.names = FALSE), colidx, drop = FALSE]
+            tmpm <- .data[rownames(.data) %in% unlist(.query_group[c(tolower(.gene_family))], use.names = FALSE), colidx, drop = FALSE]
             return(tmpm)
         }
     ))
@@ -140,7 +157,7 @@ DEFAULT_COL_START <- 12
 }
 
 .prep_data_query_celltype <- function(.data, .query, .cell_type, .celltype, ...) {
-    dat <- suppressWarnings(tryCatch(.data[.query, grep(.cell_type, colnames(.data),
+    dat <- suppressWarnings(tryCatch(.data[rownames(.data) %in% .query, grep(.cell_type, colnames(.data),
         useBytes = TRUE, ...
     ), drop = FALSE], error = function(e) {
         colidx <- lapply(.celltype, function(z) {
@@ -150,7 +167,7 @@ DEFAULT_COL_START <- 12
             )
         })
         colidx <- unique(do.call(c, colidx))
-        tmpm <- .data[.query, colidx, drop = FALSE]
+        tmpm <- .data[rownames(.data) %in% .query, colidx, drop = FALSE]
         return(tmpm)
     }))
     dat <- dat[rowSums(is.na(dat)) == 0, ]
diff --git a/data/cpdb_output_degs.RData b/data/cpdb_output_degs.RData
new file mode 100644
index 00000000..4e555cd2
Binary files /dev/null and b/data/cpdb_output_degs.RData differ
diff --git a/data/cpdb_output_stat.RData b/data/cpdb_output_stat.RData
new file mode 100644
index 00000000..6ad90586
Binary files /dev/null and b/data/cpdb_output_stat.RData differ
diff --git a/man/plot_cpdb.Rd b/man/plot_cpdb.Rd
index 008dafae..01a3891f 100644
--- a/man/plot_cpdb.Rd
+++ b/man/plot_cpdb.Rd
@@ -57,7 +57,7 @@ plot_cpdb(
 
 \item{max_size}{max size of points.}
 
-\item{keep_significant_only}{logical. Default is FALSE. Switch to TRUE if you only want to plot the significant hits from cpdb.}
+\item{keep_significant_only}{logical. Default is TRUE. Switch to FALSE if you want to plot all the results from cpdb.}
 
 \item{splitby_key}{column name in the metadata/coldata table to split the spots by. Can only take columns with binary options. If specified, name to split by MUST be specified in the meta file provided to cpdb prior to analysis.}
 
@@ -109,7 +109,7 @@ Plotting CellPhoneDB results
 \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")
 }
 }
diff --git a/vignettes/vignette.rmd b/vignettes/vignette.rmd
index 9b923b85..c6a8ef52 100644
--- a/vignettes/vignette.rmd
+++ b/vignettes/vignette.rmd
@@ -65,14 +65,18 @@ If you are using results from `CellPhoneDB` `deg_analysis` mode from version >=
 # decon = pd.read_csv("deconvoluted.txt", sep="\t")
 
 # I've provided an example datasets
-data(cpdb_output)
-data(cpdb_output2)
+data(cpdb_output_stat) # ran with CellPhoneDB statistical analysis mode
+data(cpdb_output_degs) # ran with CellPhoneDB degs analysis mode
 ```
 
 ## Heatmap
 The original heatmap plot from `CellPhoneDB` can be achieved with this reimplemented function.
 ```{r, message = FALSE, warning = FALSE}
-plot_cpdb_heatmap(pvals = pvals2, cellheight = 10, cellwidth = 10)
+plot_cpdb_heatmap(pvals = pvals_stat, cellheight = 10, cellwidth = 10)
+```
+
+```{r, message = FALSE, warning = FALSE}
+plot_cpdb_heatmap(pvals = rel_int_degs, cellheight = 10, cellwidth = 10, degs_analysis = TRUE)
 ```
 
 The current heatmap is directional (check `count_network` and `interaction_edges` for more details in `return_tables = True`).
@@ -80,7 +84,7 @@ The current heatmap is directional (check `count_network` and `interaction_edges
 To obtain the heatmap where the interaction counts are not symmetrical, do:
 
 ```{r, message = FALSE, warning = FALSE}
-plot_cpdb_heatmap(pvals = pvals2, cellheight = 10, cellwidth = 10, symmetrical = FALSE)
+plot_cpdb_heatmap(pvals = pvals_stat, cellheight = 10, cellwidth = 10, symmetrical = FALSE)
 ```
 
 The values for the `symmetrical=FALSE` mode follow the direction of the L-R direction where it's always moleculeA:celltypeA -> moleculeB:celltypeB.
@@ -105,8 +109,22 @@ plot_cpdb(
     cell_type1="B cell",
     cell_type2=".",  # this means all cell-types
     celltype_key="celltype",
-    means=means2,
-    pvals=pvals2,
+    means=means_stat,
+    pvals=pvals_stat,
+    genes=c("PTPRC", "TNFSF13"),
+    title="interacting interactions!",
+)
+```
+
+```{r, message = FALSE, warning = FALSE, fig.width = 10, fig.height = 4}
+plot_cpdb(
+    scdata=kidneyimmune,
+    cell_type1="B cell",
+    cell_type2=".",  # this means all cell-types
+    celltype_key="celltype",
+    means=means_degs,
+    pvals=rel_int_degs,
+    degs_analysis=TRUE,
     genes=c("PTPRC", "TNFSF13"),
     title="interacting interactions!",
 )
@@ -114,14 +132,40 @@ plot_cpdb(
 
 Or don't specify either and it will try to plot all significant interactions.
 
+```{r, message = FALSE, warning = FALSE, fig.width = 10, fig.height = 10}
+plot_cpdb(
+    scdata=kidneyimmune,
+    cell_type1="B cell",
+    cell_type2=".",
+    celltype_key="celltype",
+    means=means_stat,
+    pvals=pvals_stat,
+)
+```
+
+```{r, message = FALSE, warning = FALSE, fig.width = 10, fig.height = 10}
+plot_cpdb(
+    scdata=kidneyimmune,
+    cell_type1="B cell",
+    cell_type2=".",  # this means all cell-types
+    celltype_key="celltype",
+    means=means_degs,
+    pvals=rel_int_degs,
+    degs_analysis=TRUE,
+    title="interacting interactions!",
+)
+```
+
+You can also try an alternative visualisation inspired by how `squidpy` displays the results:
+
 ```{r, message = FALSE, warning = FALSE, fig.width = 10, fig.height = 3}
 plot_cpdb(
     scdata=kidneyimmune,
     cell_type1="B cell",
     cell_type2=".",
     celltype_key="celltype",
-    means=means2,
-    pvals=pvals2,
+    means=means_stat,
+    pvals=pvals_stat,
     genes=c("PTPRC", "CD40", "CLEC2D"),
     default_style=FALSE
 )
@@ -130,6 +174,8 @@ plot_cpdb(
 you can also toggle options to `splitby_key` and `gene_family`:
 
 ```{r, message = FALSE, warning = FALSE, fig.width = 10, fig.height = 6}
+data(cpdb_output) # this is a different dataset where the "Experiment" was appended to the "celltype" 
+
 plot_cpdb(
     scdata = kidneyimmune,
     cell_type1 = "B cell",
@@ -238,7 +284,7 @@ Please help contribute to the interaction grouping list [here](https://docs.goog
 Credits to Ben Stewart for coming up with the base code!
 
 ```{r, message = FALSE, warning = FALSE, fig.width = 10, fig.height = 10}
-
+data(cpdb_output2) # legacy reasons
 plot_cpdb2(
     scdata = kidneyimmune,
     cell_type1 = "B cell",
@@ -277,9 +323,9 @@ plot_cpdb3(
     cell_type1 = "B cell",
     cell_type2 = "CD4T cell|MNPd",
     celltype_key = "celltype", # column name where the cell ids are located in the metadata
-    means = means2,
-    pvals = pvals2,
-    deconvoluted = decon2 # new options from here on specific to plot_cpdb3
+    means = means_stat,
+    pvals = pvals_stat,
+    deconvoluted = decon_stat # new options from here on specific to plot_cpdb3
 )
 ```
 
@@ -295,9 +341,9 @@ plot_cpdb4(
     cell_type1 = "NK",
     cell_type2 = "Mast",
     celltype_key = "celltype",
-    means = means2,
-    pvals = pvals2,
-    deconvoluted = decon2
+    means = means_stat,
+    pvals = pvals_stat,
+    deconvoluted = decon_stat
 )
 ```