Computing Ghrelin calibration curve using R
| cell1 | cell2 |
| cell3 | cell4 |
The calibration curve can be created using R, and libracy “drc”. Optionally, one can use library “sfmisc” for formatting of the labels on plot axis.
The R code is attached below.
The example assumes the data to be available in file “ghrelin_conc std_a std_b avg.csv”
The measured data:
{| border=“1” class=“sortable” !Ghrelin (ng/ml)!!Standard a!!Standard b |- |1000000||-0.040596823||-0.052699697 |- |100000||0.136105144||0.119766263 |- |10000||0.61356354||0.606906959 |- |1000||0.846543873||0.839887292 |- |100||0.887693646||0.88345764 |- |0||0.896770802||0.896165658 |}
##### Install libraries
install.packages("drc")
install.packages("sfsmisc")
require(drc)
library(sfsmisc)
##### Read the data
hormone.data <- read.csv("ghrelin_conc std_a std_b avg.csv")
hormone.data <- hormone.data[,1:3]
colnames(hormone.data)[1:3] <- c("Concentration","Response_1", "Response_2")
##### Reorganize the data
hormone.data <- reshape(hormone.data, varying=c("Response_1","Response_2"), direction="long", v.names=c("Response"))
hormone.data <- hormone.data[,c("Concentration", "Response")]
##### Fitting the model (4-parameter log-logistic function)
hormone.data.model <- drm(Response ~ Concentration, data = hormone.data, fct = LL.4())
summary(hormone.data.model)
The calibration curve can be plotted using the commands below:
##### Plotting a nice plot par(pty="s", mar=c(5,5,1,1)) plot(hormone.data.model, type="confidence", cex.lab=2, axes=F, xlim=c(-10,10^6)) axis(side=1, at=hormone.data[1:6,1], labels=pretty10exp(hormone.data[1:6,1]), cex.axis=1.2) axis(side=2, at=seq(0,1,0.2), labels=seq(0,1,0.2)) plot(hormone.data.model, type="all", add=T, pch=21, col="red", lwd=1, cex=2, bg="green")
##### Computing the concentration from the response, for instance for a response=0.1, and alpha=1-0.95
ED(hormone.data.model, respLev=0.1, interval=“delta”, type=“absolute”, level=0.95)