Package 'chipPCR'

Description

A toolkit of functions to pre-process amplification curve data. Amplification data can be obtained from conventional PCR reactions or isothermal amplification reactions. Contains functions to normalize and baseline amplification curves, a routine to detect the start of an amplification reaction, several smoothers for amplification data, a function to distinguish positive and negative amplification reactions and a function to determine the amplification efficiency. The smoothers are based on LOWESS, moving average, cubic splines, Savitzky-Golay and others. In addition the first approximate approximate derivative maximum (FDM) and second approximate derivative maximum (SDM) can be calculated by a 5-point-stencil as quantification points from real-time amplification curves. chipPCR contains data sets of experimental nucleic acid amplification systems including the 'VideoScan' 'HCU' and a capillary convective PCR (ccPCR) system. The amplification data were generated by helicase dependent amplification (HDA) or polymerase chain reaction (PCR) under various temperature conditions. As detection system intercalating dyes (EvaGreen, SYBR Green) and hydrolysis probes (TaqMan) were used. The latest source code is available via: https://github.com/PCRuniversum/chipPCR

Details

bg.max can be used to remove missing values in amplification curve data. amptester tests if an amplification is positive. fixNA is used to impute missing values from a data column. CPP can be used to normalize curve data, to remove background, to remove outliers and further steps. Contains further functions to smooth the data by different functions including LOWESS, Moving Average, Friedman's SuperSmoother, Cubic Spline and Savitzky-Golay smoothing.

For more exhaustive description see the vignette (vignette("chipPCR")).

Author(s)

Stefan Roediger, Michal Burdukiewicz

Maintainer: Stefan Roediger <[email protected]>

References

A Highly Versatile Microscope Imaging Technology Platform for the Multiplex Real-Time Detection of Biomolecules and Autoimmune Antibodies. S. Roediger, P. Schierack, A. Boehm, J. Nitschke, I. Berger, U. Froemmel, C. Schmidt, M. Ruhland, I. Schimke, D. Roggenbuck, W. Lehmann and C. Schroeder. Advances in Biochemical Bioengineering/Biotechnology. 133:33–74, 2013.

Nucleic acid detection based on the use of microbeads: a review. S. Roediger, C. Liebsch, C. Schmidt, W. Lehmann, U. Resch-Genger, U. Schedler, P. Schierack. Microchim Acta 2014:1–18. DOI: 10.1007/s00604-014-1243-4

Roediger S, Boehm A, Schimke I. Surface Melting Curve Analysis with R. The R Journal 2013;5:37–53.

Spiess, A.-N., Deutschmann, C., Burdukiewicz, M., Himmelreich, R., Klat, K., Schierack, P., Roediger, S., 2014. Impact of Smoothing on Parameter Estimation in Quantitative DNA Amplification Experiments. Clinical Chemistry clinchem.2014.230656. doi:10.1373/clinchem.2014.230656

See Also

qpcR.news.

Examples

# Example: A simple function to test for a background range.
# Data were taken form the chipPCR C17 data set.
data(C17)
plot(C17[, 2], C17[,  3], xlab = "time [min]", ylab = "Fluorescence", 
      pch = 20)
res <- bg.max(C17[, 2], C17[, 3], bg.corr = 1.4, bg.start = 3)
abline(v = c(slot(res, "bg.start"), slot(res, "bg.stop")), col = c(1,2))
abline(h = slot(res, "fluo"), col = "blue")

Description

This function is a simple simulator of an amplification reaction based on a 5-parameter Richards function. This simplified approach was chosen because it is impossible to model the shape of any amplification curve. An implementation of realistic models is ambitious and not conclusively addressed in the literature. First, they have to take “all” random effects of noise into consideration and second, they need to be generic enough to cover all amplification processes. More sophisticated mechanistic models and simulations have been proposed elsewhere mehra_2005, cobbs_2012. This approach of AmpSim is similar to the pcrsim function from the qpcR package, which offers simulations of sigmoidal qPCR data with goodness-of-fit analysis by Ritz and Spiess 2008.

Usage

AmpSim(cyc = 1:35, b.eff = -25, bl = 0.05, ampl = 1, Cq = 20, 
	noise = FALSE, nnl = 0.025, nnl.method = "constant")

Arguments

Details

AmpSim is a simple simulator for amplification reaction. This function has several parameters which can be used to simulate the amplification curve. b.eff and Cq are most connected with another. Thus changing one of them will change both values. Cq can be used to define an approximate Cq value. The expression "approximate Cq value" is used here because the actual Cq value is dependent on the users preferred method (e.g., Cy0 method, Second Derivative Maximum (SDM) method, threshold method). AmpSim can be used to compare an experimental system to a predicted model. Moreover it can be used to simulate data with noise, missing values (NA), signal-to-noise ratios, photo-bleaching and other influences on a PCR reaction.

Author(s)

Stefan Roediger, Michal Burdukiewicz

References

A Highly Versatile Microscope Imaging Technology Platform for the Multiplex Real-Time Detection of Biomolecules and Autoimmune Antibodies. S. Roediger, P. Schierack, A. Boehm, J. Nitschke, I. Berger, U. Froemmel, C. Schmidt, M. Ruhland, I. Schimke, D. Roggenbuck, W. Lehmann and C. Schroeder. Advances in Biochemical Bioengineering/Biotechnology. 133:33–74, 2013.

Ritz, C., Spiess, A.-N.: qpcR: an R package for sigmoidal model selection in quantitative real-time polymerase chain reaction analysis. Bioinformatics 24(13), 1549–1551 (2008). doi:10.1093/bioinformatics/btn227. PMID: 18482995.

See also qpcR.news

Examples

# Example one
# Simulate a qPCR reaction with AmpSim for 40 cycles.
# Use an in-silico dilution of the template be adjusting 
# the Cq parameter. A change of 3.32 cycles corresponds 
# approximately to a 10-fold dilution.
par(mfrow = c(2,1))
plot(NA, NA, xlim = c(1,40), ylim = c(0.01,2), xlab = "Cycles", 
     ylab = "Fluorescence", main = "In-silco Dilution Experiment")
cycle.dilution <- seq(18, 35, 3.32)
for (i in 1:6) {
  lines(AmpSim(cyc = 1:40, b.eff = -25, bl = 0.01, ampl = 2, 
	      Cq = cycle.dilution[i]), type = "b", col = i, pch = 20)
}

# Example two
# Simulate a qPCR reaction with AmpSim for 40 cycles and some noise.
plot(NA, NA, xlim = c(1,40), ylim = c(0.01,2.2), xlab = "Cycles", 
     ylab = "Fluorescence", 
     main = "In-silco Dilution Experiment with Some Noise")
cycle.dilution <- seq(18, 35, 3.32)
for (i in 1:6) {
  lines(AmpSim(cyc = 1:40, b.eff = -25, bl = 0.01, ampl = 2, 
	      Cq = cycle.dilution[i], noise = TRUE, nnl = 0.05), 
	      type = "b", col = i, pch = 20)
}
par(mfrow = c(1,1))

# Example three
# Apply constant, increasing, decreasing nose to 
# amplification data.

par(mfrow = c(3,1))
method <- c("constant", "increase", "decrease")
for (j in 1:3){
    plot(NA, NA, xlim = c(1,40), ylim = c(0.02,2.2), xlab = "Cycles", 
	ylab = "Fluorescence", 
	main = paste("In-silco Dilution Experiment with noise method: ", 
					    method[j]))
    cycle.dilution <- seq(18, 35, 3.32)
    for (i in 1:6) {
      lines(AmpSim(cyc = 1:40, b.eff = -25, bl = 0.02, ampl = 2, 
		   Cq = cycle.dilution[i], noise = TRUE, nnl = 0.08, 
		   nnl.method = method[j]), type = "b", col = i, pch = 20)
   }
}
par(mfrow = c(1,1))

Description

Launches graphical user interface that allows simulating and analyzing amplification reactions. This function will open the GUI in a webpage of the default browser. All parameters of the AmpSim function can be used. In addition to this, the GUI shows some information calculated by the bg.max in a summary field and a plot below the simulated amplification curve.

Usage

AmpSim.gui()

Warning

Any ad-blocking software may be cause of malfunctions.

Author(s)

Stefan Roediger, Michal Burdukiewicz.

See Also

AmpSim, bg.max

Examples

# The code chunk below will fail if the web browser is not installed. if on UNIX platform try:
# as.vector(Sys.getenv("R_BROWSER"))
# Invoke the shiny AmpSim app in the default browser
## Not run: 
#do not execute using example(), it breaks the sequence of the plots in shiny app
AmpSim.gui()

## End(Not run)

Description

An S4 class containing the output amptester function.

Slots

.Data:

"numeric" is a vector containing the fluorescence values.

decisions:

"logical" contains outcomes of various tests. shap.noisy is presence of noise, lrt.test states if data are likely from a amplification curve and both tht.dec and tht.dec defines if the amplification is "positive" or "negative".

noiselevel:

"numeric" user-defined threshold for a significant amplification signal.

background:

range of the background signal in the amplification curve.

polygon:

The pco test determines if the points in an amplification curve (like a polygon) are in a "clockwise" order. The sum over the edges result in a positive value if the amplification curve is "clockwise" and is negative if the curve is counter-clockwise.

slope.ratio:

ratio of the slopes at the start and the end of exponential phase..

Methods

summary

signature(object = "amptest"): prints summary of the object. Silently returns vector of all calculated parameters.

show

signature(object = "amptest"): prints only .Data slot of the object.

plot

signature(object = "amptest"): plots input data and graphical interpretation of link{amptester} tests' results.

Author(s)

Stefan Roediger, Michal Burdukiewicz

See Also

amptester

Examples

# Compare a positive and a negative amplification reaction.
# First simulate positive reaction (fluo.pos) and than the 
# negative reaction (fluo.neg).
# Simulation of an amplifiaction curve with some noise and a high signal.

fluo.pos <- AmpSim(cyc = 1:40, noise = TRUE)[, 2]
ampt.pos <- amptester(fluo.pos, manual = TRUE, background = c(0, 15), 
		      noiselevel = 0.15)

# Plot amplification curve and result of amptester
plot(fluo.pos, xlab = "Cycles", ylab = "RFU", pch = 19, ylim = c(0, 1))
lines(ampt.pos, col = 2, pch = 19, type = "b")
legend(5, 1, c("Raw data", "amptester output"), 
       col = c(1,2,3), bty = "n", pch = c(19, 19))
# Borders for background calculation
abline(v = c(0,15), col = 2)
# Level for background threshold
abline(h = 0.15, col = 3, lty = 2)
text(5, 0.18, "Noise threshold")
# Summary of amptester results 
summary(ampt.pos)

# Simulation of an amplifiaction curve with high noise and a low signal.

fluo.neg <- AmpSim(cyc = 1:40, noise = TRUE, ampl = 0.13, nnl = 0.4)[, 2]
ampt.neg <- amptester(fluo.neg, manual = TRUE, background = c(0, 15), 
		      noiselevel = 0.15)

# Plot amplification curve and result of amptester
plot(fluo.neg, xlab = "Cycles", ylab = "RFU", pch = 19, ylim = c(0, 1))
lines(ampt.neg, col = 2, pch = 19, type = "b")
legend(5, 1, c("Raw data", "amptester output"), 
       col = c(1,2,3), bty = "n", pch = c(19, 19))
# Borders for background calculation
abline(v = c(0,15), col = 2)
# Level for background threshold
abline(h = 0.15, col = 3, lty = 2)
text(5, 0.18, "Noise threshold")
# Summary of amptester results
summary(ampt.neg)
#plot amptester results
plot(ampt.neg)

Description

The amptester function tests if an amplification is significant.

Usage

amptester(y, manual = FALSE, noiselevel = 0.08, background = NULL)

Arguments

Details

This function tries to estimate if data indicates an amplification process. Several instances of tests are included. The first involves a semiautomatic test if the range of the background is lower than the range of the assumed signal. To differ between the ranges an instance of bg.max is used. Herein, this function assumes that an amplification takes place in case the signal of the amplification is larger than the median + 5 * mad than the background. The automatic test uses a Wilcoxon rank sum test wilcox.test to compare the first and the last elements of the data. The input values are delivered by head and tail, respectively. For other methods please refer to the references listed below. Instead of assigning a zero to negative amplification reaction uses the current implementation of amptester very small random values. This is because some post function might fail in case all values are set to zero.

# FIRST TEST - Shapiro test (SHt)

This is a simple test based on the hypothesis that in case amplification curve data come from noise is the distribution similar to a normal distribution. The Shapiro's normality test is used to test this hypothesis. If p is >= 5e-04, then the distribution of the curve data indicates noise (no amplification).

# SECOND TEST - Resids growth test (RGt)

This tests if fluorescence values in a linear phase are stable. Whenever no amplification occurs, fluorescence values quickly deviate from linear model. Their standardized residuals will be strongly correlated with their value. For real amplification curves, situation is much more stable. Noise (that means deviations from linear model) in background do not correlate strongly with the changes in fluorescence. The decision is based on the threshold value (here 0.5).

# THIRD TEST - Linear Regression test (LRt)

This test determines the R^2 by a linear regression. The R^2 are determined from a run of circa 15 percent range of the data. If a sequence of more than six R^2s is larger than 0.8 is found that is likely a nonlinear signal. This is a bit counterintuitive because R^2 of nonlinear data should be low.

# FOURTH TEST (MANUAL) - Threshold test (THt)

This a commonly employed method. In the manual test one needs to define a fixed threshold. If the amplification curve signal exceeds the threshold than the amplification reaction is positive. Waring: This method will report positive amplification reaction if a negative amplification has a positive trend.

# FOURTH TEST (AUTOMATIC) - Threshold test (THt)

Takes the first 20 percent and the last 15 percent of any input data set (amplification curve) and perform a Wilcoxon rank sum tests with the head (nh) and tail (nt). This test is recommended over the manual THt. Warning: This method may report positive amplification reaction if a negative amplification has a positive trend.

# FIFTH TEST - Signal level test (SLt)

The meaningfulness of an amplification curve reaction can be tested by comparison of the signals 1) A robust "sigma" rule by median + 2 * mad 2) comparison of the signal/noise ratio. If less than 1.25 (25 percent) signal increase it is likely that nothing happened during the reaction. Waring: This method may report positive amplification reaction if a negative amplification has a positive trend.

# SIXTH TEST - pco test (pco)

This test determines if the points in an amplification curve (like a polygon, in particular non-convex polygons) are in a "clockwise" order. The sum over the edges result in a positive value if the amplification curve is "clockwise" and is negative if the curve is counter-clockwise ((x2 - x1)(y2 + y1)). From experience is noise positive and "true" amplification curves "highly" negative. This test depends on the definition of a threshold.

# SEVENTH TEST - Slope ratio (SlR)

Uses the inder function to find the approximated first derivative maximum, second derivative minimum and the second derivative maximum. Next the raw fluorescence at the approximated second derivative minimum and the second derivative maximum are taken from the original data set. The fluorescence intensities are normalized to the maximum fluorescence of this data. This data is used for a linear regression. Where the slope is used.

Value

An object of amptest class containing result of the test as well as the original data.

Author(s)

Stefan Roediger, Michal Burdukiewicz

References

Frank, D. N. BARCRAWL and BARTAB: software tools for the design and implementation of barcoded primers for highly multiplexed DNA sequencing BMC bioinformatics, 2009, Vol. 10, pp. 362

Peirson, S. N., Butler, J. N. and Foster, R. G. Experimental validation of novel and conventional approaches to quantitative real-time PCR data analysis Nucleic Acids Research, 2003, Vol. 31(14), pp. e73-e73

Rao, X., Lai, D. and Huang, X. A New Method for Quantitative Real-Time Polymerase Chain Reaction Data Analysis Journal of Computational Biology, 2013, Vol. 20(9), pp. 703-711

Ruijter, J. M., Ramakers, C., Hoogaars, W. M. H., Karlen, Y., Bakker, O., Hoff, M. J. B. v. d. and Moorman, A. F. M. Amplification efficiency: linking baseline and bias in the analysis of quantitative PCR data, Nucleic Acids Research, 2009, Vol. 37(6), pp. e45-e45

Rutledge, R. G. and Stewart, D. A kinetic-based sigmoidal model for the polymerase chain reaction and its application to high-capacity absolute quantitative real-time PCR BMC biotechnology, 2008, Vol. 8, pp. 47

Tichopad, A., Dilger, M., Schwarz, G. and Pfaffl, M. W. Standardized determination of real-time PCR efficiency from a single reaction set-up Nucleic Acids Research, 2003, Vol. 31(20), pp. e122

Wilhelm, J., Pingoud, A. and Hahn, M. SoFAR: software for fully automatic evaluation of real-time PCR data BioTechniques, 2003, Vol. 34(2), pp. 324-332

Zhao, S. and Fernald, R. D. Comprehensive Algorithm for Quantitative Real-Time Polymerase Chain Reaction Journal of computational biology: a journal of computational molecular cell biology, 2005, Vol. 12(8), pp. 1047-1064

Examples

# First example
# Arrange graphs in orthogonal matrix and set parameter for the plot.
par(las = 0, bty = "n", cex.axis = 1.5, cex.lab = 1.5, 
    font = 2, cex.main = 1.8, oma = c(1,1,1,1))
    
# Simulation of an amplification curve with 40 cycles and a Cq of
# circa 28. The amplification curve of "pos" (positive) has low 
# noise and the amplification curve of "neg" (negative) has high 
# noise.

pos <- AmpSim(cyc = 1:40, Cq = 28, noise = TRUE, nnl = 0.03)
neg <- AmpSim(cyc = 1:40, Cq = 28, noise = TRUE, nnl = 0.8)

# Plot the raw data of the simulations.

par(fig = c(0,0.5,0.5,1))
plot(NA, NA, xlim = c(1, 40), ylim = c(0, 2.1), xlab = "Cycles", 
     ylab = "Fluorescence", main = "qPCR - Raw data", type = "b")
mtext("A", cex = 2, side = 3, adj = 0, font = 2)
points(pos, col = 1, typ = "b", pch = 19)
points(neg, col = 2, typ = "b", pch = 20)
legend(1, 2, c("Positive", "Negative Control (noise)"), 
	       pch = c(19,20), col = c(1,2), lwd = 2, bty = "n")

# Plot data again after an analysis by ampteser. "neg" is set to small 
# random numbers, while "pos" remains unchanged.

par(fig = c(0,0.5,0,0.5), new = TRUE)
plot(NA, NA, xlim = c(1, 40), ylim = c(0, 2.1), xlab = "Cycles", 
     ylab = "Fluorescence", main = "qPCR - amptester", type = "b")
points(amptester(pos[, 2]), col = 1, type = "b", pch = 19)
points(amptester(neg[, 2]), col = 2, type = "b", pch = 20)
legend(1, 2, c("Positive", "Negative Control (noise)"), 
       pch = c(19,20), col = c(1,2), lwd = 2, bty = "n")

# Use of amptester for time-dependent measurements. Amplification curves 
# from the capillaryPCR data set were processed in a loop. The results of 
# amptester are added to the raw data.

par(fig = c(0.5,1,0,1), new = TRUE)
colors <- rainbow(8)
plot(NA, NA, xlim = c(0,80), ylim = c(0,1300), xlab = "Time [min]", 
     ylab = "Voltage (micro V)", main = "ccPCR")
mtext("B", cex = 2, side = 3, adj = 0, font = 2)
sapply(c(1,3,5,7), function(i) {
    xy.tmp <- cbind(capillaryPCR[1:750, i], capillaryPCR[1:750, i + 1])
    
# Use amptester to analyse the amplification curve.
# Note: The decisions of amptester can be invoked via res.ampt@decisions
# in the present example.

    res.ampt <- amptester(xy.tmp[, 2])
    
# Use the "decisions" of amptester in a logic to automatically decide if an
# amplification reaction is positive. In this example linear regression test
# (lrt.test) and the threshold test (tht.dec) are used.

    res.ampt <- ifelse(res.ampt@decisions[2] == TRUE && 
		       res.ampt@decisions[4] == TRUE, "positve", "negative")

# Plot the amplification curve with the decisions.
    lines(xy.tmp[, 1], xy.tmp[, 2], type = "b", pch = 20, col = colors[i])
    text(75, max(na.omit(xy.tmp[, 2])), res.ampt, cex = 1.3, col = colors[i])
  }
)
# Second Example
# Example to test an amplification reaction.
# Simulate first a positive amplification curve with 45 cycles and than a 
# negative amplification curve with 45 cycles. The negative amplification
# curve is created from a normal distribution
# 
fluo.neg <- rnorm(45)
fluo.pos <- AmpSim(cyc = 1:45, Cq = 45, ampl = 40, noise = TRUE, 
		   nnl = 0.03)[, 2]

plot(NA, NA, xlim = c(1, 45), ylim = c(-1, 45), xlab = "Cycles", 
     ylab = "Fluorescence", 
     main = "Simulation of a qPCR with 45 Cycles", type = "b")
points(amptester(fluo.pos), type = "b", pch = 20)
points(amptester(fluo.neg), type = "b", col = "red", pch = 20)
points(1:45, fluo.neg, col = "red")

legend(1,40, c("Positive", "Negative Control (noise)", 
       "noise pattern"), pch = c(20,20,1), col = c(1,2,2), lwd = 2)

Description

amptester.gui is a graphical user interface for the amptester function. This function can be used for a fast and convenient analysis of amplification curve data. In addition it is possible to analyze the Cq (quantification cycle) and to perform a report generation of the analyzed data.

Usage

amptester.gui()

Warning

Any ad-blocking software may be cause of malfunctions.

Author(s)

Stefan Roediger, Michal Burdukiewicz.

See Also

AmpSim, bg.max

Examples

# The code chunk below will fail if the web browser is not installed. if on UNIX platform try:
# as.vector(Sys.getenv("R_BROWSER"))
# Invoke the shiny AmpSim app in the default browser
## Not run: 
#do not execute using example(), it breaks the sequence of the plots in shiny app
amptester.gui()

## End(Not run)

Description

An S4 class containing the output bg.max function.

Slots

.Data:

"matrix" which columns represent respectively cycle number, raw fluorescence data, first derivative and second derivative.

bg.start:

"numeric" value representing start of the background range.

bg.stop:

"numeric" value representing end of the background range.

bg.corr:

"numeric" a value which helps to tweak on the suggested background value of bg.max.

fluo:

"numeric" a value of fluorescence at the end of amplification.

amp.stop:

"numeric" value representing end of the amplification .

Methods

plot

signature(x = "bg"): plots background information. See plot.bg

show

signature(object = "bg"): prints only .Data slot of the object.

summary

signature(object = "bg"): prints information about object prettier than show and allows easy access to some slots. See summary.bg

Author(s)

Stefan Roediger, Michal Burdukiewicz

See Also

bg.max, plot.bg, summary.bg

Examples

res <- AmpSim(cyc = 1:40, Cq = 25)
tmp <- bg.max(res)
summary(tmp)
plot(tmp)

Description

bg.max detects and corrects background noise. The detection is made without any assumptions regarding the model of this function.

Usage

## S4 method for signature 'numeric,numeric'
bg.max(x, y, bg.corr = 1.3, bg.start = 2, 
		  inder.approx = TRUE)

## S4 method for signature 'matrix,missing'
bg.max(x, y, bg.corr = 1.3, bg.start = 2, 
		  inder.approx = TRUE)

## S4 method for signature 'data.frame,missing'
bg.max(x, y, bg.corr = 1.3, bg.start = 2, 
		  inder.approx = TRUE)

Arguments

Details

Background range herein refers to a level of fluorescence measured before any specific amplification is detectable. The raw data (e.g., fluorescence intensity) measured after each step (cycle or time point) follow a non-linear progress. The background is assumed to be constant for the entire measurement. The algorithm of bg.max is based on the assumption that during the linear ground phase the signal difference of successive cycles is approximately constant. After transition to the early exponential phase the signal changes drastically. First data are smoothed by Friedman's 'super smoother' (as found in supsmu. Thereof the approximate first and second derivative are calculated by a five-point stencil inder.

The difference of cycles at the maxima of the first and second approximate derivative as well as a correction factor are used to estimate the range before the exponential phase. This function finds the background range without modeling the relationship between signal and cycle number. The start of the background range is defined be a fixed value. Since many signals tend to overshot in the first cycles a default value of 3 is chosen. bg.max tries also to estimate the end of an amplification reaction.

Value

An object of bg class containing predicted background range as well as other parameters.

Author(s)

Stefan Roediger, Michal Burdukiewicz

References

D. N. Frank. BARCRAWL and BARTAB: software tools for the design and implementation of barcoded primers for highly multiplexed DNA sequencing. BMC Bioinformatics, 10:362, 2009. ISSN 1471-2105. doi: 10.1186/1471-2105-10-362. PMID: 19874596 PMCID: PMC2777893.

S. N. Peirson, J. N. Butler, and R. G. Foster. Experimental validation of novel and conventional approaches to quantitative real-time PCR data analysis. Nucleic Acids Research, 31(14):e73, July 2003. ISSN 1362-4962. PMID: 12853650 PMCID: PMC167648.

X. Rao, D. Lai, and X. Huang. A new method for quantitative real-time polymerase chain reaction data analysis. Journal of computational biology: a journal of computational molecular cell biology, 20(9):703–711, Sept. 2013. ISSN 1557-8666. doi: 10.1089/cmb.2012.0279. PMID: 23841653 PMCID: PMC3762066.

A. Tichopad, M. Dilger, G. Schwarz, and M. W. Pfaffl. Standardized determination of real-time PCR efficiency from a single reaction set-up. Nucleic Acids Research, 31(20):e122, Oct. 2003. ISSN 1362-4962. PMID: 14530455 PMCID: PMC219490.

J. Wilhelm, A. Pingoud, and M. Hahn. Real-time PCR-based method for the estimation of genome sizes. Nucleic Acids Research, 31(10):e56, May 2003. ISSN 0305-1048. PMID: 12736322 PMCID: PMC156059.

S. Zhao and R. D. Fernald. Comprehensive algorithm for quantitative real-time polymerase chain reaction. Journal of computational biology: a journal of computational molecular cell biology, 12(8): 1047–1064, Oct. 2005. ISSN 1066-5277. doi:10.1089/cmb.2005.12.1047. PMID: 16241897 PMCID: PMC2716216.

Examples

# First example: Test for the background of an amplification reaction.
par(mfrow = c(2,1))
res <- AmpSim(cyc = 1:40, Cq = 25)
background <- bg.max(res)
plot(background, main = "Estimation of the Background Range\n
in Absence of Noise")
res.noise <- AmpSim(cyc = 1:40, Cq = 25, noise = TRUE)
background.noise <- bg.max(res.noise)
plot(background.noise, main = "Estimation of the Background Range\n
in Presence of Noise")
par(mfrow = c(1,1))


# Second example: A simple function to test for a background range.
# Data were taken form the chipPCR C17 data set.
# Note that the not the time but the "cycle number" was
# used to calculate the background range.
data(C17)
plot(C17[, 2], C17[,  3], xlab = "Cycle", ylab = "RFU", 
     main = "Estimate the begin of the Amplification\n of a HDA", 
     pch = 20)
res <- bg.max(C17[, 2:3], bg.corr = 1.4, bg.start = 1)
abline(v = c(slot(res, "bg.start"), slot(res, "bg.stop")), 
       col = c(1,2))
abline(h = slot(res, "fluo"), col = "blue")

# Third example: Test for the background of an amplification reaction.
# Simulate amplification curves with different quantification points
# within 40 cycles.
cyc <- seq(1, 40, 1)

# Use a five parameter model to simulate amplification curves
b <- -15; c <- 0.02; d <- 1
# Define the different quantification points with a difference of
# circa 3.32 cycles
e <- seq(21, 35, 3.32)

# Plot the amplification curves and the estimated background ranges.
plot(NA, NA, xlim = c(1, 40), ylim = c(0, 1), xlab = "Cycles", 
     ylab = "Fluorescence")
     
for (i in 1:length(e)) {
  fluo <- c + (d - c)/(1 + exp(b * (log(cyc) - log(e[i]))))
  points(cyc, fluo, type = "b", col = i, pch = 20)
  res <- bg.max(cyc, fluo, bg.corr = 1.4, bg.start = 1)
  abline(v = slot(res, "bg.stop"), col = i)
  abline(h = slot(res, "fluo"), col = i)
}

Description

A quantitative PCR (qPCR) with the DNA binding dye (EvaGreen) (Mao et al. 2007) was performed in a Bio-Rad iQ5 thermo cycler. The cycle-dependent increase of the fluorescence was quantified at the annealing step (59.5 degrees Celsius).

Usage

data(C126EG595)

Format

A data frame with 40 observations on the following 97 variables. The first column ("Cycle") contains the number of cycles and consecutive columns contain the replicates ("A01" to "H12").

Details

HPRT1 was amplified in the Bio-Rad iQ5. The the change of fluorescence was simultaneously monitored for the Hydrolysis probe of HPRT1 and EvaGreen. The primer sequences for HPRT1 were taken from Roediger et al. (2013). A 10 micro L qPCR reaction was composed of 250 nM primer (forward and reverse), qPCR Mix (according to the manufactures recommendations), 1 micro L template (HPRT1 amplification product), 60 nM hydrolysis probe probe for HPRT1. EvaGreen was used at 0.5 x final. During the amplification was monitored 59.5 degrees Celsius.

Source

Stefan Roediger, Claudia Deutschmann (BTU Cottbus - Senftenberg)

References

A Highly Versatile Microscope Imaging Technology Platform for the Multiplex Real-Time Detection of Biomolecules and Autoimmune Antibodies. S. Roediger, P. Schierack, A. Boehm, J. Nitschke, I. Berger, U. Froemmel, C. Schmidt, M. Ruhland, I. Schimke, D. Roggenbuck, W. Lehmann and C. Schroeder. Advances in Biochemical Bioengineering/Biotechnology. 133:33–74, 2013.

Mao, F., Leung, W.-Y., Xin, X., 2007. Characterization of EvaGreen and the implication of its physicochemical properties for qPCR applications. BMC Biotechnol. 7, 76.

Examples

data(C126EG595)
tmp  <- C126EG595

plot(NA,NA, xlim = c(1,40), ylim = c(min(tmp[, 2:ncol(tmp)]), 
   max(tmp[, 2:ncol(tmp)])), xlab = "Cycle", ylab = "RFU (FAM)", 
   main = "Amplification monitored at \n58.5 degrees Celsius (annealing step)")
apply(tmp[, 2:ncol(tmp)], 2, 
      function(x) lines(tmp[1:nrow(tmp),1],x))

Description

A quantitative PCR (qPCR) with the DNA binding dye (EvaGreen) (Mao et al. 2007) was performed in the Roche Light Cycler 1.5 thermo cycler. The cycle-dependent increase of the fluorescence was quantified at the elongation step (68.5 degrees Celsius).

Usage

data(C126EG685)

Format

A data frame with 40 observations on the following 97 variables. The first column ("Cycles") contains the number of cycles and consecutive columns contain the replicates ("A01" to "H12").

Details

MLC-2v was amplified in the Roche Light Cycler 1.5. The the change of fluorescence was simultaneously monitored for the Hydrolysis probe of MLC-2v and EvaGreen. The primer sequences for MLC-2v were taken from Roediger et al. (2013). A 10 micro L qPCR reaction was composed of 250 nM primer (forward and reverse), qPCR Mix (according to the manufactures recommendations), 1 micro L template (MLC-2v amplification product), 60 nM hydrolysis probe probe for MLC-2v. EvaGreen was used at 0.5 x final. The amplification was monitored at 68.5 degrees Celsius (elongation step).

Source

Claudia Deutschmann & Stefan Roediger, BTU Cottbus - Senftenberg, Senftenberg, Germany

References

A Highly Versatile Microscope Imaging Technology Platform for the Multiplex Real-Time Detection of Biomolecules and Autoimmune Antibodies. S. Roediger, P. Schierack, A. Boehm, J. Nitschke, I. Berger, U. Froemmel, C. Schmidt, M. Ruhland, I. Schimke, D. Roggenbuck, W. Lehmann and C. Schroeder. Advances in Biochemical Bioengineering/Biotechnology. 133:33–74, 2013.

Mao, F., Leung, W.-Y., Xin, X., 2007. Characterization of EvaGreen and the implication of its physicochemical properties for qPCR applications. BMC Biotechnol. 7, 76.

Examples

data(C126EG685)
tmp <- C126EG685

plot(NA,NA, xlim = c(1,40), ylim = c(min(tmp[, 2:ncol(tmp)]), 
    max(tmp[, 2:ncol(tmp)])), xlab = "Cycle", 
    ylab = "RFU (FAM)", 
    main = "Amplification monitored at \n68.5 degrees Celsius (elongation 
step)")

apply(tmp[, 2:ncol(tmp)], 2, 
      function(x) lines(tmp[1:nrow(tmp),1],x))

Description

Quantitative PCR (qPCR) with a hydrolysis probe (Cy5/BHQ2) and DNA binding dye (EvaGreen) (Mao et al. 2007) was performed in the Roche Light Cycler 1.5 thermo cycler. The cycle-dependent increase of the fluorescence was quantified at the annealing step.

Usage

data(C127EGHP)

Format

A data frame with 40 observations on the following 66 variables. The first columns ("index") contains index of a sample and second column ("Cycle") contains the number of cycle. Consecutive columns EG1-EG32 contains fluorescence data for Eva Green dye. Consecutive columns HP1-HP32 contains data for hydrolysis probe.

Details

MLC-2v was amplified in the Roche Light Cycler 1.5. The the change of fluorescence was simultaneously monitored for the Hydrolysis probe of MLC-2v and EvaGreen. The primer sequences for MLC-2v were taken from Roediger et al. (2013). A 10 micro L qPCR reaction was composed of 250 nM primer (forward and reverse), qPCR Mix (according to the manufactures recommendations), 1 micro L template (MLC-2v amplification product), 60 nM hydrolysis probe probe for MLC-2v. EvaGreen was used at 0.5 x final. During the amplification was monitored 59.5 degrees Celsius.

Source

Claudia Deutschmann & Stefan Roediger, BTU Cottbus - Senftenberg, Senftenberg, Germany

References

A Highly Versatile Microscope Imaging Technology Platform for the Multiplex Real-Time Detection of Biomolecules and Autoimmune Antibodies. S. Roediger, P. Schierack, A. Boehm, J. Nitschke, I. Berger, U. Froemmel, C. Schmidt, M. Ruhland, I. Schimke, D. Roggenbuck, W. Lehmann and C. Schroeder. Advances in Biochemical Bioengineering/Biotechnology. 133:33–74, 2013.

Mao, F., Leung, W.-Y., Xin, X., 2007. Characterization of EvaGreen and the implication of its physicochemical properties for qPCR applications. BMC Biotechnol. 7, 76.

Examples

str(C127EGHP)
data(C127EGHP)
tmp <- C127EGHP

par(mfrow = c(2,1))
plot(NA, NA, xlim = c(1,40), ylim = c(0,10), xlab = "Cycle", 
      ylab = "Fluorescence", main = "MLC-2v qPCR - EvaGreen")
  for (i in 3:34) {
    points(tmp[, 2], tmp[, i], type = "l", col = i)
  }

plot(NA, NA, xlim = c(1,40), ylim = c(0,10), xlab = "Cycle", 
      ylab = "Fluorescence", main = "MLC-2v qPCR - Hydrolysis probe")
  for (i in 35:66) {
    points(tmp[, 2], tmp[, i], type = "l", col = i)
  }
par(mfrow = c(1,1))

Description

A Helicase Dependent Amplification (HDA) of HPRT1 (Homo sapiens hypoxanthine phosphoribosyltransferase 1) was performed at different temperatures in the 'VideoScan' Platform 2.0 (similar to Roediger et al. (2013)). The HDA was performed at 55, 60 and 65 degrees Celsius. The optimal temperature for a HDA is circa 65 degrees Celsius. Lower temperatures will affect the slope and plateau of the HDA amplification curve.

Usage

data(C17)

Format

A data frame with 125 observations on the following 5 variables.

C17.t

Elapsed time during HDA in seconds.

C17.cycle

a numeric vector

C17.T55

Time-dependent fluorescence at 55 degrees Celsius

C17.T60

Time-dependent fluorescence at 60 degrees Celsius

C17.T65

Time-dependent fluorescence at 65 degrees Celsius

Details

To perform an isothermal amplification in 'VideoScan' 2.0, standard conditions for the IsoAmp(R) III Universal tHDA Kit (Biohelix) were used. The reaction was composed of 12.5 micro L buffer A containing 1.25 micro L 10x reaction buffer, 150 nM primer (forward and reverse), 0.75 micro L template (synthetic) and A. bidest which was covered with 50 micro L mineral oil. The primer sequences for HPRT1 were taken from Roediger et al. (2013). Preincubation: This mixture was incubated for 2 min at 95 degree. Celsius and immediately placed on ice. 12.5 micro L of reaction buffer B which was composed of 1.25 micro L 10x buffer, 40 mM NaCl, 5 mM MgSO4, 1.75 micro L dNTPs, 0.2 x EvaGreen, 1 micro L Enzyme mix and A. bidest. The fluorescence measurement in 'VideoScan' 'HCU' started directly after adding buffer B at 55, 60 or 65 degrees Celsius and revealed optimal conditions for the amplification when using 60 or 65 degrees Celsius. Temperature profile (after Preincubation): - 60 seconds at 65 degrees Celsius - 11 seconds at 55 degrees Celsius && Measurement

Source

Claudia Deutschmann & Stefan Roediger, BTU Cottbus - Senftenberg, Senftenberg, Germany

References

A Highly Versatile Microscope Imaging Technology Platform for the Multiplex Real-Time Detection of Biomolecules and Autoimmune Antibodies. S. Roediger, P. Schierack, A. Boehm, J. Nitschke, I. Berger, U. Froemmel, C. Schmidt, M. Ruhland, I. Schimke, D. Roggenbuck, W. Lehmann and C. Schroeder. Advances in Biochemical Bioengineering/Biotechnology. 133:33–74, 2013.

Examples

data(C17)
plot(NA, NA, xlim = c(0,5000), ylim = c(0,1.2), xlab = "Time [sec]", 
     ylab = "Fluorescence", 
     main = "Temperature dependency of HDA amplification reactions")
  points(C17[, 1], C17[, 3], type = "b", col = 1, pch = 20)
  points(C17[, 1], C17[, 4], type = "b", col = 2, pch = 20)
  points(C17[, 1], C17[, 5], type = "b", col = 3, pch = 20)
legend(2000, 0.4, c("55 degrees Celsius", "60 degrees Celsius", "65 degrees Celsius"), 
	col = c(1,2,3), pch = rep(20,3))

Description

A quantitative real-time PCR of adk was performed.

Usage

data("C316.amp")

Format

A data frame with 40 observations on the following 97 variables. The first column ("Cycle") contains the number of cycles and consecutive columns contain the replicates ("A01" to "H12").

Details

adk was amplified in the Bio-Rad iQ5. The change of fluorescence was simultaneously monitored (EvaGreen, Mao et al. 2007). The primer sequences for adk were taken from this study.

gDNA: 28.43 ng/microL DNA concentration, 260/280 ratio= 1.96

adk fw: CTCAGGCTCAGTTCATCATGGA adk rv: AGTTTGCCAGCATCCATAATGTC

PCR conditions: 10 minutes at 95 degrees Celsius 40 x 30 seconds at 95 degrees Celsius 45 seconds at 59 degrees Celsius 45 seconds at 68 degrees Celsius

Source

Stefan Roediger, Claudia Deutschmann, Claudia Zelck (BTU Cottbus - Senftenberg)

References

A Highly Versatile Microscope Imaging Technology Platform for the Multiplex Real-Time Detection of Biomolecules and Autoimmune Antibodies. S. Roediger, P. Schierack, A. Boehm, J. Nitschke, I. Berger, U. Froemmel, C. Schmidt, M. Ruhland, I. Schimke, D. Roggenbuck, W. Lehmann and C. Schroeder. Advances in Biochemical Bioengineering/Biotechnology. 133:33–74, 2013.

Mao, F., Leung, W.-Y., Xin, X., 2007. Characterization of EvaGreen and the implication of its physicochemical properties for qPCR applications. BMC Biotechnol. 7, 76.

Examples

data(C316.amp)
str(C316.amp)

Description

A melting curve for adk was performed.

Usage

data("C316.melt.hr")

Format

A data frame with 40 observations on the following 97 variables. The first column ("T") contains the temperature (resolution: 0.2 degrees Celsius / step) and consecutive columns contain the replicates ("A01" to "H12").

Details

adk was amplified in the Bio-Rad iQ5. After PCR, the temperature-dependent change of fluorescence was simultaneously monitored (EvaGreen, Mao et al. 2007). The primer sequences for adk were taken from this study.

adk fw: CTCAGGCTCAGTTCATCATGGA adk rv: AGTTTGCCAGCATCCATAATGTC

PCR conditions: C316.amp

Source

Stefan Roediger, Claudia Deutschmann, Claudia Zelck (BTU Cottbus - Senftenberg)

References

A Highly Versatile Microscope Imaging Technology Platform for the Multiplex Real-Time Detection of Biomolecules and Autoimmune Antibodies. S. Roediger, P. Schierack, A. Boehm, J. Nitschke, I. Berger, U. Froemmel, C. Schmidt, M. Ruhland, I. Schimke, D. Roggenbuck, W. Lehmann and C. Schroeder. Advances in Biochemical Bioengineering/Biotechnology. 133:33–74, 2013.

Mao, F., Leung, W.-Y., Xin, X., 2007. Characterization of EvaGreen and the implication of its physicochemical properties for qPCR applications. BMC Biotechnol. 7, 76.

Examples

data(C316.melt.hr)
str(C316.melt.hr)

Description

A melting curve for adk was performed.

Usage

data("C316.melt.lr")

Format

A data frame with 40 observations on the following 97 variables. The first column ("T") contains the temperature (resolution: 0.5 degrees Celsius / step) and consecutive columns contain the replicates ("A01" to "H12").

Details

adk was amplified in the Bio-Rad iQ5. After PCR, the temperature-dependent change of fluorescence was simultaneously monitored (EvaGreen, Mao et al. 2007). The primer sequences for adk were taken from this study.

adk fw: CTCAGGCTCAGTTCATCATGGA adk rv: AGTTTGCCAGCATCCATAATGTC

PCR conditions: C316.amp

Source

Stefan Roediger, Claudia Deutschmann, Claudia Zelck (BTU Cottbus - Senftenberg)

References

A Highly Versatile Microscope Imaging Technology Platform for the Multiplex Real-Time Detection of Biomolecules and Autoimmune Antibodies. S. Roediger, P. Schierack, A. Boehm, J. Nitschke, I. Berger, U. Froemmel, C. Schmidt, M. Ruhland, I. Schimke, D. Roggenbuck, W. Lehmann and C. Schroeder. Advances in Biochemical Bioengineering/Biotechnology. 133:33–74, 2013.

Mao, F., Leung, W.-Y., Xin, X., 2007. Characterization of EvaGreen and the implication of its physicochemical properties for qPCR applications. BMC Biotechnol. 7, 76.

Examples

data(C316.melt.lr)
str(C316.melt.lr)

Description

A quantitative real-time PCR of adk was performed.

Usage

data("C317.amp")

Format

A data frame with 40 observations on the following 97 variables. The first column ("Cycle") contains the number of cycles and consecutive columns contain the replicates ("A01" to "H12").

Details

adk was amplified in the Bio-Rad CFX96. The change of fluorescence was simultaneously monitored (EvaGreen, Mao et al. 2007). The primer sequences for adk were taken from this study.

gDNA: 28.43 ng/microL DNA concentration, 260/280 ratio= 1.96

adk fw: CTCAGGCTCAGTTCATCATGGA adk rv: AGTTTGCCAGCATCCATAATGTC

PCR conditions: 10 minutes at 95 degrees Celsius 40 x 30 seconds at 95 degrees Celsius 45 seconds at 59 degrees Celsius 45 seconds at 68 degrees Celsius

Source

Stefan Roediger, Claudia Deutschmann, Claudia Zelck (BTU Cottbus - Senftenberg)

References

A Highly Versatile Microscope Imaging Technology Platform for the Multiplex Real-Time Detection of Biomolecules and Autoimmune Antibodies. S. Roediger, P. Schierack, A. Boehm, J. Nitschke, I. Berger, U. Froemmel, C. Schmidt, M. Ruhland, I. Schimke, D. Roggenbuck, W. Lehmann and C. Schroeder. Advances in Biochemical Bioengineering/Biotechnology. 133:33–74, 2013.

Mao, F., Leung, W.-Y., Xin, X., 2007. Characterization of EvaGreen and the implication of its physicochemical properties for qPCR applications. BMC Biotechnol. 7, 76.

Examples

data(C317.amp)
  str(C317.amp)

Description

A melting curve for adk was performed.

Usage

data("C317.melt.hr")

Format

A data frame with 40 observations on the following 97 variables. The first column ("Temperature") contains the temperature (resolution: 0.1 degrees Celsius / step) and consecutive columns contain the replicates ("A1" to "H12").

Details

adk was amplified in the Bio-Rad CFX96. After PCR, the temperature-dependent change of fluorescence was simultaneously monitored (EvaGreen, Mao et al. 2007). The primer sequences for adk were taken from this study.

adk fw: CTCAGGCTCAGTTCATCATGGA adk rv: AGTTTGCCAGCATCCATAATGTC

PCR conditions: C317.amp

Source

Stefan Roediger, Claudia Deutschmann, Claudia Zelck (BTU Cottbus - Senftenberg)

References

A Highly Versatile Microscope Imaging Technology Platform for the Multiplex Real-Time Detection of Biomolecules and Autoimmune Antibodies. S. Roediger, P. Schierack, A. Boehm, J. Nitschke, I. Berger, U. Froemmel, C. Schmidt, M. Ruhland, I. Schimke, D. Roggenbuck, W. Lehmann and C. Schroeder. Advances in Biochemical Bioengineering/Biotechnology. 133:33–74, 2013.

Mao, F., Leung, W.-Y., Xin, X., 2007. Characterization of EvaGreen and the implication of its physicochemical properties for qPCR applications. BMC Biotechnol. 7, 76.

Examples

data(C317.melt.hr)
  str(C317.melt.hr)

Description

A melting curve for adk was performed.

Usage

data("C317.melt.lr")

Format

A data frame with 40 observations on the following 97 variables. The first column ("Temperature") contains the temperature (resolution: 0.5 degrees Celsius / step) and consecutive columns contain the replicates ("A1" to "H12").

Details

adk was amplified in the Bio-Rad CFX96. After PCR, the temperature-dependent change of fluorescence was simultaneously monitored (EvaGreen, Mao et al. 2007). The primer sequences for adk were taken from this study.

adk fw: CTCAGGCTCAGTTCATCATGGA adk rv: AGTTTGCCAGCATCCATAATGTC

PCR conditions: C317.amp

Source

Stefan Roediger, Claudia Deutschmann, Claudia Zelck (BTU Cottbus - Senftenberg)

References

A Highly Versatile Microscope Imaging Technology Platform for the Multiplex Real-Time Detection of Biomolecules and Autoimmune Antibodies. S. Roediger, P. Schierack, A. Boehm, J. Nitschke, I. Berger, U. Froemmel, C. Schmidt, M. Ruhland, I. Schimke, D. Roggenbuck, W. Lehmann and C. Schroeder. Advances in Biochemical Bioengineering/Biotechnology. 133:33–74, 2013.

Mao, F., Leung, W.-Y., Xin, X., 2007. Characterization of EvaGreen and the implication of its physicochemical properties for qPCR applications. BMC Biotechnol. 7, 76.

Examples

data(C317.melt.lr)
  str(C317.melt.lr)

Description

qPCR Experiment for the amplification of MLC-2v using the 'VideoScan' heating/cooling-unit

Usage

data(C54)

Format

A data frame with 56 observations on the following 4 variables.

Cycle

Cycle number

D1

Stock concentration of input cDNA

D2

1/10 diluted stock cDNA

D3

1/100 diluted stock cDNA

Details

The aim was to amplify MLC-2v in the 'VideoScan' and to monitor with a hydrolysis probe for MLC-2v. The primer sequences for MLC-2v were taken from Roediger et al. (2013). The amplification was detected in solution of the '1 HCU' (see Roediger et al. 2013 for details). A 20 micro L PCR reaction was composed of 250 nM primer (forward and reverse), 1x Maxima Probe qPCR Master Mix (Fermentas), 1 micro L template (MLC-2v amplification product in different dilutions), 50 nM hydrolysis probe probe for MLC-2v and A. bidest. During the amplification, fluorescence was measured at 59.5 degree Celsius. The Cy5 channel was used to monitor the MLC-2v specific hydrolysis probe. Input stock cDNA was used undiluted (D1). D2 was 1/1000 and D3 1/1000000 diluted in A. bidest. The D1, D2, and D3 have different numbers measure points and D2 contains a missing value at cycle 37.

Source

Stefan Roediger, Claudia Deutschmann

References

A Highly Versatile Microscope Imaging Technology Platform for the Multiplex Real-Time Detection of Biomolecules and Autoimmune Antibodies. S. Roediger, P. Schierack, A. Boehm, J. Nitschke, I. Berger, U. Froemmel, C. Schmidt, M. Ruhland, I. Schimke, D. Roggenbuck, W. Lehmann and C. Schroeder. Advances in Biochemical Bioengineering/Biotechnology. 133:33–74, 2013.

Examples

data(C54)
str(C54)
plot(NA, NA, xlim = c(0,56), ylim = c(0, 0.7), xlab = "Cycle", 
     ylab = "refMFI")
apply(C54[, c(2:4)], 2, function(x) lines(C54[, 1], x))

Description

Dilution experiment and metling curve (C60.melt) for the human genes MLC-2v and Vimentin (see Roediger et al. 2013) using the Roch Light Cycler 1.5.

Usage

data(C60.amp)

Format

A data frame with 45 observations on the following 33 variables.

Index

Index of Cycles

Vim.0.1

Vimentin water control

Vim.0.2

Vimentin water control

Vim.1.1

Vimentin 10^-3 diluted

Vim.1.2

Vimentin 10^-3 diluted

Vim.2.1

Vimentin 10^-4 diluted

Vim.2.2

Vimentin 10^-4 diluted

Vim.3.1

Vimentin 10^-5 diluted

Vim.3.2

Vimentin 10^-5 diluted

Vim.4.1

Vimentin 10^-6 diluted

Vim.4.2

Vimentin 10^-6 diluted

Vim.5.1

Vimentin 10^-7 diluted

Vim.5.2

Vimentin 10^-7 diluted

Vim.6.1

Vimentin 10^-8 diluted

Vim.6.2

Vimentin 10^-8 diluted

Vim.7.1

Vimentin 10^-9 diluted

Vim.7.2

Vimentin 10^-9 diluted

MLC2v.1.1

MLC-2v 10^-3 diluted

MLC2v.1.2

MLC-2v 10^-3 diluted

MLC2v.2.1

MLC-2v 10^-4 diluted

MLC2v.2.2

MLC-2v 10^-4 diluted

MLC2v.3.1

MLC-2v 10^-5 diluted

MLC2v.3.2

MLC-2v 10^-5 diluted

MLC2v.4.1

MLC-2v 10^-6 diluted

MLC2v.4.2

MLC-2v 10^-6 diluted

MLC2v.5.1

MLC-2v 10^-7 diluted

MLC2v.5.2

MLC-2v 10^-7 diluted

MLC2v.6.1

MLC-2v 10^-8 diluted

MLC2v.6.2

MLC-2v 10^-8 diluted

MLC2v.7.1

MLC-2v 10^-9 diluted

MLC2v.7.2

MLC-2v 10^-9 diluted

MLC2v.0.1

MLC-2v water control

MLC2v.0.2

MLC-2v water control

Details

MLC-2v and Vimentin were amplified in the Roche Light Cycler 1.5. Decadic dilutions of the input cDNA were prepared. The change of fluorescence was simultaneously monitored with EvaGreen. The primer sequences for MLC-2v were taken from Roediger et al. (2013). A 10 micro L qPCR reaction was composed of 250 nM primer (forward and reverse), Roche qPCR Master-Mix (according to the manufactures recommendations) and 1 micro L input DNA. EvaGreen was used at 1x final. During the amplification was monitored 58 degrees Celsius. Temperature profile:

95 deg C for 8 minutes 40 x 95 deg C for 10 sec 58 deg C for 15 sec 69 deg C for 25 sec

Source

Stefan Roediger, Claudia Deutschmann (BTU Cottbus - Senftenberg)

References

A Highly Versatile Microscope Imaging Technology Platform for the Multiplex Real-Time Detection of Biomolecules and Autoimmune Antibodies. S. Roediger, P. Schierack, A. Boehm, J. Nitschke, I. Berger, U. Froemmel, C. Schmidt, M. Ruhland, I. Schimke, D. Roggenbuck, W. Lehmann and C. Schroeder. Advances in Biochemical Bioengineering/Biotechnology. 133:33–74, 2013.

Mao, F., Leung, W.-Y., Xin, X., 2007. Characterization of EvaGreen and the implication of its physicochemical properties for qPCR applications. BMC Biotechnol. 7, 76.

Examples

## data(C60.amp)
str(C60.amp)

Description

Melting curves were continuously monitored with the Light Cycler 1.5 (Roche). For details on sample preparation refer to C60.amp.

Usage

data(C60.melt)

Format

A data frame with 128 observations on the following 65 variables. Refer to C60.amp for details of the specific samples.

Index

Index of elements

Vim.0.1.T

Temperature

Vim.0.1.F

Fluorescence

Vim.0.2.T

Temperature

Vim.0.2.F

Fluorescence

Vim.1.1.T

Temperature

Vim.1.1.F

Fluorescence

Vim.1.2.T

Temperature

Vim.1.2.F

Fluorescence

Vim.2.1.T

Temperature

Vim.2.1.F

Fluorescence

Vim.2.2.T

Temperature

Vim.2.2.F

Fluorescence

Vim.3.1.T

Temperature

Vim.3.1.F

Fluorescence

Vim.3.2.T

Temperature

Vim.3.2.F

Fluorescence

Vim.4.1.T

Temperature

Vim.4.1.F

Fluorescence

Vim.4.2.T

Temperature

Vim.4.2.F

Fluorescence

Vim.5.1.T

Temperature

Vim.5.1.F

Fluorescence

Vim.5.2.T

Temperature

Vim.5.2.F

Fluorescence

Vim.6.1.T

Temperature

Vim.6.1.F

Fluorescence

Vim.6.2.T

Temperature

Vim.6.2.F

Fluorescence

Vim.7.1.T

Temperature

Vim.7.1.F

Fluorescence

Vim.7.2.T

Temperature

Vim.7.2.F

Fluorescence

MLC2v.1.1.T

Temperature

MLC2v.1.1.F

Fluorescence

MLC2v.1.2.T

Temperature

MLC2v.1.2.F

Fluorescence

MLC2v.2.1.T

Temperature

MLC2v.2.1.F

Fluorescence

MLC2v.2.2.T

Temperature

MLC2v.2.2.F

Fluorescence

MLC2v.3.1.T

Temperature

MLC2v.3.1.F

Fluorescence

MLC2v.3.2.T

Temperature

MLC2v.3.2.F

Fluorescence

MLC2v.4.1.T

Temperature

MLC2v.4.1.F

Fluorescence

MLC2v.4.2.T

Temperature

MLC2v.4.2.F

Fluorescence

MLC2v.5.1.T

Temperature

MLC2v.5.1.F

Fluorescence

MLC2v.5.2.T

Temperature

MLC2v.5.2.F

Fluorescence

MLC2v.6.1.T

Temperature

MLC2v.6.1.F

Fluorescence

MLC2v.6.2.T

Temperature

MLC2v.6.2.F

Fluorescence

MLC2v.7.1.T

Temperature

MLC2v.7.1.F

Fluorescence

MLC2v.7.2.T

Temperature

MLC2v.7.2.F

Fluorescence

MLC2v.0.1.T

Temperature

MLC2v.0.1.F

Fluorescence

MLC2v.0.2.T

Temperature

MLC2v.0.2.F

Fluorescence

Details

Melting curves were continuously monitored with the Light Cycler 1.5 (Roche).

Source

Stefan Roediger, Claudia Deutschmann (BTU Cottbus - Senftenberg)

References

A Highly Versatile Microscope Imaging Technology Platform for the Multiplex Real-Time Detection of Biomolecules and Autoimmune Antibodies. S. Roediger, P. Schierack, A. Boehm, J. Nitschke, I. Berger, U. Froemmel, C. Schmidt, M. Ruhland, I. Schimke, D. Roggenbuck, W. Lehmann and C. Schroeder. Advances in Biochemical Bioengineering/Biotechnology. 133:33–74, 2013.

Mao, F., Leung, W.-Y., Xin, X., 2007. Characterization of EvaGreen and the implication of its physicochemical properties for qPCR applications. BMC Biotechnol. 7, 76.

Examples

## data(C60.melt)
str(C60.melt)

Description

A Helicase Dependent Amplification (HDA) of HPRT1 (Homo sapiens hypoxanthine phosphoribosyltransferase 1) was performed at three different input DNA quantities using the Bio-Rad iQ5 thermo cycler. The HDA was performed at 65 degrees Celsius. The optimal temperature for a HDA is circa 65 degrees Celsius. Lower temperatures will affect the slope and plateau of the HDA amplification curve.

Usage

data(C67)

Format

A data frame with 43 observations on the following 6 variables.

Cycles.C67

a numeric vector containing the cycle numbers

t.C67

a numeric vector containing the time elapsed between the cycles. The time was calculated by the cycle duration of one iQ5 thermocycler step (71 seconds / step).

D1

Dilution 1.

D2

Dilution 2.

D3

Dilution 3.

D4

Dilution 4.

Details

To perform an isothermal amplification in 'VideoScan', standard conditions for the IsoAmp(R) III Universal tHDA Kit (Biohelix) were used. The reaction was composed of 12.5 micro L buffer A containing 1.25 micro L 10x reaction buffer, 150 nM primer (forward and reverse), 0.75 micro L template (synthetic) and A. bidest which was covered with 50 micro L mineral oil. The primer sequences for HPRT1 were taken from Roediger et al. (2013). Preincubation: This mixture was incubated for 2 min at 95 degree. Celsius and immediately placed on ice. 12.5 micro L of reaction buffer B which was composed of 1.25 micro L 10x buffer, 40 mM NaCl, 5 mM MgSO4, 1.75 micro L dNTPs, 0.2 x EvaGreen, 1 micro L Enzyme mix and A. bidest. The fluorescence measurement started directly after adding buffer B and the preincubation step. Temperature profile if the iQ5 thermo cycler (after Preincubation): - 60 seconds at 65 degrees Celsius - 11 seconds at 55 degrees Celsius && Measurement

Source

Claudia Deutschmann & Stefan Roediger, BTU Cottbus - Senftenberg, Senftenberg, Germany

References

A Highly Versatile Microscope Imaging Technology Platform for the Multiplex Real-Time Detection of Biomolecules and Autoimmune Antibodies. S. Roediger, P. Schierack, A. Boehm, J. Nitschke, I. Berger, U. Froemmel, C. Schmidt, M. Ruhland, I. Schimke, D. Roggenbuck, W. Lehmann and C. Schroeder. Advances in Biochemical Bioengineering/Biotechnology. 133:33–74, 2013.

Examples

data(C67)
## maybe str(C67) ; plot(C67) ...

Description

A Helicase Dependent Amplification (HDA) of pCNG1 was performed. The 'VideoScan' Platform (Roediger et al. (2013)) was used to monitor the amplification. The HDA was performed at 65 degrees Celsius. Two concentrations of input DNA were used.

Usage

data(C81)

Format

A data frame with 351 observations on the following 5 variables.

Cycle

Cycles HDA measurements.

t.D1

Dilution 1, elapsed time during HDA in seconds.

MFI.D1

Dilution 1, fluorescence.

t.D2

Dilution 2, elapsed time during HDA in seconds.

MFI.D2

Dilution 2, fluorescence.

Details

To perform an isothermal amplification in 'VideoScan', standard conditions for the IsoAmp(R) III Universal tHDA Kit (Biohelix) were used. The reaction was composed of reaction mix A)10 micro L A. bidest, 1.25 micro L 10xbuffer, 0.75 micro L primer(150 nM final), 0.5 micro L template plasmid. Preincubation: This mixture was incubated for 2 min at 95 degree. Celsius and immediately placed on ice. Reaction mix B) 5 micro L A. bidest., 1.25 micro L 10x buffer, 2 micro L NaCl, 1.25 micro L MgSO4, 1.75 micro L dNTPs, 0.25 micro L EvaGreen, 1 micro L enzyme mix. The mix was covered with 50 micro L mineral oil. The fluorescence measurement in 'VideoScan' 'HCU' started directly after adding buffer B at 65 degrees Celsius. A 1x (D1) and a 1:10 dilution (D2) were tested. Temperature profile (after Preincubation): - 60 seconds at 65 degrees Celsius - 11 seconds at 55 degrees Celsius && Measurement

Source

Claudia Deutschmann & Stefan Roediger, BTU Cottbus - Senftenberg, Senftenberg, Germany

References

A Highly Versatile Microscope Imaging Technology Platform for the Multiplex Real-Time Detection of Biomolecules and Autoimmune Antibodies. S. Roediger, P. Schierack, A. Boehm, J. Nitschke, I. Berger, U. Froemmel, C. Schmidt, M. Ruhland, I. Schimke, D. Roggenbuck, W. Lehmann and C. Schroeder. Advances in Biochemical Bioengineering/Biotechnology. 133:33–74, 2013.

Examples

data(C81)
# First example
# Comparison of Lowess, Moving average and splines to smooth amplification curve 
# data of
# HDA for pCNG1.

plot(NA, NA, xlim = c(0, 120), ylim = c(0, 1.2), xlab = "Time [min]", 
     ylab = "Fluorescence", main = "VideScan HCU HDA amplification - Raw data")
  points(C81[, 2]/60, C81[, 3], type = "b", col = 1, pch = 20)
  points(C81[, 4]/60, C81[, 5], type = "b", col = 2, pch = 20)
legend(2000, 0.4, c("D1", "D2"), col = c(1,2), pch = rep(20,2))

Description

A Helicase Dependent Amplification (HDA) of Vimentin (Vim) was performed. The 'VideoScan' Platform (Roediger et al. (2013)) was used to monitor the amplification. The HDA was performed at 65 degrees Celsius. Three concentrations of input DNA (D1, D2, D3) were used.

Usage

data(C85)

Format

A data frame with 301 observations on the following 5 variables.

Cycle

Cycles HDA measurements.

t.D1

Dilution 1, elapsed time during HDA in seconds.

MFI.D1

Dilution 1, fluorescence.

t.D2

Dilution 2, elapsed time during HDA in seconds.

MFI.D2

Dilution 2, fluorescence.

t.D3

Dilution 3, elapsed time during HDA in seconds.

MFI.D3

Dilution 3, fluorescence.

Details

To perform an isothermal amplification in 'VideoScan', standard conditions for the IsoAmp(R) III Universal tHDA Kit (Biohelix) were used. Primers and templates are described in Roediger et al. (2013). The reaction was composed of reaction mix A)10 micro L A. bidest, 1.25 micro L 10xbuffer, 0.75 micro L primer(150nM final), 0.5 micro L template plasmid. Preincubation: This mixture was incubated for 2 min at 95 degree. Celsius and immediately placed on ice. Reaction mix B) 5 micro L A. bidest., 1.25 micro L 10x buffer, 2 micro L NaCl, 1.25 micro L MgSO4, 1.75 micro L dNTPs, 0.25 micro L EvaGreen, 1 micro L enzyme mix. The mix was covered with 50 micro L mineral oil. The fluorescence measurement in 'VideoScan' 'HCU' started directly after adding buffer B at 65 degrees Celsius. A 1x (D1), a 1:10 dilution (D2) and a 1:100 (D3) dilution were tested. Temperature profile (after Preincubation): - 60 seconds at 65 degrees Celsius - 11 seconds at 55 degrees Celsius && Measurement

Source

Claudia Deutschmann & Stefan Roediger, BTU Cottbus - Senftenberg, Senftenberg, Germany

References

A Highly Versatile Microscope Imaging Technology Platform for the Multiplex Real-Time Detection of Biomolecules and Autoimmune Antibodies. S. Roediger, P. Schierack, A. Boehm, J. Nitschke, I. Berger, U. Froemmel, C. Schmidt, M. Ruhland, I. Schimke, D. Roggenbuck, W. Lehmann and C. Schroeder. Advances in Biochemical Bioengineering/Biotechnology. 133:33–74, 2013.

Examples

data(C85)
# First example
plot(NA, NA, xlim = c(0,85), ylim = c(0,1), xlab = "Time [min]", 
      ylab = "Fluorescence", main = "HDA amplification")
points(C85[, 2]/60, C85[, 3], type = "b", col = 1, pch = 20)
points(C85[, 4]/60, C85[, 5], type = "b", col = 2, pch = 20)
points(C85[, 6]/60, C85[, 7], type = "b", col = 3, pch = 20)
legend(40, 0.5, c("D1, 1x", "D2, 1:10", "D3, 1:100"), col = c(1:3), 
	pch = rep(20,3))

# Second example
plot(NA, NA, xlim = c(0,30), ylim = c(0,0.8), xlab = "Time [min]", 
      ylab = "Fluorescence", main = "HDA amplification")
points(C85[, 2]/60, C85[, 3], type = "b", col = 1, pch = 20)
points(C85[, 2]/60, smoother(C85[, 2]/60, C85[, 3], 
      method = list("savgol")), type = "b", col = 2, pch = 20)
points(C85[, 2]/60, smoother(C85[, 2]/60, C85[, 3], 
      method = list("smooth")), type = "b", col = 3, pch = 20)
points(C85[, 2]/60, smoother(C85[, 2]/60, C85[, 3], 
      method = list("mova")), type = "b", col = 4, pch = 20)

legend(1, 0.8, c("D1, raw", "D1, savgol", "D1, smooth", "D1, mova"), 
	col = c(1:4), pch = rep(20,4))

# Third example
# Comparison of Lowess, Moving average and splines to smooth amplification 
# curve data of
# a HDA using the 'VideoScan' 'HCU' for amplification and monitoring.

xrange <- 2:2400
plot(NA, NA, xlim = c(0,85), ylim = c(0.4, 0.8), xlab = "Time [min]", 
      ylab = "RFI", main = "Raw data")
points(C85[, 2]/60, C85[, 3], type = "b", col = 1, pch = 20)
points(C85[, 4]/60, C85[, 5], type = "b", col = 2, pch = 20)
points(C85[, 6]/60, C85[, 7], type = "b", col = 3, pch = 20)
legend("topleft", c("D1, 1x", "D2, 1:10", "D3, 1:100"), col = c(1:3), 
	pch = rep(20,3))

mtext("A", cex = 2, side = 3, adj = 0, font = 2)

plot(NA, NA, xlim = c(0,40), ylim = c(-0.05, 0.3), xlab = "Time [min]", 
      ylab = "RFI", main = "Moving average")
movaww <- seq(1,17,4)
for (i in 1:length(movaww)) {
  for (j in c(2,4,6)) {
    tmp <- data.frame(na.omit(C85[xrange, j])/60, na.omit(C85[xrange, j + 1]))
    tmp.out <- smoother(tmp[, 1], tmp[, 2], method = list(mova = list(movaww = movaww[i])), 
			bg.outliers = TRUE)
    lines(data.frame(tmp[, 1], tmp.out), type = "l", pch = 20, cex = 0.5, 
	  col = i)
    }
}
mtext("B", cex = 2, side = 3, adj = 0, font = 2)
legend("topleft", c("D1, 1x", "D2, 1:10", "D3, 1:100"), col = c(1:3), 
	pch = rep(20,3))
legend("bottomright", 6, paste("movaww : ", movaww), pch = 20, lwd = 2, 
	col = 1:length(movaww))
    
plot(NA, NA, xlim = c(0,40), ylim = c(-0.05, 0.3), xlab = "Time [min]", 
      ylab = "RFI", main = "Cubic Spline")
df.fact <- seq(0.5,0.9,0.1)
for (i in 1:length(df.fact)) {
  for (j in c(2,4,6)) {
    tmp <- data.frame(na.omit(C85[xrange, j])/60, na.omit(C85[xrange, j + 1]))
    tmp.out <- smoother(tmp[, 1], tmp[, 2], 
		  method = list(smooth = list(df.fact = df.fact[i])), 
		  bg.outliers = TRUE)
    
    lines(data.frame(tmp[, 1], tmp.out), type = "l", pch = 20, 
    cex = 0.5, col = i)
    }
}
    
mtext("C", cex = 2, side = 3, adj = 0, font = 2)
legend("topleft", c("D1, 1x", "D2, 1:10", "D3, 1:100"), col = c(1:3), 
pch = rep(20,3))
legend("bottomright", paste("df.fact : ", df.fact), pch = 20, lwd = 2, 
col = 1:length(df.fact))
    
plot(NA, NA, xlim = c(0,40), ylim = c(-0.05, 0.3), xlab = "Time [min]", 
ylab = "RFI", main = "Lowess")
f <- seq(0.01,0.2,0.04)
for (i in 1:length(f)) {
  for (j in c(2,4,6)) {
    tmp <- data.frame(na.omit(C85[xrange, j])/60, na.omit(C85[xrange, j + 1]))
    tmp.out <- smoother(tmp[, 1], tmp[, 2], method = list(lowess = list(f = f[i])), 
		  bg.outliers = TRUE)
    
    lines(data.frame(tmp[, 1], tmp.out), type = "l", pch = 20, cex = 0.5, 
    col = i)
    }
    }
    
    mtext("D", cex = 2, side = 3, adj = 0, font = 2)
    legend("topleft", c("D1, 1x", "D2, 1:10", "D3, 1:100"), col = c(1:3), 
    pch = rep(20,3))
    legend("bottomright", paste("f : ", f), pch = 20, lwd = 2, col = 1:length(f))
	
plot(NA, NA, xlim = c(0,40), ylim = c(-0.05, 0.3), xlab = "Time [min]", 
ylab = "RFI", main = "Friedman's\n''super smoother''")
span <- seq(0.01,0.05,0.01)
for (i in 1:length(span)) {
  for (j in c(2,4,6)) {
    tmp <- data.frame(na.omit(C85[xrange, j])/60, na.omit(C85[xrange, j + 1]))
    tmp.out <- smoother(tmp[, 1], tmp[, 2], method = list(supsmu = list(span = span[i])), 
	  bg.outliers = TRUE)
    
    lines(data.frame(tmp[, 1], tmp.out), type = "l", pch = 20, cex = 0.5, 
col = i)
    }
    }
    
    mtext("E", cex = 2, side = 3, adj = 0, font = 2)
    legend("topleft", c("D1, 1x", "D2, 1:10", "D3, 1:100"), col = c(1:3), 
pch = rep(20,3))
    legend("bottomright", paste("span : ", span), pch = 20, lwd = 2, col = 1:length(span))
	  
plot(NA, NA, xlim = c(0,40), ylim = c(-0.05, 0.3), xlab = "Time [min]", 
ylab = "RFI", main = "Savitzky-Golay")

for (j in c(2,4,6)) {
  tmp <- data.frame(na.omit(C85[xrange, j])/60, na.omit(C85[xrange, j + 1]))
  tmp.out <- smoother(tmp[, 1], tmp[, 2], method = list("savgol"), 
	bg.outliers = TRUE)
  
  lines(data.frame(tmp[, 1], tmp.out), type = "l", pch = 20, cex = 0.5, 
col = 1)
  }
  
  mtext("F", cex = 2, side = 3, adj = 0, font = 2)
  legend("bottomright", paste("/ : ", NULL), pch = 20, lwd = 2, col = 1:length(span))

Description

The capillary convective PCR (ccPCR) is a modified device of the ccPCR system proposed by Chou et al. 2011.

Usage

data(capillaryPCR)

Format

A data frame with 1844 observations on the following 10 variables.

t.121205

Elapsed time during amplification

ED.121205

a numeric vector

t.121128

Elapsed time during amplification

ED.121128

a numeric vector

t.121130.1

Elapsed time during amplification

ED.121130.1

a numeric vector

t.121130.2

Elapsed time during amplification

ED.121130.2

a numeric vector

t.121130.3

Elapsed time during amplification

ED.121130.3

a numeric vector

Details

Modified version of the capillary convective tube isothermal heater heater by Chou et al. 2011. As heating system a conventional block heat was used. On the top of the heating block, we placed for the uptake of the capillaries an aluminum block (8 mm height) in which four holes (3.2 mm diameter and 3.0 mm depth with round shaped bottom) were drilled. The capillaries are regular 100 micro L Roche LightCycler(R). These glass capillaries have a round shaped closed bottom (2.3 mm inner diameter and 3.2 mm outer diameter). An "ESE-Log" detector (QIAGEN Lake Constance) was used for the real time fluorescent measurements, which was mounted in a distance of 5-10 mm next to the capillary. The PCR was performed with SYBR(R) Green fluorescent intercalating dye. Thereof the ESE-Log has in one channel the excitation at 470 nm and the detection at 520 nm. The data was recorded by the FL Digital Software (QIAGEN Lake Constance) and the exported text based raw data.

Source

Ralf Himmelreich, IMM, Mainz, Germany

References

Chou, W., Chen, P., Miao Jr, M., Kuo, L., Yeh, S. and Chen, P. (2011). Rapid DNA amplification in a capillary tube by natural convection with a single isothermal heater. Biotech. 50, 52-57.

Examples

# First example
data(capillaryPCR)
plot(NA, NA, xlim = c(0,80), ylim = c(0,1300), xlab = "Time [min]", 
     ylab = "Voltage [micro V]", main = "ccPCR - Raw Data")

for (i in c(1,3,5,7)) {
  lines(capillaryPCR[, i], capillaryPCR[, i+1], type = "b", pch = 20) 
}

abline(h = 290, v = c(18, 23, 35))
legend(60,800, c("Run 1", "Run 2", "Run 3", "Control"), pch = 20, lwd = 2)
  
# Second example
par(mfrow = c(2,1))

type <- c("mova", "spline", "savgol")
plot(NA, NA, xlim = c(0,80), ylim = c(0,1100), xlab = "Time [min]", 
     ylab = "Voltage [micro V]", main = "ccPCR with mova, 
     spline and savgol")
for (i in 1:3) {
  for (j in c(1,3,5,7)) {
      tmp <- data.frame(na.omit(capillaryPCR[, j]), 
			na.omit(capillaryPCR[, j+1]))
      tmp.sm <- smoother(tmp[, 1], tmp[, 2], method = list(type[i]))
      lines(data.frame(tmp[, 1], tmp.sm), type = "b", pch = 20, cex = 0.5, 
	    col = i)
  }
}

abline(h = 200, v = c(17.5, 21.3, 32.9))
legend(0, 1000, c("mova", "spline", "savgol"), pch = 20, lwd = 2, 
	col = c(1:3))
  
plot(NA, NA, xlim = c(10,40), ylim = c(50,300), xlab = "Time [min]", 
     ylab = "Voltage [micro V]", main = "ccPCR with mova, 
     spline and savgol")
for (i in 1:3) {
  for (j in c(1,3,5,7)) {
      tmp <- data.frame(na.omit(capillaryPCR[, j]), 
			na.omit(capillaryPCR[, j+1]))
      tmp.sm <- smoother(tmp[, 1], tmp[, 2], method = list(type[i]))
      lines(data.frame(tmp[, 1], tmp.sm), type = "b", pch = 20, cex = 0.5, 
	    col = i)
  }
}
abline(h = 200, v = c(17.5, 21.3, 32.9))
legend(10, 300, c("mova", "spline", "savgol"), pch = 20, lwd = 2, 
	col = c(1:3))

par(mfrow = c(1,1))

# Third example
method <- c("lowess","mova","savgol","smooth","spline", "supsmu")

plot(NA, NA, xlim = c(0,100), ylim = c(-50,1100), xlab = "Time [min]", 
     ylab = "Voltage [micro V]", main = "capillary convective PCR")
for (i in 1:length(method)) {
  for (j in c(1,3,5,7)) {
      tmp <- data.frame(na.omit(capillaryPCR[, j]), 
			na.omit(capillaryPCR[, j+1]))
      tmp.sm <- smoother(tmp[, 1], tmp[, 2], method = list(method[i]))
      lines(data.frame(tmp[, 1], tmp.sm), type = "l", pch = 20, cex = 0.5, 
	    col = i)
  }
}
legend(0,1000, method, pch = 20, lwd = 2, col = 1:length(method))


par(fig = c(0.5,1,0.25,0.8), new = TRUE)
plot(NA, NA, xlim = c(10,40), ylim = c(50,300), xlab = "Time [min]", 
     ylab = "Voltage [micro V]", main = "")
for (i in 1:length(method)) {
  for (j in c(1,3,5,7)) {
      tmp <- data.frame(na.omit(capillaryPCR[, j]), 
			na.omit(capillaryPCR[, j+1]))
      tmp.sm <- smoother(tmp[, 1], tmp[, 2], method = list(method[i]))
      lines(data.frame(tmp[, 1], tmp.sm), type = "l", pch = 20, cex = 0.5, 
	    col = i)
  }
}
legend(0,1000, method, pch = 20, lwd = 2, col = 1:length(method))

# Fourth example
# Comparison of Lowess, Moving average and splines to smooth amplification 
# curve data of
# a capillary convective PCR.

plot(NA, NA, xlim = c(10,40), ylim = c(50, 300), xlab = "Time [min]", 
     ylab = "Voltage [micro V]", main = "ccPCR - Moving average")
movaww <- seq(1,17,4)
for (i in 1:length(movaww)) {
  for (j in c(1,3,5,7)) {
	    tmp <- data.frame(na.omit(capillaryPCR[, j]), 
			      na.omit(capillaryPCR[, j+1]))
	    tmp.out <- smoother(tmp[, 1], tmp[, 2], 
                 method = list(mova = list(movaww = movaww[i])))
	    
	    lines(data.frame(tmp[, 1], tmp.out), type = "l", pch = 20, 
		  cex = 0.5, col = i)
  }
}
text(10,300, "A)", cex = 3)
legend(25,200, paste("movaww : ", movaww), pch = 20, lwd = 2, 
	col = 1:length(movaww))

plot(NA, NA, xlim = c(10,40), ylim = c(50, 300), xlab = "Time [min]", 
     ylab = "Voltage [micro V]", main = "ccPCR - Cubic Spline")
df.fact <- seq(0.5,0.9,0.1)
for (i in 1:length(df.fact)) {
  for (j in c(1,3,5,7)) {
    tmp <- data.frame(na.omit(capillaryPCR[, j]), 
		      na.omit(capillaryPCR[, j+1]))
    tmp.out <- smoother(tmp[, 1], tmp[, 2], method = list(smooth = 
    list(df.fact = df.fact[i])))
   
    lines(data.frame(tmp[, 1], tmp.out), type = "l", pch = 20, 
		  cex = 0.5, col = i)
  }
}
text(10,300, "B)", cex = 3)
legend(30,200, paste("df.fact : ", df.fact), pch = 20, lwd = 2, 
	col = 1:length(df.fact))
	

plot(NA, NA, xlim = c(10,40), ylim = c(50, 300), xlab = "Time [min]", 
     ylab = "Voltage [micro V]", main = "ccPCR - Lowess")
f <- seq(0.01,0.2,0.04)
for (i in 1:length(f)) {
  for (j in c(1,3,5,7)) {
	    tmp <- data.frame(na.omit(capillaryPCR[, j]), 
			      na.omit(capillaryPCR[, j+1]))
	    tmp.out <- smoother(tmp[, 1], tmp[, 2], method = list(lowess = list(f = f[i])))
	    
	    lines(data.frame(tmp[, 1], tmp.out), type = "l", pch = 20, 
		  cex = 0.5, col = i)
  }
}
text(10,300, "C)", cex = 3)
legend(30,200,  paste("f : ", f), pch = 20, lwd = 2, col = 1:length(f))

plot(NA, NA, xlim = c(10,40), ylim = c(50, 300), xlab = "Time [min]", 
     ylab = "Voltage [micro V]", 
     main = "ccPCR - Friedman's ''super smoother''")
span <- seq(0.01,0.05,0.01)
for (i in 1:length(span)) {
  for (j in c(1,3,5,7)) {
	    tmp <- data.frame(na.omit(capillaryPCR[, j]), 
			      na.omit(capillaryPCR[, j+1]))
	    tmp.out <- smoother(tmp[, 1], tmp[, 2], 
                 method = list(supsmu = list(span = span[i])))
	    
	    lines(data.frame(tmp[, 1], tmp.out), type = "l", pch = 20, 
		  cex = 0.5, col = i)
  }
}
text(10,300, "D)", cex = 3)
legend(25,200,  paste("span : ", f), pch = 20, lwd = 2, col = 1:length(span))

par(mfrow = c(1,1), cex = 1)

Description

Quantitative PCR (qPCR) with a hydrolysis probe and DNA binding dye (EvaGreen) (Mao et al. 2007) was performed in the 'VideoScan' heating cooling unit. The cycle-dependent increase of the fluorescence was quantified at three different temperatures in order to estimate temperature dependent effects.

Usage

data(CD74)

Format

A data frame with 60 observations on the following 19 variables. refMFI (referenced Mean Fluorescence Intensity), fluorescence. Dilution A, 1x; Dilution B, 1:10; Dilution B, 1:100.

Cycle

PCR cycles

EG.30.A

refMFI at 30 degrees Celsius, EvaGreen, Dilution A

EG.59.5.A

refMFI at 59.5 degrees Celsius, EvaGreen, Dilution A

EG.68.5.A

refMFI at 68.5 degrees Celsius, EvaGreen, Dilution A

HP.30.A

refMFI at 30 degrees Celsius, hydrolysis probe, Dilution A

HP.59.5.A

refMFI at 59.5 degrees Celsius, hydrolysis probe, Dilution A

HP.68.5.A

refMFI at 68.5 degrees Celsius, hydrolysis probe, Dilution A

EG.30.B

refMFI at 30 degrees Celsius, EvaGreen, Dilution B

EG.59.5.B

refMFI at 59.5 degrees Celsius, EvaGreen, Dilution B

EG.68.5.B

refMFI at 68.5 degrees Celsius, EvaGreen, Dilution B

HP.30.B

refMFI at 30 degrees Celsius, hydrolysis probe, Dilution B

HP.59.5.B

refMFI at 59.5 degrees Celsius, hydrolysis probe, Dilution B

HP.68.5.B

refMFI at 68.5 degrees Celsius, hydrolysis probe, Dilution B

EG.30.C

refMFI at 30 degrees Celsius, EvaGreen, Dilution C

EG.59.5.C

refMFI at 59.5 degrees Celsius, EvaGreen, Dilution C

EG.68.5.C

refMFI at 68.5 degrees Celsius, EvaGreen, Dilution C

HP.30.C

refMFI at 30 degrees Celsius, hydrolysis probe, Dilution C

HP.59.5.C

refMFI at 59.5 degrees Celsius, hydrolysis probe, Dilution C

HP.68.5.C

refMFI at 68.5 degrees Celsius, hydrolysis probe, Dilution C

Details

The aim was to amplify MLC-2v in the 'VideoScan' platform while the intercalating dye EvaGreen and a hydrolysis probe for MLC-2v were used simultaneously. The primer sequences for MLC-2v were taken from Roediger et al. (2013). The amplification was detected in solution of the '1 HCU' (see Roediger et al. 2013 for details). A 20 micro L PCR reaction was composed of 500 nM primer (forward and reverse), 1x Maxima Probe qPCR Master Mix (Fermentas), 1 micro L template (MLC-2v amplification product in different dilutions), 50 nM hydrolysis probe probe for MLC-2v, 0.5 x EvaGreen and A. bidest. During the amplification, fluorescence was measured at 3 different temperatures, at 59.5 degrees Celsius the annealing temperature, at 68.5 degree Celsius the elongation temperature and at 30 degrees Celsius. The FAM channel was used to monitor EvaGreen and the Cy5 channel to monitor the MLC-2v specific hydrolysis probe.

Source

Claudia Deutschmann & Stefan Roediger, BTU Cottbus - Senftenberg, Senftenberg, Germany

References

A Highly Versatile Microscope Imaging Technology Platform for the Multiplex Real-Time Detection of Biomolecules and Autoimmune Antibodies. S. Roediger, P. Schierack, A. Boehm, J. Nitschke, I. Berger, U. Froemmel, C. Schmidt, M. Ruhland, I. Schimke, D. Roggenbuck, W. Lehmann and C. Schroeder. Advances in Biochemical Bioengineering/Biotechnology. 133:33–74, 2013.

Mao, F., Leung, W.-Y., Xin, X., 2007. Characterization of EvaGreen and the implication of its physicochemical properties for qPCR applications. BMC Biotechnol. 7, 76.

Examples

# First example
# Comparison of smoothers and filter on amplification curves
# Amplification curves were measured at three temperature (30, 
# 59.5, 68.5 degrees Celsius) using the 'VideoScan' 'HCU' (see 
# Roediger et al. 2013 for details). MLC-2v was amplified.
# The change of fluorescence was monitored by the intercalating
# dye EvaGreen and hydrolysis probes.

data(CD74)
par(mfrow = c(1,2))
plot(NA, NA, xlim = c(1,30), ylim = c(0,2), xlab = "Cycle", 
     ylab = "MFI", main = "VideoScan HCU\nRaw Data")
lim <- 1:30
for (j in c(2:4)) {
  for (i in seq(j,19,6)) {
    lines(CD74[lim, 1], CD74[lim, i], col = 1) 
  }
}
for (j in c(5:7)) {
  for (i in seq(j,19,6)) {
    lines(CD74[lim, 1], CD74[lim, i], col = 2) 
  }
}

plot(NA, NA, xlim = c(1,30), ylim = c(0,1.8), xlab = "Cycle", 
     ylab = "MFI", main = "VideoScan HCU\nSmoothed Data")
lim <- 1:30
for (j in c(2:4)) {
  for (i in seq(j,19,6)) {
    lines(CD74[lim, 1], smoother(CD74[lim, 1], CD74[lim, i], trans = TRUE), 
	  col = 1) 
  }
}
for (j in c(5:7)) {
  for (i in seq(j,19,6)) {
    lines(CD74[lim, 1], smoother(CD74[lim, 1], CD74[lim, i], trans = TRUE), 
	  col = 2) 
  }
}
par(mfrow = c(1,1))

Description

Helicase Dependent Amplification in the 'VideoScan' 'HCU' of HPRT1 (Homo sapiens hypoxanthine phosphoribosyltransferase 1)

Usage

data(CD75)

Format

A data frame with 93 observations on the following 6 variables. The data frame contains three replicates of a HDA for HPRT1.

CD75.t1

Elapsed time during HDA in seconds.

CD75.F1

Time-dependent fluorescence during HDA.

CD75.t2

Elapsed time during HDA in seconds.a numeric vector

CD75.F2

Time-dependent fluorescence during HDA

CD75.t3

Elapsed time during HDA in seconds.

CD75.F3

Time-dependent fluorescence during HDA.

Details

To perform an isothermal amplification in 'VideoScan', standard conditions for the IsoAmp(R) III Universal tHDA Kit (Biohelix) were used. The reaction was composed of 12.5 micro L buffer A containing 1.25 micro L 10x reaction buffer, 150 nM primer (forward and reverse), 0.75 micro L template (synthetic) and A. bidest which was covered with 50 micro L mineral oil. The primer sequences for HPRT1 were taken from Roediger et al. (2013). Preincubation: This mixture was incubated for 2 min at 95 degree. Celsius and immediately placed on ice. 12.5 micro L of reaction buffer B which was composed of 1.25 micro L 10x buffer, 40 mM NaCl, 5 mM MgSO4, 1.75 micro L dNTPs, 0.2 x EvaGreen, 1 micro L Enzyme mix and A. bidest. The fluorescence measurement in 'VideoScan' 'HCU' started directly after adding buffer B at 55, 60 or 65 degrees Celsius and revealed optimal conditions for the amplification when using 60 or 65 degree Celsius. Temperature profile (after Preincubation): - 60 seconds at 65 degrees Celsius - 11 seconds at 55 degrees Celsius && Measurement

Source

Claudia Deutschmann & Stefan Roediger, BTU Cottbus - Senftenberg, Senftenberg, Germany

References

A Highly Versatile Microscope Imaging Technology Platform for the Multiplex Real-Time Detection of Biomolecules and Autoimmune Antibodies. S. Roediger, P. Schierack, A. Boehm, J. Nitschke, I. Berger, U. Froemmel, C. Schmidt, M. Ruhland, I. Schimke, D. Roggenbuck, W. Lehmann and C. Schroeder. Advances in Biochemical Bioengineering/Biotechnology. 133:33–74, 2013.

Examples

data(CD75)
## maybe str(CD75) ; plot(CD75) ...

Description

The chipPCR package contains numerous data sets from commercial and experimental technologies for the amplification of nucleic acids. The data sets include results from qPCR experiments with the 'VideoScan' 'HCU' (Roediger et al. 2013), Bio-Rad (iQ5, CFX96), capillary convective PCR (ccPCR) and Roche Light Cycler 1.5. Real-time monitored amplification reactions were performed using standard amplification methods (qPCR, based on Taq polymerase) and quantitative isothermal amplification (qIA). In selected data sets are melting curves and dilution series available. Most of the data sets have equidistant measure points. However, some datasets have none homogeneous measure points as indicated below.

capillary convective PCR (ccPCR)

capillaryPCR: The capillary convective PCR (ccPCR) is a modified device of the ccPCR system proposed by Chou et al. 2013.

standard qPCR - commercial thermo cyclers

C60.amp: qPCR Experiment for the Amplification of MLC-2v and Vimentin (as decadic dilutions) Using the Roche Light Cycler 1.5.

C60.melt: Melt Curves MLC-2v and Vimentin for the qPCR experiment C60.amp using the Roche Light Cycler 1.5

C126EG595: A quantitative PCR (qPCR) with the DNA binding dye (EvaGreen) (Mao et al. 2007) was performed in a Bio-Rad iQ5 thermo cycler. The cycle-dependent increase of the fluorescence was quantified at the elongation step (59.5 degrees Celsius).

C126EG685: A quantitative PCR (qPCR) with the DNA binding dye (EvaGreen) (Mao et al. 2007) was performed in a Bio-Rad iQ5 thermo cycler. The cycle-dependent increase of the fluorescence was quantified at the elongation step (68.5 degrees Celsius).

C127EGHP: Quantitative PCR (qPCR) with a hydrolysis probe (Cy5/BHQ2) and DNA binding dye (EvaGreen) (Mao et al. 2007) performed in the Roche Light Cycler 1.5 thermo cycler.

VIMCFX96_60: Human vimentin amplification curve data (measured during annealing phase at 60 degrees Celsius) for 96 replicate samples in a Bio-Rad CFX96 thermo cycler.

VIMCFX96_69: Human vimentin amplification curve data (measured during elongation phase at 69 degrees Celsius) for 96 replicate samples in a Bio-Rad CFX96 thermo cycler.

VIMCFX96_meltcurve: Human vimentin melting curve data for 96 replicate samples in a Bio-Rad CFX96 thermo cycler.

VIMiQ5_595: Human vimentin amplification curve data (measured during annealing phase at 59.5 degrees Celsius) for 96 replicate samples in a Bio-Rad iQ5 thermo cycler.

VIMiQ5_685: Human vimentin amplification curve data (measured during elongation phase at 68.5 degrees Celsius) for 96 replicate samples in a Bio-Rad iQ5 thermo cycler.

VIMiQ5_melt: Human vimentin melting curve data for 96 replicate samples in a Bio-Rad iQ5 thermo cycler.

standard qPCR - experimental thermo cyclers

C54: qPCR Experiment in the 'VideoScan' heating/cooling-unit for the amplification using different concentrations of MLC-2v input cDNA quantities.

CD74: Quantitative PCR with a hydrolysis probe and DNA binding dye (EvaGreen) for MLC-2v measured at 59.5 degrees Celsius (annealing temperature), 68.5 degrees Celsius (elongation temperature) and at 30 degrees Celsius.

Simulations

Eff625: Highly replicate number amplification curves with an approximate amplification efficiency of 62.5 percent at cycle number 18. The data were derived from a simulation such as the AmpSim function.

Eff750: Highly replicate number amplification curves with an approximate amplification efficiency of 75 percent at cycle number 18. The data were derived from a simulation such as the AmpSim function.

Eff875: Highly replicate number amplification curves with an approximate amplification efficiency of 87.5 percent at cycle number 18. The data were derived from a simulation such as the AmpSim function.

Eff1000: Highly replicate number amplification curves with an approximate amplification efficiency of 100 percent at cycle number 18. The data were derived from a simulation such as the AmpSim function.

Isothermal Amplification - Helicase Dependent Amplification

C67: A Helicase Dependent Amplification (HDA) of HPRT1 (Homo sapiens hypoxanthine phosphoribosyltransferase 1), performed at different input DNA quantities using the Bio-Rad iQ5 thermo cycler.

CD75: Helicase Dependent Amplification in the 'VideoScan' 'HCU' of HPRT1 (Homo sapiens hypoxanthine phosphoribosyltransferase 1) measured at at 55, 60 or 65 degrees Celsius.

C81: Helicase Dependent Amplification (HDA) of pCNG1 using the 'VideoScan' Platform (Roediger et al. (2013)). The HDA was performed at 65 degree Celsius. Two concentrations of input DNA were used.

C85: Helicase Dependent Amplification (HDA) of Vimentin (Vim) in the 'VideoScan' Platform (Roediger et al. (2013)). The HDA was performed at 65 degree Celsius with three dilutions of input DNA.

Source

Stefan Roediger

References

A Highly Versatile Microscope Imaging Technology Platform for the Multiplex Real-Time Detection of Biomolecules and Autoimmune Antibodies. S. Roediger, P. Schierack, A. Boehm, J. Nitschke, I. Berger, U. Froemmel, C. Schmidt, M. Ruhland, I. Schimke, D. Roggenbuck, W. Lehmann and C. Schroeder. Advances in Biochemical Bioengineering/Biotechnology. 133:33–74, 2013.

Examples

data(VIMiQ5_melt)

tmp <- VIMiQ5_melt

plot(NA, NA, xlim = c(55,95), ylim = c(0, 40000), 
    xlab = "Temperature (degrees Celsius)",ylab = "RFU", 
    main = "Melting curve in iQ5 (Bio-Rad)")
apply(tmp[, 2:ncol(tmp)], 2, 
      function(x) lines(tmp[1:nrow(tmp),1],x))

Fmean <- rowMeans(tmp[, 2:ncol(tmp)])
lines(tmp[1:nrow(tmp),1], Fmean, col = "red", lwd = 3)

legend(55, 4000, c("Raw", "Mean"), pch = c(19,19), col = c(1,2))

Description

CPP encompasses a set of functions to pre-process an amplification curve. The pre-processing includes options to normalize curve data, to remove background, to remove outliers in the background range and to test if an amplification is significant.

Usage

## S4 method for signature 'numeric,numeric'
CPP(x, y, smoother = TRUE, method = "savgol", 
				trans = FALSE, method.reg = "lmrob", 
				bg.outliers = FALSE, median = FALSE, 
				method.norm = "none", qnL = 0.03, amptest = FALSE, 
				manual = FALSE, nl = 0.08, bg.range = NULL, ...)

## S4 method for signature 'matrix,missing'
CPP(x, y, smoother = TRUE, method = "savgol", 
			       trans = FALSE, method.reg = "lmrob", 
			       bg.outliers = FALSE, median = FALSE, 
			       method.norm = "none", qnL = 0.03, amptest = FALSE, 
			       manual = FALSE, nl = 0.08, bg.range = NULL, ...)

## S4 method for signature 'data.frame,missing'
CPP(x, y, smoother = TRUE, 
				   method = "savgol", trans = FALSE, 
				   method.reg = "lmrob", bg.outliers = FALSE, 
				   median = FALSE, method.norm = "none", 
				   qnL = 0.03, amptest = FALSE, 
				   manual = FALSE, nl = 0.08, bg.range = NULL, ...)

Arguments

Details

CPP uses the bg.max function to estimate automatically the start of the amplification process. In the background range there is often noise which makes it harder to determine a meaningful background value. Therefore CPP can optionally remove outliers by finding the value with largest difference from the mean as provided by the rm.outlier function. This function also tries to prevent calculations of non amplified signals.

The slope of the background range is often unequal to zero. By setting the parameter trans it is possible to apply a simple correction of the slope. Thereby either a robust linear regression by computing MM-type regression estimators, a nonparametric rank-based estimator or a standard linear regression model. Care is needed when using trans with time series (see lm for details).

Author(s)

Stefan Roediger, Michal Burdukiewicz

See Also

Normalization: normalizer

Smoothing: smoother

Robust linear regression: lm.coefs

Examples

# Function to pre-process an amplification curve.
# Take a subset of the C17 data frame.
data(C17)
par(mfrow = c(2,1))
plot(NA, NA, xlab = "Time [sec]", ylab = "refMFI", 
     main = "HDA Raw Data", 
     xlim = c(0, 2500), ylim = c(0,1.1), pch = 20)
for (i in 3:5) {
  lines(C17[1:50, 1], C17[1:50, i], col = i - 2, 
	type = "b", pch = 20)
}

legend(50, 0.5, c("55 degrees Celsius", "60 degrees Celsius", "65 degrees Celsius"), 
	col = c(1,2,3), pch = rep(20,3))

# Use CPP to pre-process the data by removing the missing value and 
# normalization of the data
plot(NA, NA, xlab = "Time [sec]", ylab = "refMFI", 
     main = "Curve Pre-processor Applied to HDA Data", 
     xlim = c(0, 2500), ylim = c(0,1.1), pch = 20)
for (i in 3:5) {
  y.cpp <- CPP(C17[2:50, 1], C17[2:50, i], method.norm = "minm", 
	      bg.outliers = TRUE)$y.norm
  lines(C17[2:50, 1], y.cpp, col = i - 2, 
	type = "b", pch = 20)
}
legend(50, 1, c("55 degrees Celsius", "60 degrees Celsius", "65 degrees Celsius"), 
	col = c(1,2,3), pch = rep(20,3))
par(mfrow = c(1,1))

Description

An S4 class containing the output inder function.

Slots

.Data:

"matrix" is a matrix containing smoothed data as well as the first and second derivative.

method:

"character" used method of smoothing.

Methods

summary

signature(object = "der"): calculates and prints approximate first derivative maximum, second derivative maximum, second derivative minimum and second derivative center. See summary.der.

show

signature(object = "der"): prints only .Data slot of the object.

Author(s)

Stefan Roediger, Michal Burdukiewicz

See Also

inder

Examples

pcr <- AmpSim(cyc = 1:40)
res <- inder(pcr[, 1], pcr[, 2])
sums <- summary(res)
print(sums)

Description

An S4 class containing the output effcalc function.

Slots

.Data:

"matrix" containing the "Concentration", "Location" (mean, median), "Deviation" (standard deviation, median absolute deviation), "Coefficient of Variance" (CV, RSD) sequential in the columns.

amplification.efficiency:

"numeric" value representing amplification efficiency.

regression:

"lm" the results of the linear regression and .

correlation.test:

"htest". the correlation test (Pearson) results.

Methods

plot

signature(x = "eff"): plots calculated efficiency. See plot.eff

show

signature(object = "eff"): prints only .Data slot of the object.

summary

signature(object = "eff"): prints information about object prettier than show.

Author(s)

Stefan Roediger, Michal Burdukiewicz

See Also

effcalc, plot.eff,

Description

Highly replicate number amplification curves with an approximate amplification efficiency of 100 percent at cycle number 18. The data were derived from a simulation such as the AmpSim function.

Usage

data(Eff1000)

Format

A data frame with 40 (Cycles) observations on the following 1000 (amplification curves) variables. The columns are all replicates.

Examples

data(Eff1000)

plot(NA, NA, xlim = c(1,40), ylim = c(0,max(Eff1000)), xlab = "Cycle",
    ylab = "RFU", 
    main = "Amplification Curves with 100 Percent Efficiency")
apply(Eff1000[, 1:ncol(Eff1000)], 2, function(x) lines(1:40,x))

Fmean <- rowMeans(Eff1000[, 1:ncol(Eff1000)])
lines(1:40, Fmean, col = "red", lwd = 3)

legend(1, quantile(unlist(Eff1000), 0.9), c("Raw", "Mean"), 
	pch = c(19,19), col = c(1,2))

Description

Highly replicate number amplification curves with an approximate amplification efficiency of 62.5 percent at cycle number 18. The data were derived from a simulation such as the AmpSim function.

Usage

data(Eff625)

Format

A data frame with 40 (Cycles) observations on the following 1000 (amplification curves) variables. The columns are all replicates.

Examples

data(Eff625)

plot(NA, NA, xlim = c(1,40), ylim = c(0,max(Eff625)), xlab = "Cycle",
    ylab = "RFU", 
    main = "Amplification Curves with 62.5 Percent Efficiency")
apply(Eff625[, 1:ncol(Eff625)], 2, function(x) lines(1:40,x))

Fmean <- rowMeans(Eff625[, 1:ncol(Eff625)])
lines(1:40, Fmean, col = "red", lwd = 3)

legend(1, quantile(unlist(Eff625), 0.9), c("Raw", "Mean"), 
      pch = c(19,19), col = c(1,2))