ggcorrplot<-function(df, var, color="#FFFFFF00", stat="signif"){
  m.df<-df %>% spread(Cyt, Value) %>% select(-pats)
  mcor<-cor(m.df, m.df, use="pairwise.complete.obs") # Por defecto usa el método de Pearson.
  mpval<-Hmisc::rcorr(as.matrix(m.df))$P

  df<-mcor %>% as.data.frame() %>% add_column(Var1=rownames(mcor),.before=1) %>%
    gather(Var2, Value, -Var1)
  df.pval<-mpval %>% as.data.frame() %>% add_column(Var1=rownames(mpval),.before=1) %>%
    gather(Var2, Value, -Var1)

  if (stat=="signif"){
    df.pval$Value<-gtools::stars.pval(as.numeric(df.pval$Value))
  }

  order<- mcor %>% as.data.frame() %>% add_column(Var1=rownames(mcor),.before=1) %>% clustsort

  ggplot(df, aes(Var1, Var2, fill=Value))+
    scale_x_discrete(limits=order$x)+
    scale_y_discrete(limits=order$y)+
    theme_heatmap(line.color=color)+
    geom_text(data=df.pval, aes(label=round(Value, 2)))
}