# NOT RUN {
## One reference gene, one gene of interest,
## one control and one treatment sample with
## 4 replicates each => 1 x Ratio = 1.
DAT1 <- pcrbatch(reps, fluo = c(2:9, 2:9), model = l5)
GROUP1 <- c("g1c1", "g1c1", "g1c1", "g1c1",
"g1s1", "g1s1", "g1s1", "g1s1",
"r1c1", "r1c1", "r1c1", "r1c1",
"r1s1", "r1s1", "r1s1", "r1s1")
ratiobatch(DAT1, GROUP1, refmean = FALSE)
## One reference gene, one gene of interest,
## two control and two treatment samples with
## 2 replicates each => 4 x Ratio = 1.
DAT2 <- pcrbatch(reps, fluo = c(2:9, 2:9), model = l5)
GROUP2 <- c("g1c1", "g1c1", "g1c2", "g1c2",
"g1s1", "g1s1", "g1s2", "g1s2",
"r1c1", "r1c1", "r1c2", "r1c2",
"r1s1", "r1s1", "r1s2", "r1s2")
ratiobatch(DAT2, GROUP2, refmean = FALSE)
## Two reference genes, one gene of interest,
## one control and one treatment samples with
## 4 replicates each => 2 x Ratio = 1.
DAT3 <- pcrbatch(reps, fluo = c(2:9, 2:9, 2:9), model = l5)
GROUP3 <- c("g1c1", "g1c1", "g1c1", "g1c1",
"g1s1", "g1s1", "g1s1", "g1s1",
"r1c1", "r1c1", "r1c1", "r1c1",
"r1s1", "r1s1", "r1s1", "r1s1",
"r2c1", "r2c1", "r2c1", "r2c1",
"r2s1", "r2s1", "r2s1", "r2s1")
ratiobatch(DAT3, GROUP3, refmean = FALSE)
## Two reference genes, one gene of interest,
## one control and one treatment samples with
## 4 replicates each.
## Reference genes are averaged => 1 x Ratio = 1.
DAT4 <- pcrbatch(reps, fluo = c(2:9, 2:9, 2:9), model = l5)
GROUP4 <- c("g1c1", "g1c1", "g1c1", "g1c1",
"g1s1", "g1s1", "g1s1", "g1s1",
"r1c1", "r1c1", "r1c1", "r1c1",
"r1s1", "r1s1", "r1s1", "r1s1",
"r2c1", "r2c1", "r2c1", "r2c1",
"r2s1", "r2s1", "r2s1", "r2s1")
ratiobatch(DAT4, GROUP4, refmean = TRUE)
## Same as above, but use same efficiency E = 2.
ratiobatch(DAT4, GROUP4, which.eff = 2)
## No reference genes, two genes-of-interest,
## two control and two treatment samples with
## 2 replicates each, efficiency from sigmoidal model.
DAT6 <- pcrbatch(reps, fluo = 2:17, model = l5)
GROUP6 <- c("g1s1", "g1s1", "g1s2", "g1s2",
"g2s1", "g2s1", "g2s2", "g2s2",
"g1c1", "g1c1", "g1c2", "g1c2",
"g2c1", "g2c1", "g2c2", "g2c2")
ratiobatch(DAT6, GROUP6, which.eff = "sig")
## Same as above, but using a mechanistic model (mak3).
## BEWARE: type.eff must be "individual"!
DAT7 <- pcrbatch(reps, fluo = 2:17, model = l5,
methods = c("sigfit", "mak3"))
GROUP7 <- c("g1s1", "g1s1", "g1s2", "g1s2",
"g2s1", "g2s1", "g2s2", "g2s2",
"g1c1", "g1c1", "g1c2", "g1c2",
"g2c1", "g2c1", "g2c2", "g2c2")
ratiobatch(DAT7, GROUP7, which.eff = "mak",
type.eff = "individual")
## Using external efficiencies from a
## calibration curve. Can be supplied by the
## user from external calibration (or likewise),
## but in this example acquired by function 'calib'.
ml1 <- modlist(reps, fluo = 2:25, model = l5)
DIL <- rep(10^(5:0), each = 4)
EFF <- calib(refcurve = ml1, dil = DIL)$eff
DAT8 <- pcrbatch(ml1)
GROUP8 <- c(rep("g1s1", 4), rep("g1s2", 4),
rep("g1s3", 4), rep("g1s4", 4),
rep("g1s5", 4), rep("g1c1", 4))
ratiobatch(DAT8, GROUP8, which.eff = EFF)
# }
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