Plotting dose response curves with ggplot2 and drc

Question

In biology we often want to plot dose response curves. The R package 'drc' is really useful and base graphics can easily handle 'drm models'. However, I would like to add my drm curves to a ggplot2.

My dataset:

 library("drc")
 library("reshape2")
 library("ggplot2")
 demo=structure(list(X = c(0, 1e-08, 3e-08, 1e-07, 3e-07, 1e-06, 3e-06, 
 1e-05, 3e-05, 1e-04, 3e-04), Y1 = c(0, 1, 12, 19, 28, 32, 35, 
 39, NA, 39, NA), Y2 = c(0, 0, 10, 18, 30, 35, 41, 43, NA, 43, 
 NA), Y3 = c(0, 4, 15, 22, 28, 35, 38, 44, NA, 44, NA)), .Names = c("X", 
"Y1", "Y2", "Y3"), class = "data.frame", row.names = c(NA, -11L
))

Using base graphics:

plot(drm(data = reshape2::melt(demo,id.vars = "X"),value~X,fct=LL.4(),na.action = na.omit),type="bars")

produces a nice 4-parameter dose response plot.

Trying to plot the same plot in ggplot2, I stumble upon 2 issues.

1. There is no way of directly adding the drm model curve. I need to rewrite the 4-PL as a function and add it in the form of a stat_function, which is cumbersome to say the least.

   ggplot(reshape2::melt(demo,id.vars = "X"),aes(X,value)) + 
     geom_point() + 
     stat_function(fun = function(x){
       drm_y=function(x, drm){
         coef(drm)[2]+((coef(drm)[3]-coef(drm)[2])/(1+exp((coef(drm)[1]*(log(x)-log(coef(drm)[4]))))))
       }
   + drm_y(x,drm = drm(data = reshape2::melt(demo,id.vars = "X"), value~X, fct=LL.4(), na.action = na.omit))
    })
   

2. If that wasn't enough it only works if scale_x is continuous. If I want to add scale_x_log10(), I get: Warning message: In log(x): NaNs produced.

I realise that log10(0) = -Inf but there are ways of handling this. Either (as is the case with plot.drc) the x=0 value is plotted on the x-axis essentially as 1/100 of the pre-lowest x-value. (demo$X[which.min(demo$X)+1]/100) or as in GraphPad Prism, the 0s are omitted from the dose response curve entirely.

My questions are:

1. Is there a way of plotting drm models in ggplot2 directly?

2. How can I link a dataset with its corresponding 4-PL curvefit so that they will be plotted in the same colour?

Answer 1

A recent paper from the authors of the drc package included instructions for extracting parameters for use by ggplot2. They don't work within ggplot2 but extract data from the model. This is their solution applied to your data.

demo1 <- reshape2::melt(demo,id.vars = "X") # get numbers ready for use.
demo.LL.4 <- drm(data = demo1,value~X,fct=LL.4(),na.action = na.omit) # run model.

The predict function can extract the parameters from drm models. It isn't compatible with multiple curves that were fit using curveid.

# predictions and confidence intervals.
demo.fits <- expand.grid(conc=exp(seq(log(1.00e-04), log(1.00e-09), length=100))) 
# new data with predictions
pm <- predict(demo.LL.4, newdata=demo.fits, interval="confidence") 
    demo.fits$p <- pm[,1]
    demo.fits$pmin <- pm[,2]
    demo.fits$pmax <- pm[,3]

They advise shifting the zero concentration to avoid issues with coord_trans.

demo1$XX <- demo1$X
demo1$XX[demo1$XX == 0] <- 1.00e-09

Then comes plotting the curve, omitting geom_ribbon stops the errors from being drawn.

ggplot(demo1, aes(x = XX, y = value)) +
  geom_point() +
  geom_ribbon(data=demo.fits, aes(x=conc, y=p, ymin=pmin, ymax=pmax), alpha=0.2) +
  geom_line(data=demo.fits, aes(x=conc, y=p)) +
  coord_trans(x="log") 

To graph multiple curves together the process can be repeated. Add IDs to each set.

demo.fits_1 <- data.frame(label = "curve1", demo.fits)

Then use rbind to combine all the extracted parameters. From there ggplot can handle colours.