How to create a (total) Ghrelin calibration curve?

Version 9
by (unknown)
Version 10
by (unknown)

Deletions or items before changed

Additions or items after changed

1 == Computing Ghrelin calibration curve using R ==
2 ||cell1||cell2||
3 ||cell3||cell4||
4
5
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.
8
9 The example assumes the data to be available in file "ghrelin_conc std_a std_b avg.csv"
10
11 The measured data:
12
13 -
{| border="1" class="sortable"
+
|| border="1" class="sortable"
14 !Ghrelin (ng/ml)!!Standard a!!Standard b
15 |-
16 |1000000||-0.040596823||-0.052699697
17 |-
18 |100000||0.136105144||0.119766263
19 |-
20 |10000||0.61356354||0.606906959
21 |-
22 |1000||0.846543873||0.839887292
23 |-
24 |100||0.887693646||0.88345764
25 |-
26 |0||0.896770802||0.896165658
27 -
|}
+
||
28
29 {{{
30
31 ##### Install libraries
32 install.packages("drc")
33 install.packages("sfsmisc")
34 require(drc)
35 library(sfsmisc)
36
37 ##### Read the data
38 hormone.data <- read.csv("ghrelin_conc std_a std_b avg.csv")
39 hormone.data <- hormone.data[,1:3]
40 colnames(hormone.data)[1:3] <- c("Concentration","Response_1", "Response_2")
41
42 ##### Reorganize the data
43 hormone.data <- reshape(hormone.data, varying=c("Response_1","Response_2"), direction="long", v.names=c("Response"))
44 hormone.data <- hormone.data[,c("Concentration", "Response")]
45
46 ##### Fitting the model (4-parameter log-logistic function)
47 hormone.data.model <- drm(Response ~ Concentration, data = hormone.data, fct = LL.4())
48 summary(hormone.data.model)
49
50 }}}
51
52 The calibration curve can be plotted using the commands below:
53
54 {{{
55 ##### Plotting a nice plot
56 par(pty="s", mar=c(5,5,1,1))
57 plot(hormone.data.model, type="confidence", cex.lab=2, axes=F, xlim=c(-10,10^6))
58 axis(side=1, at=hormone.data[1:6,1], labels=pretty10exp(hormone.data[1:6,1]), cex.axis=1.2)
59 axis(side=2, at=seq(0,1,0.2), labels=seq(0,1,0.2))
60 plot(hormone.data.model, type="all", add=T, pch=21, col="red", lwd=1, cex=2, bg="green")
61 }}}
62
63
64
65
66 ##### Computing the concentration from the response, for instance for a response=0.1, and alpha=1-0.95
67 ED(hormone.data.model, respLev=0.1, interval="delta", type="absolute", level=0.95)