chipPCR (version 0.0.8-10)

effcalc: Analysis of the amplification efficiency

Description

effcalc calculates the amplification efficiency of a dilution.

Usage

"effcalc"(x, y, logx = TRUE, RSD = FALSE, rob = FALSE, level = 0.95)
"effcalc"(x, y, logx = TRUE, RSD = FALSE, rob = FALSE, level = 0.95)
"effcalc"(x, y, logx = TRUE, RSD = FALSE, rob = FALSE, level = 0.95)

Arguments

x

is the column of a data frame for the concentration (dilution).

y

are multiple columns of Cq values from a data.frame (e.g., [, c(1:n)]).

logx

is a logical parameter used to convert the concentration into a decadic logarithm.

RSD

Setting the option RSD = TRUE shows the relative standard deviation (RSD) in percent.

rob

Using the option rob as TRUE the median and the median absolute deviation (MAD) is plotted instead of the mean and standard deviation.

level

Tolerance/confidence level.

Value

an object of the class eff.

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. http://www.ncbi.nlm.nih.gov/pubmed/22437246

S. Mehra and W.-S. Hu. A kinetic model of quantitative real-time polymerase chain reaction. Biotechnology and bioengineering, 91(7):848--860, Sept. 2005. ISSN 0006-3592. doi: 10.1002/bit.20555. PMID: 15986490.

M. Guescini, D. Sisti, M. B. Rocchi, L. Stocchi, and V. Stocchi. A new real-time PCR method to overcome significant quantitative inaccuracy due to slight amplification inhibition. BMC Bioinformatics, 9(1):326, July 2008. ISSN 1471-2105. doi: 10.1186/1471-2105-9-326. PMID: 18667053.

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.

A. Staalberg, P. Aman, B. Ridell, P. Mostad, and M. Kubista. Quantitative real-time PCR method for detection of b-lymphocyte monoclonality by comparison of kappa and lambda immunoglobulin light chain expression. Clinical Chemistry, 49(1):51--59, Jan. 2003. ISSN 0009-9147. PMID: 12507960.

W. Liu and D. A. Saint. A new quantitative method of real time reverse transcription polymerase chain reaction assay based on simulation of polymerase chain reaction kinetics. Analytical Biochemistry, 302(1):52--59, Mar. 2002. ISSN 0003-2697. doi: 10.1006/abio.2001.5530. PMID: 11846375.

See Also

eff

plot.eff

Examples

## Not run: 
# First Example
# Amplification efficiency plot 

require(MBmca)
par(mfrow = c(1,2), las = 0, bty = "n", cex.axis = 1.2, cex.lab = 1.2, 
    font = 2, cex.main = 1, oma = c(0.1, 0.1, 0.5, 0.1))

# Simulate a qPCR reaction with AmpSim for 45 cycles and some noise.
# Define number of cycles
cyc.no <- 45

# Create an empty plot
plot(NA, NA, xlim = c(1,cyc.no), ylim = c(0.01,1.1), xlab = "Cycles", 
     ylab = "Fluorescence", main = "")
mtext("A", cex = 2, side = 3, adj = 0, font = 2)

# Create a sequence of "whised" Cq value for the simulation
cycle <- rep(seq(15, 34, 3.5), 3)

# Define the levels for the decadic dilution with concentrations
# from 100 to 0.001 (six steps).
# The in-slico experiment is designed to have three replicates at
# six dilution steps.
dilution <-rep(c(100, 10, 1, 0.1, 0.01, 0.001), 3)

# Create an empty matrix for the results of the concentration
# dependent Cq values
ma.out <- matrix(data = NA, nrow = cyc.no, 
		 ncol = length(cycle))

# Use AmpSim to simulate amplification curves at different 
# concentrations. The simulation is performed with the addition
# of some noise. This will do a generation of unique amplification
# curves, even under idential paramter settings. Calculate the 
# pseudo Second Derivative Maximum (SDM) (Cq) by using the the 
# diffQ2 function from the MBmca package.

Cq.out <- vector()

for (i in 1:18) {
  ma.out[1:cyc.no, i] <- AmpSim(cyc = c(1:cyc.no), b.eff = -50, bl = 0.001, 
				ampl = 1, Cq = cycle[i], noise = TRUE, 
				nnl = 0.02)[, 2]
  lines(1:cyc.no, ma.out[, i])
  tmpP <- mcaSmoother(1:cyc.no, ma.out[, i])
  #TURNED OFF INDER - NEW COMPATIBILITY TO MBmca STILL PENDING
  Cq.tmp <- diffQ2(tmpP, inder = FALSE)$xTm1.2.D2[1]
  abline(v = Cq.tmp)
  Cq.out <- c(Cq.out, Cq.tmp)
}

# Assign the calculated Cqs to the corresponding concentrations
tmp <- data.frame(dilution[1:6],
		  Cq.out[1:6],
		  Cq.out[7:12],
		  Cq.out[13:18])
		  
# Determine the amplification efficiency by using the effcalc function
plot(effcalc(tmp[, 1], tmp[, 2:4]), CI = TRUE)
mtext("B", cex = 2, side = 3, adj = 0, font = 2) 
par(mfrow = c(1,1))
## End(Not run)