qpcr_analyser: qPCR Analyser
In dpcR: Digital PCR Analysis

Description Arguments Details Value Author(s) References See Also Examples

Calculate statistics based on fluorescence. The function can be used to analyze amplification curve data from quantitative real-time PCR experiments. The analysis includes the fitting of the amplification curve by a non-linear function and the calculation of a quantification point (often referred to as Cp (crossing-point), Cq or Ct) based on a user defined method. The function can be used to analyze data from chamber based dPCR machines.

`input`	a dataframe containing the qPCR data or a result of function `modlist` or an object of the class `adpcr`.
`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. Use fluo = 2 to chose the second column for example.
`model`	is the model to be used for the analysis for all runs. Defaults to 'l5' (see `pcrfit`).
`norm`	logical. Indicates if the raw data should be normalized within [0, 1] before model fitting.
`iter_tr`	`iter_tr` number of iteration to fit the curve.
`type`	is the method for the crossing point/threshold cycle estimation and efficiency estimation (efficiency). Defaults to 'Cy0' (`Cy0`).
`takeoff`	logical; if `TRUE` calculates the first significant cycle of the exponential region (takeoff point). See `takeoff` for details.

The qpcRanalyzer is a functions to automatize the analysis of amplification curves from conventional quantitative real-time PCR (qPCR) experiments and is adapted for the needs in dPCR. This function calls instances of the qpcR package to calculate the quantification points (cpD1, cpD2, Cy0 (default), TOP (optional)), the amplification efficiency, fluorescence at the quantification point (Cq), the absolute change of fluorescence and the take-off point (TOP). Most of the central functionality of the qpcR package is accessible. The user can assign concentrations to the samples. One column contains binary converted (pos (1) and neg (0)) results for the amplification reaction based on a user defined criteria (Cq-range, fluorescence cut-off, ...). qpcr_analyser tries to detect cases where an amplification did not take place of was impossible to analyze. By default qpcr_analyser analyses uses the Cy0 as described in Guescini et al. (2008) for estimation of the quantification point since method is considered to be better suited for many probe systems. By default a 5-parameter model is used to fit the amplification curves. As such qpcr_analyser is a function, which serves for preliminary data inspection (see Example section) and as input for other R functions from the dpcR package (e.g., plot_panel).

A matrix where each column represents crossing point, efficiency, the raw fluorescence value at the point defined by type and difference between minimum and maximum of observed fluorescence. If takeoff parameter is TRUE, additional two column represents start and the end of the fluorescence growth.

Stefan Roediger, Andrej-Nikolai Spiess, Michal Burdukiewicz.

Ritz C, Spiess An-N, qpcR: an R package for sigmoidal model selection in quantitative real-time polymerase chain reaction analysis. Bioinformatics 24 (13), 2008.

Andrej-Nikolai Spiess (2013). qpcR: Modelling and analysis of real-time PCR data.
https://CRAN.R-project.org/package=qpcR

modlist.

# Take data of guescini1 data set from the qpcR R package.
library(qpcR)
# Use the first column containing the cycles and the second column for sample F1.1.
data(guescini1)
qpcr_analyser(guescini1, cyc = 1, fluo = 2)

# Use similar setting as before but set takeoff to true for an estimation of
# the first significant cycle of the exponential region.
qpcr_analyser(guescini1, cyc = 1, fluo = 2, takeoff = TRUE)

# Use similar setting as before but use qpcr_analyser in a loop to calculate the results for the
# first four columns containing the fluorescence in guescini1
print(qpcr_analyser(guescini1, cyc = 1, fluo = 2:5, takeoff = TRUE))

# Run qpcr_analyser on the list of models (finer control on fitting model process)
models <- modlist(guescini1)
qpcr_analyser(models)