diff --git a/R/plot_cpdb.R b/R/plot_cpdb.R
index 706f654..72f000f 100644
--- a/R/plot_cpdb.R
+++ b/R/plot_cpdb.R
@@ -271,13 +271,7 @@ plot_cpdb <- function(
     # .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)) {
     if (length(groups) > 0) {
@@ -555,7 +549,11 @@ plot_cpdb <- function(
       if (filter_by_cellsign == TRUE) {
         requireNamespace("dplyr")
         df <- df %>%
-          dplyr::filter(cellsign >= 1)
+          dplyr::filter(!is.na(cellsign))
+        df <- df %>%
+          dplyr::group_by(Var1) %>%
+          dplyr::filter(dplyr::n_distinct(significant) > 1) %>%
+          as.data.frame()
       }
       if (scale_alpha_by_cellsign == TRUE) {
         if (default_style) {
@@ -786,11 +784,11 @@ plot_cpdb <- function(
         g <- g + geom_point(aes(
           x = Var2, y = Var1, colour = scaled_means,
           size = scaled_means
-        ), data = df2, inherit_aes = FALSE, na_rm = TRUE)
+        ), 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) {
diff --git a/R/utils.R b/R/utils.R
index d520396..87b2021 100644
--- a/R/utils.R
+++ b/R/utils.R
@@ -12,22 +12,6 @@ SPECIAL_SEP <- paste0(rep(DEFAULT_SEP, 3), collapse = "")
 DEFAULT_CPDB_SEP <- "|"
 
 
-.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$Var1
-    tmp_mat <- tmp_mat[, -1]
-    tmp_mat <- tmp_mat[rownames(reference), colnames(reference)]
-    return(tmp_mat)
-}
-
 .prep_table <- function(data) {
     dat <- data
     rownames(dat) <- paste0(dat$id_cp_interaction, SPECIAL_SEP, dat$interacting_pair)
diff --git a/vignettes/vignette_v5.rmd b/vignettes/vignette_v5.rmd
index 267d869..d0f624c 100644
--- a/vignettes/vignette_v5.rmd
+++ b/vignettes/vignette_v5.rmd
@@ -15,7 +15,7 @@ vignette: >
 
 # CellPhoneDB v5 results
 
-From version 5 of [CellPhoneDB](https://www.github.com/ventolab/cellphonedb), there is a new output file - `interaction_scores`. 
+From version 5 of [CellPhoneDB](https://www.github.com/ventolab/cellphonedb), there are two new output files - `interaction_scores` and `CellSign`. 
 
 According to the official repository, this table corresponds to:
 
@@ -31,7 +31,7 @@ To score interactions CellPhoneDB v5 employs the following protocol:
 
 `cellsign`: accepts the new `CellSign` data.
 
-The aim of the CellSign module is to identify activated receptors and prioritise high-confidence interactions by leveraging the activity of the downstream transcription factors (TFs). CellSign relies on a database of receptors linked to their putative downstream TFs.
+The aim of the CellSign module is to identify activated receptors and prioritise high-confidence interactions by leveraging the activity of the downstream transcription factors (TFs). CellSign relies on a database of receptors linked to their putative downstream TFs. This is a binary table where 1 means it's a hit. In `ktplots/ktplotspy`, we convert all other values to 0.5 adn fit it to the `alpha` parameter for visualisation.
 
 `ktplots` will support these output via inclusion into the existing `plot_cpdb` function. We will gradually enable their functionality across the other functions, as well as with in the python package eventually.