qpcR (version 1.3-7.1)

ratiobatch: Calculation of ratios in a batch format for multiple genes/samples

Description

For multiple qPCR data from type 'pcrbatch', this function calculates ratios between samples, using normalization against one or more reference gene(s), if supplied. Multiple reference genes can be averaged according to Vandesompele et al. (2002). The input may be single qPCR data or (more likely) data containing replicates. This is essentially a version of ratiocalc that can handle multiple reference genes and genes-of-interest with multiple (replicated) samples as found in large-scale qPCR runs such as 96- or 384-Well plates. A boxplot representation for all Monte-Carlo simulations, permutations and error propagations including 95% confidence intervals is calculated for each ratio calculation.

Usage

ratiobatch(data, group = NULL, plot = TRUE, 
           combs = c("same", "across", "all"), 
           type.eff = "mean.single", which.cp = "cpD2", 
           which.eff = "sli", refmean = FALSE,
           dataout = NULL, verbose = TRUE, ...)

Arguments

data

multiple qPCR data generated by pcrbatch.

group

a character vector defining the replicates (if any) of control/treatment samples and reference genes/genes-of-interest. See 'Details'

plot

logical. If TRUE, plots are displayed for the diagnostics and analysis.

combs

type of combinations between different samples (i.e. r1s1:g1s2). See 'Details'.

type.eff

type of efficiency averaging used. Same as in ratiocalc.

which.eff

efficiency obtained from which method. Same as in ratiocalc.

which.cp

threshold cycle obtained from which method. Same as in ratiocalc.

dataout

an optional file path where to store the result dataframe.

refmean

logical. If TRUE, multiple reference are averaged before calculating the ratios. See 'Details'.

verbose

logical. If TRUE, the steps of analysis are shown in the console window

...

other parameters to be passed to ratiocalc.

Value

• A list with the following components:
• resList
  a list with the results from the combinations as list items.
• resDat
  a dataframe with the results in colums.
• Both resList and resDat have as names the combinations used for the ratio calculation. If plot = TRUE, a boxplot matrix from the Monte-Carlo simulations, permutations and error propagations is given including 95% confidence intervals as coloured horizontal lines.

Details

Similar to ratiocalc, the replicates of the 'pcrbatch' data columns are to be defined as a character vector with the following abbreviations: "g1s1": gene-of-interest #1 in treatment sample #1 "g1c1": gene-of-interest #1 in control sample #1 "r1s1": reference gene #1 in treatment sample #1 "r1c1": reference gene #1 in control sample #1 There is no distinction between the different technical replicates so that three different runs of gene-of-interest #1 in treatment sample #2 are defined as c("g1s2", "g1s2", "g1s2"). Example: 1 control sample with 2 genes-of-interest (2 technical replicates), 2 treatment samples with 2 genes-of-interest (2 technical replicates): "g1c1", "g1c1", "g2c1", "g2c1", "g1s1", "g1s1", "g1s2", "g1s2", "g2s1", "g2s1", "g2s2", "g2s2" The ratios are calculated for all pairwise 'rc:gc' and 'rs:gs' combinations according to: For all control samples $i = 1 \ldots I$ and treatment samples $j = 1 \ldots J$, reference genes $k = 1 \ldots K$ and genes-of-interest $l = 1 \ldots L$, calculate Without reference genes: $$\frac{E(g_lc_i)^{cp(g_lc_i)}}{E(g_ls_j)^{cp(g_ls_j)}}$$ With reference genes: $$\frac{E(g_lc_i)^{cp(g_lc_i)}}{E(g_ls_j)^{cp(g_ls_j)}}/\frac{E(r_kc_i)^{cp(r_kc_i)}}{E(r_ks_j)^{cp(r_ks_j)}}$$ For the mechanistic models makX/cm3 the following is calculated: Without reference genes: $$\frac{D0(g_ls_j)}{D0(g_lc_i)}$$ With reference genes: $$\frac{D0(g_ls_j)}{D0(g_lc_i)}/\frac{D0(r_ks_j)}{D0(r_kc_i)}$$ Efficiencies can be taken from the individual curves or averaged from the replicates as described in the documentation to ratiocalc. It is also possible to give external efficiencies (i.e. acquired by some calibration curve) to the function. See 'Examples'. The different combinations of type.eff, which.eff and which.cp can yield very different results in ratio calculation. We observed a relatively stable setup which minimizes the overall variance using the combination type.eff = "mean.single" # averaging efficiency across replicates which.eff = "sli" # taking efficiency from the sliding window method which.cp = "sig" # using the second derivative maximum of a sigmoidal fit This is also the default setup in the function. The lowest variance can be obtained for the threshold cycles if the asymmetric 5-parameter l5 model is used in the pcrbatch function. There are three different combination setups possible when calculating the pairwise ratios: combs = "same": reference genes, genes-of-interest, control and treatment samples are the same, i.e. $i = k, m = o, j = n, l = p$. combs = "across": control and treatment samples are the same, while the genes are combinated, i.e. $i \neq k, m \neq o, j = n, l = p,$. combs = "all": reference genes, genes-of-interest, control and treatment samples are all combinated, i.e. $i \neq k, m \neq o, j \neq n, l \neq p$. The last setting rarely makes sense and is very time-intensive. combs = "same" is the most common setting, but combs = "across" also makes sense if different genes-of-interest and reference gene combinations should be calculated for the same samples. From version 1.3-6, ratiobatch has the option of averaging several reference genes, as described in Vandesompele et al. (2002). Threshold cycles and efficiency values for any $i$ reference genes with $j$ replicates are averaged before calculating the ratios using the averaged value $\mu_r$ for all reference genes in a control/treatment sample. The overall error $\sigma_r$ is obtained by error propagation. The whole procedure is accomplished by function refmean, which can be used as a stand-alone function, but is most conveniently used inside ratiobatch setting refmean = TRUE. See in 'Examples'. For details about reference gene averaging by refmean, see there. If none or only one per sample is found, the data is analyzed without using reference gene averaging/error propagation.

References

Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Vandesompele J, De Preter K, Pattyn F, Poppe B, Van Roy N, De Paepe A, Speleman F. Genome Biol (2002), 3: research0034-research0034.11.

Examples

## 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") 
RES1 <- 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") 
RES2 <- 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") 
RES3 <- 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") 
RES4 <- ratiobatch(DAT4, GROUP4, refmean = TRUE)

## Same as above, but use same efficiency E = 2.         
RES5 <- 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")            
RES6 <- 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")
RES7 <- 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, model = l5)
DIL <- rep(10^(6:0), each = 4) 
EFF <- calib(refcurve = ml1, dil = DIL)$eff   
pba <- pcrbatch(ml1)
GROUP8 <- c(rep("g1s1", 4), rep("g1s2", 4),
            rep("g1s3", 4), rep("g1s4", 4), 
            rep("g1s5", 4), rep("g1s6", 4), 
            rep("g1c1", 4)) 
RES8 <- ratiobatch(pba, GROUP8, which.eff = EFF)