Type:	Package
Title:	Quantitative Polymerase Chain Reaction (qPCR) Data Mining and Machine Learning Toolkit as Described in Burdukiewicz (2022) <doi:10.21105/Joss.04407>
Version:	1.2-0
Date:	2025-06-11
Description:	Extracts features from amplification curve data of quantitative Polymerase Chain Reactions (qPCR) according to Pabinger et al. 2014 <doi:10.1016/j.bdq.2014.08.002> for machine learning purposes. Helper functions prepare the amplification curve data for processing as functional data (e.g., Hausdorff distance) or enable the plotting of amplification curve classes (negative, ambiguous, positive). The hookreg() and hookregNL() functions of Burdukiewicz et al. (2018) <doi:10.1016/j.bdq.2018.08.001> can be used to predict amplification curves with an hook effect-like curvature. The pcrfit_single() function can be used to extract features from an amplification curve.
License:	MIT + file LICENSE
LazyLoad:	yes
LazyData:	yes
URL:	https://CRAN.R-project.org/package=PCRedux
BugReports:	https://github.com/PCRuniversum/PCRedux/issues
Depends:	R (≥ 3.5.0)
Imports:	changepoint, chipPCR, ecp, fda.usc, MBmca, pbapply, pracma, qpcR, robustbase, segmented, shiny, stats, utils, zoo
Suggests:	DT, future, knitr, listenv, RDML, readxl, rmarkdown, shinycssloaders, spelling, testthat, xtable
NeedsCompilation:	no
VignetteBuilder:	knitr
Encoding:	UTF-8
Language:	en-US
RoxygenNote:	7.1.2
Packaged:	2025-06-11 07:35:38 UTC; User
Repository:	CRAN
Date/Publication:	2025-06-13 05:00:02 UTC
Author:	Stefan Roediger [aut], Michal Burdukiewicz [aut], Andrej-Nikolai Spiess [cre, aut], Konstantin A. Blagodatskikh [aut], Dominik Rafacz [ctb]
Maintainer:	Andrej-Nikolai Spiess <draspiess@gmail.com>

PCRedux - quantitative PCR Data Mining and Machine Learning Toolkit

Description

PCRedux package is a toolbox for the analysis of sigmoid curve (qPCR) data.

Usage

l4

Format

An object of class list of length 11.

Machine learning

In machine learning and statistics, the classification should be used to identify a new unknown observation. This observation is assigned to a number of categories. One basis is training data sets containing observations with known classes. Using the example of sigmoid amplification curves, this could be an assignment to the class "negative","ambiguous" or "positive". Basically, a number of descriptors (e. g., characteristics of curvature) are required to be able to assign classes. This package contains functions for extracting characteristics. In addition, the package contains data sets of classified amplification curves.

Author(s)

Stefan Roediger, Michal Burdukiewcz, Andrej-Nikolai Spiess, Konstantin A. Blagodatskikh

Examples

# Use the mblrr function to analyse amplification curves
library(qpcR)
mblrr(x=boggy[, 1], y=boggy[, 2])

The datasets implemented in PCRedux

Description

A compilation of datasets for method evaluation/comparison.

Usage

data_sample
RAS002
RAS002_decisions
kbqPCR
decision_res_kbqPCR

Details

data_sample
Setup: Amplification curve data were analyzed with the encu() and the decision_modus() functions.
Details:
Data sets: batsch1, boggy, C126EG595, competimer, dil4reps94, guescini1, karlen1, lievens1, reps384, rutledge, testdat, vermeulen1, VIMCFX96_60, stepone_std.rdml, RAS002.rdml, RAS003.rdml, HCU32_aggR.csv, lc96_bACTXY.rdml.

RAS002
Setup: Amplification curve data of the RAS002.rdml data set.
Details:
Data sets: RAS002.rdml.

RAS002_decisions
Setup: Classes of the amplification curves from the RAS002.rdml data set.
Details:
Data sets: decision_res_RAS002.csv.

Author(s)

Stefan Roediger

References

Roediger, S., Burdukiewicz, M., Spiess, A.-N. & Blagodatskikh, K. Enabling reproducible real-time quantitative PCR research: the RDML package. Bioinformatics (2017). doi:10.1093/bioinformatics/btx528
Roediger, S., Burdukiewicz, M. & Schierack, P. chipPCR: an R package to pre-process raw data of amplification curves. Bioinformatics 31, 2900–2902 (2015)
Ritz, C. & Spiess, A.-N. qpcR: an R package for sigmoidal model selection in quantitative real-time polymerase chain reaction analysis. Bioinformatics 24, 1549–1551 (2008).

Examples

## 'data_sample' dataset.
head(data_sample)

## 'RAS002.rdml' dataset as rda file.
head(RAS002)
head(RAS002_decisions)

armor: fetch errors gently

Description

armor is a helper function that catches errors and creates an output that can be used for further processing.

Usage

armor(f, n = 1)

Arguments

Value

gives a numeric value (S3 class) as output for errors

Author(s)

Andrej Nikolai Spiess, Stefan Roediger

Examples

# Fetch the error from the diffQ function
# In the following the approximate derivative of the amplification curve data
# x <- RAS002[, 1] and y <- RAS002[, 2] is calculated by diffQ(). 
# This will not give an error.
x <- RAS002[, 1]
y <- RAS002[, 2]
armor_diffQ_passes <- armor(diffQ(cbind(x, y), verbose = TRUE)$xy)
armor_diffQ_passes
#
# In the following the approximate derivative of the sequences x <- 1:40
# and y <- 1:40 is calculated by diffQ(). However, this will fail.
# This will give the "internal" error
# > 
# Error in list.res[[i]][[8]] : subscript out of bounds
# that is resolved to 0.
x <- 1:40
y <- 1:40
armor_diffQ_fails <- armor(diffQ(cbind(x, y), verbose = TRUE)$xy)
armor_diffQ_fails

A function to test for autocorrelation of amplification curve data from a quantitative PCR experiment

Description

autocorrelation_test is a function for an autocorrelation analysis from a quantitative PCR experiment. The result of the function is a correlation coefficient.

Usage

autocorrelation_test(y, n = 8, sig.level = 0.01)

Arguments

Value

gives a numeric value (S3 class) as output for an autocorrelation

Author(s)

Stefan Roediger, Michal Burdukiewcz

Examples

library(qpcR)
default.par <- par(no.readonly = TRUE)
# Test for autocorrelation in amplification curve data
# Test for autocorrelation in the testdat data set
res_ac <- sapply(2:ncol(testdat), function(i) {
                    autocorrelation_test(testdat[, i])
                }
         )

# Plot curve data as overview
# Define the colors for the amplification curves
colors <- rainbow(ncol(testdat)-1, alpha=0.3)
# Names of samplesfile:///home/tux/R_malade
samples <- colnames(testdat)[-1]
layout(matrix(c(1,2,1,3), 2, 2, byrow = TRUE))
matplot(testdat[, 1], testdat[, -1], xlab="Cycle", ylab="RFU",
        main="testdat data set", type="l", lty=1, col=colors, lwd=2)
legend("topleft", samples, pch=19, col=colors, ncol=2, bty="n")

# Curves rated by a human after analysis of the overview. 1 = positive,
# 0 = negative
human_rating <- c(1,1,0,0,1,1,0,0,
                  1,1,0,0,1,1,0,0,
                  1,1,0,0,1,1,0,0)

# Convert the n.s. (not significant) to 0 and others to 1.
# Combine the results of the aromatic autocorrelation_test as variable "ac",
# the human rated values as variable "hr" in a new data frame (res_ac_hr).
res_ac_hr <- as.matrix(data.frame(ac=ifelse(res_ac=="n.s.", 0, 1),
                                  hr=human_rating))
res_performeR <- performeR(res_ac_hr[, "ac"], res_ac_hr[, "hr"])

# Add ratings by human and autocorrelation_test to the plot
par(las=2)
plot(1:nrow(res_ac_hr), res_ac_hr[, "hr"], xlab="Sample", ylab="Decisions",
     xaxt="n", yaxt="n", pch=19)
axis(2, at=c(0,1), labels=c("negative", "positive"), las=2)
axis(1, at=1:nrow(res_ac_hr), labels=colnames(testdat)[-1], las=2)
points(1:nrow(res_ac_hr), res_ac_hr[, "ac"], pch=1, cex=2, col="red")
legend("topleft", c("Human", "autocorrelation_test"), pch=c(19,1),
       bty="n", col=c("black","red"))

barplot(as.matrix(res_performeR[, c(1:10,12)]), yaxt="n",
        ylab="", main="Performance of autocorrelation_test")
axis(2, at=c(0,1), labels=c("0", "1"), las=2)
par(default.par)

A function to get a decision (modus) from a vector of classes

Description

decision_modus is a function that can be used to find the most frequent (modus) decision. The classes can be defined by the user (e.g., a", "n", "y" -> "ambiguous", "negative", "positive"). This function is useful if large collections of varying decision (e.g., "a", "a", "a", "n", "n") need to be condensed to a single decision (3 x "a", 2 x "n" -> "a").

Usage

decision_modus(data, variables = c("a", "n", "y"), max_freq = TRUE)

Arguments

Value

gives a factor (S3 class, type of integer) as output for a decision

Author(s)

Stefan Roediger, Michal Burdukiewcz

Examples

# First example
# Enter a string of arbritary of "a","a","y","n"
# Result:
# [1] a
# Levels: a b n y

decision_modus(c("a","a","y","n","b"))

# Second example
# Analyze data from the decision_res_testdat.csv data file
filename <- system.file("decision_res_testdat.csv", package = "PCRedux")
my_data <- read.csv(filename)
head(my_data)

dec <- unlist(lapply(1L:nrow(my_data), function(i) {
       decision_modus(my_data[i, 2:4])
}))

names(dec) <- my_data[, 1]
dec

A function to calculate the slope and intercept of an amplification curve data from a quantitative PCR experiment.

Description

earlyreg is a function to calculate the slope and intercept of an amplification curve data from a quantitative PCR experiment. The number of cycles to be analyzed is defined by the user (default 6 cycles). The output contains the Maximal Information Coefficient (MIC), which can be interpreted as a correlation measure with a range of [0,1]. A value of 0 mean statistically independent data and 1 approaches in "probability for noiseless functional relationships" (see original study by Reshef, D. N. et al. Detecting novel associations in large data sets. Science, 334, 1518-1524 (2011)).

Usage

earlyreg(x, y, range = 5, normalize = FALSE)

Arguments

Value

gives a numeric vector (S3 class, type of double) as output for local regression

Author(s)

Stefan Roediger, Michal Burdukiewcz

Examples

# Calculate slope and intercept on noise (negative) amplification curve data
# for the cycles 2 to 7 for the C316.amp data set
library(chipPCR)
data(C316.amp)

# Plot the data
plot(C316.amp[, 2], y=C316.amp[, 3], xlab="Cycle", ylab="RFU",
     main="C316.amp data set", lty=1, type="l")
res <- earlyreg(x=C316.amp[, 2], y=C316.amp[, 3], range=5)
res

A function to calculate numerous features from amplification curve data from a quantitative PCR experiment.

Description

encu (ENcode CUrves) is a function to calculate numerous features of a large amplification curve data set. The pcrfit_single is performing the analysis for a single process.

Usage

encu(data, detection_chemistry = NA, device = NA)

Arguments

Value

gives a data.frame vector (S3 class, type of list) as output for features

The output of the encu function is identical to the pcrfit_single function.

Author(s)

Stefan Roediger, Michal Burdukiewcz

Examples

library(qpcR)

# Calculate curve features of an amplification curve data. Note that not all
# available CPU cores are used. If need set "all" to use all available cores.
# In this example the testdat data set from the qpcR package is used.
# The samples F1.1 and F1.2 are positive amplification curves. The samples
# F1.3 and F1.4 are negative.
res_encu <- encu(testdat[, 1:3])
res_encu

A function to calculate to head to tail ratio of amplification curve data from a quantitative PCR experiment

Description

head2tailratio is a function to calculate the ratio of the head and the tail of a quantitative PCR amplification curve. In this test, only the head (first six cycles) and the tail (last six cycles) form the region of interest (ROI).

Usage

head2tailratio(y, normalize = FALSE, slope_normalizer = FALSE, verbose = FALSE)

Arguments

Value

gives a numeric (S3 class, type of double) as output for the head to tail ratio

Author(s)

Stefan Roediger, Michal Burdukiewcz

Examples

library(qpcR)

# calculate head to tail ratio on amplification curve data
res_head2tailratio <- sapply(2:ncol(competimer), function(i) {
   head2tailratio(y=competimer[, i], normalize=TRUE, slope_normalizer=TRUE)
})

res_head2tailratio_cluster <- kmeans(res_head2tailratio, 3)$cluster

matplot(competimer[, 1], competimer[, -1], xlab="Cycle", ylab="RFU",
        main="competimer data set", type="l", lty=1, col=res_head2tailratio_cluster, lwd=2)

A function to calculate the slope and intercept of an amplification curve data from a quantitative PCR experiment at the end of the data stream.

Description

hookreg is a function to calculate the slope and intercept of an amplification curve data from a quantitative PCR experiment. The idea is that a strong negative slope at the end of an amplification curve is indicative for a hook effect (see Barratt and Mackay 2002).

Usage

hookreg(
  x,
  y,
  normalize = TRUE,
  sig.level = 0.0025,
  CI.level = 0.9975,
  robust = FALSE
)

Arguments

Value

gives a numeric (S3 class, type of double) as output for the detection of a hook

Author(s)

Stefan Roediger, Michal Burdukiewcz

References

K. Barratt, J.F. Mackay, Improving Real-Time PCR Genotyping Assays by Asymmetric Amplification, J. Clin. Microbiol. 40 (2002) 1571–1572. doi:10.1128/JCM.40.4.1571-1572.2002.

Examples

library(qpcR)

default.par <- par(no.readonly = TRUE)
# Calculate slope and intercept on noise (negative) amplification curve data
# for the last eight cycles.
res_hook <- data.frame(sample=colnames(boggy)[-1], 
                       t(sapply(2:ncol(boggy), function(i) {
                       hookreg(x=boggy[, 1], y=boggy[, i])})))
res_hook

data_colors <- rainbow(ncol(boggy[, -1]), alpha=0.5)
cl <- kmeans(na.omit(res_hook[, 2:3]), 2)$cluster

par(mfrow=c(1,2))
matplot(x=boggy[, 1], y=boggy[, -1], xlab="Cycle", ylab="RFU",
 main="boggy Data Set", type="l", lty=1, lwd=2, col=data_colors)
 legend("topleft", as.character(res_hook$sample), pch=19,
         col=data_colors, bty="n")

plot(res_hook$intercept, res_hook$slope, pch=19, cex=2, col=data_colors,
 xlab="intercept", ylab="Slope",
 main="Clusters of Amplification Curves with an Hook Effect-like Curvature\nboggy Data Set")
 points(res_hook$intercept, res_hook$slope, col=cl, pch=cl, cex=cl)
 legend("topright", c("Strong Hook effect", " Weak Hook effect"), pch=c(1,2), col=c(1,2), bty="n")
 text(res_hook$intercept, res_hook$slope, res_hook$sample)

par(default.par)

hookregNL - A function to calculate the slope of amplification curves in the tail region

Description

hookregNL is a function to calculate the slope and intercept of an amplification curve from a quantitative PCR experiment. The idea is that a strong negative slope at the end of an amplification curve is indicative for a hook effect (see Barratt and Mackay 2002). In contrast to hookreg fits this function a sex-parameter model to the amplification curve and extracts the coefficient, which describes the slope.

Usage

hookregNL(x, y, plot = FALSE, level = 0.995, simple = TRUE, manualtrim = 5)

Arguments

Value

gives a numeric (S3 class, type of double) as output for the detection of a hook

Author(s)

Andrej-Nikolai Spiess, Stefan Roediger, Michal Burdukiewcz

References

K. Barratt, J.F. Mackay, Improving Real-Time PCR Genotyping Assays by Asymmetric Amplification, J. Clin. Microbiol. 40 (2002) 1571–1572. doi:10.1128/JCM.40.4.1571-1572.2002.

Examples

library(qpcR)

# Analyze data from the boggy data set for potential hook effect like
# curvature
# has hook
res <- hookregNL(boggy[, 1], boggy[, 2])
res

# has no hook
res <- hookregNL(boggy[, 1], boggy[, 12])
res

Human Rater 2.0

Description

Launches graphical user interface for the manual annotation of large amplification curve data sets, similarly to the humanrater function.

Usage

humanrater2()

Value

No return value, called for side effects

Warning

Any ad-blocking software may cause malfunctions.

A function to perform a Local Robust Regression in Ranges defined by Qunantile-filtering

Description

mblrr is a function to perform the Median based Local Robust Regression (mblrr) from a quantitative PCR experiment. In detail, this function attempts to break the amplification curve in two parts (head (~background) and tail (~plateau)). Subsequent, a robust linear regression analysis (lmrob) is preformed individually on both parts. The rational behind this analysis is that the slope and intercept of an amplification curve differ in the background and plateau region.

Usage

mblrr(x, y, sig.level = 0.01, normalize = FALSE)

Arguments

Details

mblrr_intercept_bg is the intercept of the head region, mblrr_slope_bg is the slope of the head region, mblrr_cor_bg is the coefficient of correlation of the head region, mblrr_intercept_pt is the intercept of the tail region, mblrr_intercept_pt is the slope of the tail region, mblrr_cor_pt is the coefficient of correlation of the tail region

Value

gives a numeric (S3 class, type of double) as output for the regressed regions

Author(s)

Stefan Roediger, Michal Burdukiewcz

Examples

library(qpcR)

# Perform an mblrr analysis on noise (negative) amplification data of qPCR data
# with 35 cycles.
mblrr(x=boggy[, 1], y=boggy[, 2], normalize=TRUE)

pcrfit_single - A function to extract features from an amplification curve

Description

The pcrfit_single is responsible for the extraction of features from amplification curve data. The function can be used for custom functions for a paralleled analysis of amplification curve data. An example is given in the vignette.

Usage

pcrfit_single(x)

Arguments

Details

Details can be found in the vignette.

Value

Output Description

gives a data.frame (S3 class, type of list) as output for the curve features

Author(s)

Stefan Roediger, Michal Burdukiewcz

References

M. Febrero-Bande, M.O. de la Fuente, others, Statistical computing in functional data analysis: The R package fda.usc, Journal of Statistical Software. 51 (2012) 1–28. http://www.jstatsoft.org/v51/i04/

A.-N. Spiess, C. Deutschmann, M. Burdukiewicz, R. Himmelreich, K. Klat, P. Schierack, S. Roediger, Impact of Smoothing on Parameter Estimation in Quantitative DNA Amplification Experiments, Clinical Chemistry. 61 (2015) 379–388. doi:10.1373/clinchem.2014.230656.

S. Roediger, A. Boehm, I. Schimke, Surface Melting Curve Analysis with R, The R Journal. 5 (2013) 37–53. http://journal.r-project.org/archive/2013-2/roediger-bohm-schimke.pdf.

S. Roediger, M. Burdukiewicz, K.A. Blagodatskikh, P. Schierack, R as an Environment for the Reproducible Analysis of DNA Amplification Experiments, The R Journal. 7 (2015) 127–150. http://journal.r-project.org/archive/2015-1/RJ-2015-1.pdf.

S. Pabinger, S. Roediger, A. Kriegner, K. Vierlinger, A. Weinhauusel, A survey of tools for the analysis of quantitative PCR (qPCR) data, Biomolecular Detection and Quantification. 1 (2014) 23–33. doi:10.1016/j.bdq.2014.08.002.

S. Roediger, M. Burdukiewicz, P. Schierack, chipPCR: an R package to pre-process raw data of amplification curves, Bioinformatics. 31 (2015) 2900–2902. doi:10.1093/bioinformatics/btv205.

Examples

# Load the chipPCR package and analyze from the C126EG685 the first qPCR run
# "A01" (column 2).
library(chipPCR)
res <- pcrfit_single(C126EG685[, 2])

Performance analysis for binary classification

Description

This function performs an analysis sensitivity and specificity to asses the performance of a binary classification test. For further reading the studies by Brenner and Gefeller 1997, James 2013 by Kuhn 2008 are a good starting point.

Usage

performeR(sample, reference)

Arguments

Details

TP, true positive; FP, false positive; TN, true negative; FN, false negative

Sensitivity - TPR, true positive rate TPR = TP / (TP + FN)

Specificity - SPC, true negative rate SPC = TN / (TN + FP)

Precision - PPV, positive predictive value PPV = TP / (TP + FP)

Negative predictive value - NPV NPV = TN / (TN + FN)

Fall-out, FPR, false positive rate FPR = FP / (FP + TN) = 1 - SPC

False negative rate - FNR FNR = FN / (TN + FN) = 1 - TPR

False discovery rate - FDR FDR = FP / (TP + FP) = 1 - PPV

Accuracy - ACC ACC = (TP + TN) / (TP + FP + FN + TN)

F1 score F1 = 2TP / (2TP + FP + FN)

Likelihood ratio positive - LRp LRp = TPR/(1-SPC)

Matthews correlation coefficient (MCC) MCC = (TP*TN - FP*FN) / sqrt(TN + FP) * sqrt(TN+FN) )

Cohen's kappa (binary classification) kappa=(p0-pc)/(1-p0)

r (reference) is the trusted label and s (sample) is the predicted value

n = a + b + c + d

pc=((a+b)/n)((a+c)/n)+((c+d)/n)((b+d)/n)

po=(a+d)/n

Value

gives a data.frame (S3 class, type of list) as output for the performance

Author(s)

Stefan Roediger, Michal Burdukiewcz

References

H. Brenner, O. Gefeller, others, Variation of sensitivity, specificity, likelihood ratios and predictive values with disease prevalence, Statistics in Medicine. 16 (1997) 981–991.

M. Kuhn, Building Predictive Models in R Using the caret Package, Journal of Statistical Software. 28 (2008). doi:10.18637/jss.v028.i05.

G. James, D. Witten, T. Hastie, R. Tibshirani, An Introduction to Statistical Learning, Springer New York, New York, NY, (2013). doi:10.1007/978-1-4614-7138-7.

Examples

# Produce some arbitrary binary decisions data
# test_data is the new test or method that should be analyzed
# reference_data is the reference data set that should be analyzed
test_data <- c(0,0,0,0,0,0,1,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1)
reference_data <- c(0,0,0,0,1,1,1,1,0,1,0,1,0,1,0,1,0,1,0,1,1,1,1,1)

# Plot the data of the decisions
plot(1:length(test_data), test_data, xlab="Sample", ylab="Decisions",
     yaxt="n", pch=19)
axis(2, at=c(0,1), labels=c("negative", "positive"), las=2)
points(1:length(reference_data), reference_data, pch=1, cex=2, col="blue")
legend("topleft", c("Sample", "Reference"), pch=c(19,1),
        cex=c(1.5,1.5), bty="n", col=c("black","blue"))

# Do the statistical analysis with the performeR function
performeR(sample=test_data, reference=reference_data)

A helper function to convert amplification curve data to the fdata format.

Description

qPCR2fdata is a helper function to convert qPCR data to the functional fdata class as proposed by Febrero-Bande & de la Fuente (2012). This function prepares the data for further analysis with the fda.usc package, which includes utilities for functional data analysis (e.g., Hausdorff distance).

Usage

qPCR2fdata(data, preprocess = FALSE)

Arguments

Value

gives an fdata object (S3 class, type of list) as output for a converted amplification curve.

Author(s)

Stefan Roediger, Michal Burdukiewcz

References

M. Febrero-Bande, M.O. de la Fuente, others, Statistical computing in functional data analysis: The R package fda.usc, Journal of Statistical Software. 51 (2012) 1–28. http://www.jstatsoft.org/v51/i04/

S. Roediger, M. Burdukiewicz, P. Schierack, chipPCR: an R package to pre-process raw data of amplification curves, Bioinformatics. 31 (2015) 2900–2902. doi:10.1093/bioinformatics/btv205.

Examples

library(qpcR)
library(fda.usc)

default.par <- par(no.readonly = TRUE)
# Calculate slope and intercept on noise (negative) amplification curve data
# for the last eight cycles.
# Convert the qPCR data set to the fdata format
res_fdata <- qPCR2fdata(testdat)

# Extract column names and create rainbow color to label the data
res_fdata_colnames <- colnames(testdat[-1])
data_colors <- rainbow(length(res_fdata_colnames), alpha=0.5)

# Plot the converted qPCR data
par(mfrow=c(1,2))
plot(res_fdata, xlab="cycles", ylab="RFU", main="testdat", type="l",
                   lty=1, lwd=2, col=data_colors)
legend("topleft", as.character(res_fdata_colnames), pch=19,
         col=data_colors, bty="n", ncol=2)

# Calculate the Hausdorff distance (fda.usc) package and plot the distances
# as clustered data.

res_fdata_hclust <- metric.hausdorff(res_fdata)
plot(hclust(as.dist(res_fdata_hclust)), main="Clusters of the amplification\n
   curves as calculated by the Hausdorff distance")
par(default.par)

PCRedux app

Description

A graphical user interface for computing the properties of amplification curves. Take a look at the vignette to learn more about the different ways to start the app.

Usage

run_PCRedux()

Value

null.

No return value, called for side effects

Note

Any ad-blocking software may cause malfunctions.

A function to Group Amplification Curves According to their Shape

Description

tReem is a function to group amplification curves from a quantitative PCR experiment according to their shape. Either the Pearson correlation coefficient or the Hausdorff distance is used as measure. In most cases the grouping based on the Pearson correlation coefficient is sufficient. The grouping based on the Hausdorff distance can be very slow for large data sets.

Usage

tReem(data, cor = TRUE, k = 2)

Arguments

Value

gives a data.frame (S3 class, type of list) as output for the manual analyzed data

Author(s)

Stefan Roediger, Andrej-Nikolai Spiess

Examples

# Classify amplification curve data by Hausdorff distance
library(qpcR)
tReem(testdat[, 1:5])

winklR: A function to calculate the angle based on the first and the second derivative of an amplification curve data from a quantitative PCR experiment.

Description

winklR is a function to calculate the in the trajectory of the first and the second derivatives maxima and minima of an amplification curve data from a quantitative PCR experiment. For the determination of the angle (central angle), the origin is the maximum of the first derivative. On this basis, the vectors to the minimum and maximum of the second derivative are determined. This means that systematic off-sets, such as those caused by background, are taken into account. The output contains the angle.

Usage

winklR(x, y, normalize = FALSE, preprocess = TRUE)

Arguments

Value

gives a list (S3 class, type of list) as output for the angles from an amplification curve.

Author(s)

Stefan Roediger

Examples

# Calculate the angles for amplification curve data from the RAS002 data set
data(RAS002)

# Plot the data
plot(RAS002[, 1],
  y = RAS002[, 2], xlab = "Cycle", ylab = "RFU",
  main = "RAS002 data set", lty = 1, type = "l"
)
res <- winklR(x = RAS002[, 1], y = RAS002[, 2])
res
plot(rbind(res$origin, res$p1, res$p2), col = c("black", "green", "blue"))

plot(RAS002[, 1],
  y = RAS002[, 7], xlab = "Cycle", ylab = "RFU",
  main = "RAS002 data set", lty = 1, type = "l"
)
res <- winklR(x = RAS002[, 1], y = RAS002[, 7])
res
plot(rbind(res$origin, res$p1, res$p2), col = c("black", "green", "blue"))

res_angles <- unlist(lapply(2:21, function(i) {
  winklR(RAS002[, 1], RAS002[, i])$angle
}))
cdplot(RAS002_decisions[1L:20] ~ res_angles, xlab = "angle", ylab = "decision")