How to create a (total) Ghrelin calibration curve?

Version 8
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Version 9
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1 == Computing Ghrelin calibration curve using R ==
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6 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.
7 The R code is attached below.
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9 The example assumes the data to be available in file "ghrelin_conc std_a std_b avg.csv"
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11 The measured data:
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{|
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! Ghrelin (ng/ml) || Standard a || Standard b
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{| border="1" class="sortable"
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!Ghrelin (ng/ml)!!Standard a!!Standard b
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| 1000000 || -0.040596823 || -0.052699697
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|1000000||-0.040596823||-0.052699697
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| 100000 || 0.136105144 || 0.119766263
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|100000||0.136105144||0.119766263
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| 10000 || 0.61356354 || 0.606906959
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|10000||0.61356354||0.606906959
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| 1000 || 0.846543873 || 0.839887292
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|1000||0.846543873||0.839887292
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| 100 || 0.887693646 || 0.88345764
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|100||0.887693646||0.88345764
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| 0 || 0.896770802 || 0.896165658
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|0||0.896770802||0.896165658
27 |}
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30 {{{
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32 ##### Install libraries
33 install.packages("drc")
34 install.packages("sfsmisc")
35 require(drc)
36 library(sfsmisc)
37
38 ##### Read the data
39 hormone.data <- read.csv("ghrelin_conc std_a std_b avg.csv")
40 hormone.data <- hormone.data[,1:3]
41 colnames(hormone.data)[1:3] <- c("Concentration","Response_1", "Response_2")
42
43 ##### Reorganize the data
44 hormone.data <- reshape(hormone.data, varying=c("Response_1","Response_2"), direction="long", v.names=c("Response"))
45 hormone.data <- hormone.data[,c("Concentration", "Response")]
46
47 ##### Fitting the model (4-parameter log-logistic function)
48 hormone.data.model <- drm(Response ~ Concentration, data = hormone.data, fct = LL.4())
49 summary(hormone.data.model)
50
51 }}}
52
53 The calibration curve can be plotted using the commands below:
54
55 {{{
56 ##### Plotting a nice plot
57 par(pty="s", mar=c(5,5,1,1))
58 plot(hormone.data.model, type="confidence", cex.lab=2, axes=F, xlim=c(-10,10^6))
59 axis(side=1, at=hormone.data[1:6,1], labels=pretty10exp(hormone.data[1:6,1]), cex.axis=1.2)
60 axis(side=2, at=seq(0,1,0.2), labels=seq(0,1,0.2))
61 plot(hormone.data.model, type="all", add=T, pch=21, col="red", lwd=1, cex=2, bg="green")
62 }}}
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66
67 ##### Computing the concentration from the response, for instance for a response=0.1, and alpha=1-0.95
68 ED(hormone.data.model, respLev=0.1, interval="delta", type="absolute", level=0.95)