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28 lines
1.1 KiB
28 lines
1.1 KiB
ggcorrplot<-function(df, var, color="#FFFFFF00", stat="signif"){
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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))$P
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df<-mcor %>% as.data.frame() %>% add_column(Var1=rownames(mcor),.before=1) %>%
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gather(Var2, Value, -Var1)
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df.pval<-mpval %>% as.data.frame() %>% add_column(Var1=rownames(mpval),.before=1) %>%
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gather(Var2, Value, -Var1)
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df.pval$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$Value)
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}
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order<- mcor %>% as.data.frame() %>% add_column(Var1=rownames(mcor),.before=1) %>% clustsort
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ggplot(df, aes(Var1, Var2))+
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scale_x_discrete(limits=order$x)+
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scale_y_discrete(limits=order$y)+
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geom_tile(aes(fill=Value), color=color)+
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geom_text(data=df.pval, aes(label=Value))+
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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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