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41 lines
1.4 KiB
41 lines
1.4 KiB
ggcorrplot<-function(df, var, color="#FFFFFF00", stat="signif", tri="all"){
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m.df<-df %>% spread(Cyt, Value) %>% select(-pats)
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mcor<-cor(m.df, m.df, use="pairwise.complete.obs") # Por defecto usa el método de Pearson.
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mpval<-Hmisc::rcorr(as.matrix(m.df))$P
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order<- mcor %>% as.data.frame() %>% add_column(Var1=rownames(mcor),.before=1) %>% clustsort
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mor<-mcor[order$y, order$x]
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mpval<-mpval[order$y, order$x]
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df<-mcor %>% as.data.frame()
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if (tri == "lower"){
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df[lower.tri(df, diag=T)]<-NA
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}
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df<-df %>% add_column(Var1=rownames(mcor),.before=1) %>%
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gather(Var2, Value, -Var1) %>% filter(!is.na(Value))
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df.pval<-mpval %>% as.data.frame()
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if (tri == "lower"){
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df.pval[lower.tri(df.pval, diag=T)]<-NA
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}
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df.pval<-df.pval %>% add_column(Var1=rownames(mpval),.before=1) %>%
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gather(Var2, Value, -Var1) %>% filter(!is.na(Value))
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df.pval$Value<-round(df.pval$Value, 3)
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if (stat=="signif"){
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df.pval$Value<-gtools::stars.pval(df.pval$Value)
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}
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df$Var1<-factor(df$Var1, levels=order$x)
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df$Var2<-factor(df$Var2, levels=order$y)
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ggplot(df, aes(Var1, Var2))+
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geom_tile(aes(fill=Value), color=color)+
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geom_text(data=df.pval, aes(label=Value), color="white")+
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scale_fill_gradientn(colors=col2(200))+
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theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust=0.5),
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panel.background = element_blank(),
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axis.ticks = element_blank())
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}
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