ggplot2 legend for abline and stat_smooth

问题:

I have some problems with ggplot legends, here is my first code with only the legend for corrGenes, which is fine.

gene1=c(1.041,0.699,0.602,0.602,2.585,0.602,1.000,0.602,1.230,1.176,0.699,0.477,1.322)
    BIME = c(0.477,0.477,0.301,0.477,2.398,0.301,0.602,0.301,0.602,0.699,0.602,0.477,1.176)
    corrGenes=c(0.922,0.982,0.934,0.917,0.993,0.697,0.000,0.440,0.859,0.788,0.912,0.687,0.894)

DF=data.frame(gene1,BIME,corrGenes)

plot= ggplot(data=DF,aes(x=gene1,y=BIME))+
  geom_point(aes(colour=corrGenes),size=5)+
  ylab("BIME normalized counts (log10(RPKM))")+
  xlab("gene1 normalized counts (log10(RPKM))")

When I add abline and smooth, I get the correct plot with :

plot= ggplot(data=DF,aes(x=gene1,y=BIME))+
  geom_point(aes(colour=corrGenes),size=5)+
  geom_abline(intercept=0, slope=1)+
  stat_smooth(method = "lm",se=FALSE)+
  ylab("BIME normalized counts (log10(RPKM))")+
  xlab("gene1 normalized counts (log10(RPKM))")

but no way to get the legend for them, I tried and many other combinations:

plot= ggplot(data=DF,aes(x=gene1,y=BIME))+
  geom_point(aes(colour=corrGenes),size=5)+
  geom_abline(aes(colour="best"),intercept=0, slope=1)+
  stat_smooth(aes(colour="data"),method = "lm",se=FALSE)+
  scale_colour_manual(name="Fit", values=c("data"="blue", "best"="black"))+
  ylab("BIME normalized counts (log10(RPKM))")+
  xlab("gene1 normalized counts (log10(RPKM))")

If anyone has an idea to solve this tiny but very annoying problem, it would be very helpfull!

回答1:

The show_guide=TRUE argument should display the legends for both geom_abline and stat_smooth. Try running the below code.

plot= ggplot(data=DF,aes(x=gene1,y=BIME))+
geom_point(aes(colour=corrGenes),size=5)+
geom_abline(aes(colour="best"),intercept=0, slope=1, show_guide=TRUE)+
stat_smooth(aes(colour="data"),method = "lm",se=FALSE, show_guide=TRUE)+
scale_colour_manual(name="Fit", values=c("data"="blue", "best"="black"))+
ylab("BIME normalized counts (log10(RPKM))")+
xlab("gene1 normalized counts (log10(RPKM))")

回答2:

Not sure if this is the best solution, but I was able to tell ggplot to have two scales, one for the colours (your points), the other one for the fill colour. Which fill colour you are probably asking? The one I added in the aes for the two lines:

plot = ggplot(data=DF,aes(x=gene1,y=BIME)) + 
  geom_point(size=5, aes(colour=corrGenes)) +
  geom_abline(aes(fill="black"),intercept=0, slope=1) + 
  stat_smooth(aes(fill="blue"), method = "lm",se=FALSE) +
  scale_fill_manual(name='My Lines', values=c("black", "blue"))+
  ylab("BIME normalized counts (log10(RPKM))")+
  xlab("gene1 normalized counts (log10(RPKM))")

回答3:

Finally, I found anther way using a trick. First, I've computed the linear regression and convert the results to a data frame which I add my best fit (Intercept = 0 and slope =1), then I added a column for type of data (data or best).

modele = lm(BIME ~ gene1, data=DF)
coefs = data.frame(intercept=coef(modele)[1],slope=coef(modele)[2])
coefs= rbind(coefs,list(0,1))
regression=as.factor(c('data','best'))
coefs=cbind(coefs,regression)

then I plotted it with a unique geom_abline command and moving the DF from ggplot() to geom_point() and used the linetype parameter to differenciate the two lines :

plot = ggplot()+
  geom_point(data=pointSameStrandDF,aes(x=gene1,y=BIME,colour=corrGenes),size=5)+
  geom_abline(data=coefs, aes(intercept=intercept,slope=slope,linetype=regression), show_guide=TRUE)+
  ylab("BIME normalized counts (log10(RPKM))")+
  xlab("gene1 normalized counts (log10(RPKM))")

There is maybe a way to use colors for those 2 lines, but I can't find out how?

Thanks for your help guys!