diff --git a/R/ggheatmap.R b/R/ggheatmap.R
index b480c20..6d22a32 100644
--- a/R/ggheatmap.R
+++ b/R/ggheatmap.R
@@ -1,10 +1,10 @@
-ggheatmap<-function(df, x=NULL, y=NULL, value=NULL, grouping="mean", exclude_group=NULL, scale="none"){
+ggheatmap<-function(df, x=NULL, y=NULL, value=NULL, grouping="mean", exclude_group=NULL, scale="none",color="#FFFFFF00"){
   if (is.null(x)){x=colnames(df)[1]}
   if (is.null(y)){y=colnames(df)[2]}
   if (is.null(value)){value=colnames(df)[3]}
 
   df<-rename(df, "VarX"=all_of(x), "VarY"=all_of(y), "Value"=all_of(value))
-  
+
   if (is.null(exclude_group)){
     df<-df %>% group_by(VarX,VarY)
   }else{
@@ -17,7 +17,7 @@ ggheatmap<-function(df, x=NULL, y=NULL, value=NULL, grouping="mean", exclude_gro
     df<-df %>% summarise(Value=median(Value)) %>% ungroup
   }
   order<-clustsort(df %>% spread(VarY,Value) %>% select(!all_of(exclude_group)) %>% as.data.frame)
-  
+
   if (scale != "none"){
     if (scale == "rows"){
       cols<-unique(df$VarY)
@@ -32,12 +32,16 @@ ggheatmap<-function(df, x=NULL, y=NULL, value=NULL, grouping="mean", exclude_gro
       df<-gather(sca.df, VarX, Value, all_of(cols))
     }
   }
-  
+
   df$VarX<-factor(df$VarX, levels=order$x)
   df$VarY<-factor(df$VarY, levels=order$y)
-  
+
   df %>%
     ggplot(aes(VarX, VarY, fill=Value))+
     labs(x=x, y=y)+
-    theme_heatmap()
+    geom_tile(aes(fill=Value), color=color)+
+    scale_fill_gradientn(colors=col2(200))+
+    theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust=0.5),
+          panel.background = element_blank(),
+          axis.ticks = element_blank())
 }
diff --git a/man/ggheatmap.Rd b/man/ggheatmap.Rd
index 80ae147..7264618 100644
--- a/man/ggheatmap.Rd
+++ b/man/ggheatmap.Rd
@@ -2,7 +2,7 @@
 \alias{ggheatmap}
 \title{ggheatmap}
 \usage{
-ggheatmap(df, x=NULL, y=NULL, value=NULL, grouping="mean", exclude_group=NULL, scale="none")
+ggheatmap(df, x=NULL, y=NULL, value=NULL, grouping="mean", exclude_group=NULL, scale="none", color="#FFFFFF00")
 }
 \arguments{
   \item{df}{A data frame in "long" format.}
@@ -12,6 +12,7 @@ ggheatmap(df, x=NULL, y=NULL, value=NULL, grouping="mean", exclude_group=NULL, s
   \item{grouping}{The function that will be used to group if there is more than one observation for each X-Y combination. "mean" or "median" are posible, defaulting to "mean".}
   \item{exclude_group}{If you want to exclude columns from grouping, you can introduce a character vector here. Useful if you want to facet the plot.}
   \item{scale}{Escales (z-score) by "rows" or "cols" if diferent of "none" (default).}
+  \item{color}{The color of the lines of geom_tile (the border). By default, its transparent.}
 }
 \description{
 Generates a heatmap using ggplot for a dataframe.
diff --git a/vignettes/citfuns.Rmd b/vignettes/citfuns.Rmd
index 39e736c..205e189 100644
--- a/vignettes/citfuns.Rmd
+++ b/vignettes/citfuns.Rmd
@@ -28,7 +28,7 @@ You will also need [Git](https://git-scm.com/downloads) installed in your comput
 
 Now we are ready to install CitFuns package:
 ```{r, eval=F}
-devtools::install_git("https://git.ratg.cat/marcelcosta/CitFuns.git")
+devtools::install_git("https://git.ratg.cat/marcelcosta/CitFuns.git", build_vignettes = T)
 ```
 
 ## Update
@@ -123,8 +123,15 @@ By default, ggcorrplot converts the p-value into *star* significance equivalence
 ggcorrplot(df, var = "Cyt", value = "Value", color = "white", stat="pval")
 ```
 
+ggcorrplot uses "pearson" by default to obtain a correlation coeficient, although "spearman" is also available.
+
+```{r, fig.width=5, fig.height=4}
+ggcorrplot(df, var = "Cyt", value = "Value", color = "white", stat="pval", method="spearman")
+```
+
+
 Finally, you can show only the upper part or the lower part of the specular matrix.
 
 ```{r, fig.width=5, fig.height=4}
 ggcorrplot(df, var = "Cyt", value = "Value", color = "black", tri="lower")
-```
\ No newline at end of file
+```