Using ggplot to draw the LD50 graph

UPDATE: As of ggplot 2.0.0, released in Dec 2015, to use the geom_smooth() ggplot function, there is a need to put the method arguments (method.args = list()) into a list as detailed below.
As of 19th of Jan 2016, it means that the scripts here that use the geom_smooth() don't work. I will repair them all but it'll take a few days. Thanks to the commentors for sorting this out.


As mentioned previously, we do a lot of drug testing in our laboratory.
Here is the same data plotted with ggplot.
The experimental set up, done by a student in the lab, is as follows:

• using some cells and add various concentrations of a novel drug
• leave for 48 hours
• then measure how many cells are dead
• the experiment was done four times with the doses improved each time

Here is the graph and calculated LD50 - a measure of how good the drug is:

Here is the script:

# START of SCRIPT

library(ggplot2)

###  this is the data   ### 

# data from four experiments

conc <- c(5.00E-07, 1.00E-06, 1.00E-05, 

          5.00E-07, 1.00E-06, 5.00E-06, 1.00E-05, 2.00E-05, 

          5.00E-07, 1.00E-06, 2.50E-06, 5.00E-06, 1.00E-05, 

          5.00E-07, 1.00E-06, 2.50E-06, 5.00E-06, 1.00E-05)

dead.cells <- c(34.6, 47.7, 81.7, 

                37.6, 55.7, 89.1, 84.3, 85.2, 

                34.4, 46.1, 76.2, 84.3, 84.1, 

                24.5, 26.1, 60.6, 82.7, 87)

# transform the data to make it postive and put into a data frame for fitting 

data <- as.data.frame(conc)   # create the data frame

data$dead.cells <- dead.cells

data$nM <- data$conc * 1000000000

data$log.nM <- log10(data$nM) 

###  fit the data  ###

# make sure logconc remains positive, otherwise multiply to keep positive values

# (such as in this example where the iconc is multiplied by 1000

fit <- nls(dead.cells ~ bot+(top-bot)/(1+(log.nM/LD50)^slope),

           data = data,

           start=list(bot=20, top=95, LD50=3, slope=-12))

m <- coef(fit)

val <- format((10^m[3]),dig=0)

###  ggplot the results  ###

p <- ggplot(data=data,          # specify the data frame with data

            aes(x=nM, y=dead.cells)) +   # specify x and y

  geom_point() +          # make a scatter plot

  scale_x_log10(breaks = c(500, 1000, 2500, 5000, 10000, 20000))+

  xlab("Drug concentration (nM)") +   # label x-axis

  ylab("Dead cells (% of cells)") +    # label y-axis

  ggtitle("Drug Dose Response and LD50") +  # add a title

  theme_bw() +      # a simple theme

  expand_limits(y=c(20,100))   # customise the y-axis

# Add the line to graph using methods.args (New: Jan 2016)

p <- p +  geom_smooth(method = "nls",

                      method.args = list(formula = y ~ bot+(top-bot)/(1+( x / LD50)^slope), 

                                    start=list(bot=20, top=95, LD50=3, slope=-12)),

                      se = FALSE)

# Add the text with the LD50 to the graph. 

p <- p+ annotate(geom="text", x=7000, y= 60, label="LD50(nM): ",  color="red") +

   annotate(geom="text", x=9800, y= 60, label=val,  color="red")

p # show the plot

#END OF SCRIPT