Limit Cy0 values

Calculates the Cq values of a qPCR experiment within a defined range of cycles. The function can be used to extract Cq values of a chamber based qPCR for conversion into a dPCR experiment. All Cq values are obtained by Second Derivative Maximum or by Cy0 method (Guescini et al. (2008)).

limit_cq(data, cyc = 1, fluo = NULL, Cq_range = c(1, max(data[cyc])),
  model = l5, SDM = TRUE, pb = FALSE)

Arguments

data

a dataframe containing the qPCR data.

cyc

the column containing the cycle data. Defaults to first column.

fluo

the column(s) (runs) to be analyzed. If NULL, all runs will be considered (equivalent of (1L:ncol(data))[-cyc]).

Cq_range

is a user defined range of cycles to be used for the determination of the Cq values.

model

is the model to be used for the analysis for all runs. Defaults to 'l5' (see pcrfit).

SDM

if TRUE, Cq is approximated by the second derivative method. If FALSE, Cy0 method is used instead.

pb

if TRUE, progress bar is shown.

Value

A data frame with two columns and number of rows equal to the number of runs analyzed. The column Cy0 contains calculated Cy0 values. The column in.range contains adequate logical constant if given Cy0 value is in user-defined Cq_range.

Details

The Cq_range for this function an be defined be the user. The default is to take all amplification curves into consideration. However, under certain circumstances it is recommended to define a range. For example if amplifications are positive in early cycle numbers (less than 10).

Approximated second derivative is influenced both by how often interpolation takes place in each data interval and by the smoothing method used. The user is encouraged to seek optimal parameters for his data himself. See inder for details.

The calculation of the Cy0 value (equivalent of Cq) is based on a five-parameter function. From experience this functions leads to good fitting and avoids overfitting of critical data sets. Regardless, the user is recommended to test for the optimal fitting function himself (see mselect for details).

References

Guescini M, Sisti D, Rocchi MB, Stocchi L & Stocchi V (2008) A new real-time PCR method to overcome significant quantitative inaccuracy due to slight amplification inhibition. BMC Bioinformatics, 9: 326.

Ruijter JM, Pfaffl MW, Zhao S, et al. (2013) Evaluation of qPCR curve analysis methods for reliable biomarker discovery: bias, resolution, precision, and implications. Methods, San Diego Calif 59:32--46.

See also

SDM method: inder, summary.der.

Cy0 method: mselect, efficiency.

Examples

library(qpcR)
#> Loading required package: MASS
#> Loading required package: minpack.lm
#> Loading required package: rgl
#> Loading required package: robustbase
#> 
#> Attaching package: ‘robustbase’
#> The following object is masked from ‘package:stats’:
#> 
#>     sigma
#> Loading required package: Matrix
#> 
#> Attaching package: ‘qpcR’
#> The following object is masked from ‘package:dpcR’:
#> 
#>     l5
test <- cbind(reps[1L:45, ], reps2[1L:45, 2L:ncol(reps2)], reps3[1L:45,
              2L:ncol(reps3)])

# results.dPCR contains a column with the Cy0 values and a column with 
# converted values.
Cq.range <- c(20, 30)
ranged <- limit_cq(data = test, cyc = 1, fluo = NULL,
                           Cq_range = Cq.range, model = l5)
# Same as above, but without Cq.range
no_range <- limit_cq(data = test, cyc = 1, fluo = NULL, model = l5)

# Same as above, but only three columns
no_range234 <- limit_cq(data = test, cyc = 1, fluo = c(2:4), model = l5)