# NOT RUN {
## Replacing the reference gene values by
## averaged ones in the original data.
## => RES1 is new dataset.
## => GROUP1_mod in global environment is
## new labeling vector.
DAT1 <- pcrbatch(reps, fluo = 2:19, model = l5)
GROUP1 <- c("r1c1", "r1c1", "r2c1", "r2c1", "g1c1", "g1c1",
"r1s1", "r1s1", "r1s2", "r1s2", "r2s1", "r2s1",
"r2s2", "r2s2", "g1s1", "g1s1", "g1s2", "g1s2")
RES1 <- refmean(DAT1, GROUP1, which.eff = "sig", which.cp = "cpD2")
## Using three reference genes without replicates
## and then 'ratiobatch'.
## This can also be called in 'ratiobatch' directly
## with parameter 'refmean = TRUE'. See there.
## In this example, already averaged dataset and
## new labeling vector are supplied to 'ratiobatch',
## so one has to set 'refmean = FALSE'.
DAT2 <- pcrbatch(reps, fluo = 2:9, model = l5)
GROUP2 <- c("r1c1", "r2c1", "r3c1", "g1c1", "r1s1", "r2s1", "r3s1", "g1s1" )
RES2 <- refmean(DAT2, GROUP2, which.eff = "sig", which.cp = "cpD2")
ratiobatch(RES2, GROUP2_mod, refmean = FALSE)
## Comparison between 'refmean' ct-value arithmetic averaging
## and 'geNorm' relative quantities geometric averaging
## using data from the geNorm manual (2008), page 6.
## We will use HK1-HK3 as in the manual (no replicates).
## First we create a 'pcrbatch' dataset and then
## override the ct values with those of the manual and all
## efficiencies with E = 2. Sample A is considered as control sample.
DAT3 <- pcrbatch(reps, fluo = 2:17, model = l5)
DAT3[8, -1] <- c(32.10, 27.00, 34.90, 23.00,
33.30, 28.40, 36.10, 24.20,
31.00, 27.50, 34.00, 26.35,
30.50, 28.20, 33.00, 25.45)
DAT3[1, -1] <- 2
GROUP3 <- c("r1c1", "r2c1", "r3c1", "g1c1",
"r1s1", "r2s1", "r3s1", "g1s1",
"r1s2", "r2s2", "r3s2", "g1s2",
"r1s3", "r2s3", "r3s3", "g1s3")
RES3 <- refmean(DAT3, GROUP3, which.eff = "sig", which.cp = "cpD2")
ratiobatch(RES3, GROUP3_mod, which.cp = "cpD2",
which.eff = "sig", refmean = FALSE)
## Results:
## r1c1:g1c1:r1s1:g1s1 refmean 1.0497
## geNorm 1.0472 (2.351/2.245)
## r1c1:g1c1:r1s2:g1s2 refmean 0.0693
## geNorm 0.0695 (0.156/2.245)
## r1c1:g1c1:r1s3:g1s3 refmean 0.1081
## geNorm 0.1074 (0.241/2.245)
## Slight differences are due to rounding.
# }
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