th.cyc {chipPCR}

Threshold Cycle

Description

th.cyc calculates the number of cycles at which the fluorescence exceeds a defined threshold, called the threshold cycle (Ct). According to the MIQE guidelines the Ct is referred to as quantification cycle (Cq). The calculated Cq is a relative value, which depends on the template copy number, instrument, reagents, amplification efficiency and probe technology. Low Cqs correlate with high quantities template copy numbers. Real-time technologies enable the quantification of nucleic acids by calculation of specific curve parameters like the quantification point (Cq) and the amplification efficiency (AE) based on the kinetics of the amplification curve. The Cq represents the number of cycles (time for qIA) needed to reach a defined fluorescence signal level in the exponential phase of the amplification curve. The Cq can be determined from a fixed threshold value or by various analytical algorithm as described elsewhere (Bustin et al. 2009, Ruijter et al. 2013, Tellinghuisen et al. 2014).

Usage

th.cyc(x, y, r = 2500, auto = FALSE, linear = TRUE)

Arguments

x

is a vector containing the time or cycle values.

y

is a vector containing the fluorescence values.

r

a fluorescence value which defines the threshold.

auto

is logical parameter which indicates if an automatic estimation of the threshold should be used (Note: Experimental, not safe to use).

linear

is logical parameter which indicates if a linear or quadratic regression should be used for the calculation. (Note: Experimental, not safe to use).

Details

The Threshold Cycle (Ct) (Cq according to MIQE, see Bustin et al. 2009) is the cycle number at which the fluorescence exceeds significantly a point above the baseline and defined threshold in a particular samples. Thus the Ct is the cycle when sufficient numbers of amplicons have accumulated. The th.cyc calculates the intersection of the user defined Ct value (r) and a linear regression or quadratic polynomial in the range of the user defined Ct value. In contrast to other methods, th.cyc have no requirement to fit a "complex" non linear model to the entire data set but rather focuses on the specific area. The polynomial is calculated from four neighbor values at the fluorescence threshold.

Value

An object of class th.

Author(s)

Stefan Roediger, Michal Burdukiewicz

References

Stephen A. Bustin, Vladimir Benes, Jeremy A. Garson, Jan Hellemans, Jim Huggett, Mikael Kubista, Reinhold Mueller, Tania Nolan, Michael W. Pfaffl, Gregory L. Shipley, Jo Vandesompele, and Carl T. Wittwer. (Apr 2009). "The MIQE Guidelines: Minimum Information for Publication of Quantitative Real-Time PCR Experiments". Clin Chem. 55 (4):611–22. doi:10.1373/clinchem.2008.112797. PMID 19246619

Ruijter, J.M., Pfaffl, M.W., Zhao, S., Spiess, A.N., Boggy, G., Blom, J., Rutledge, R.G., Sisti, D., Lievens, A., De Preter, K., Derveaux, S., Hellemans, J., Vandesompele, J.: Evaluation of qPCR curve analysis methods for reliable biomarker discovery: bias, resolution, precision, and implications. Methods (San Diego, Calif.) 59(1), 32–46 (2013). doi:10.1016/j.ymeth.2012.08.011. PMID: 22975077

Tellinghuisen, J., Spiess, A.-N.: Comparing real-time quantitative polymerase chain reaction analysis methods for precision, linearity, and accuracy of estimating amplification efficiency. Analytical Biochemistry 449, 76–82 (2014). doi:10.1016/j.ab.2013.12.020. PMID: 24365068

Examples

# First example
# Raw data from the VIMCFX96_69 data set.
# Cycles
x <- VIMCFX96_69[, 1]
# Fluoresce values
y <- VIMCFX96_69[, 2]

# Plot the raw data
plot(x, y, xlab = "Cycle", ylab = "Fluo")
# Calculate the the Ct value
res <- th.cyc(x, y, r = 2300)
lines(res@input, col = 2, lwd = 2)
# Threshold fluorescence value
abline(h = res@.Data[2], col = 3)
# Calculated Ct value
abline(v = res@.Data[1], col = 4)

# Second example
# Application of the th.cyc method to determine the Cq from a continous
# amplification reaction.
plot(NA, NA, xlim = c(0,80), ylim = c(0,1200), xlab = "Time [min]", 
     ylab = "Voltage [micro V]", main = "ccPCR - Raw Data")

# Threshold level "r" (50 micro Volts)
for (i in c(1,3,5,7)) {
  y.tmp <- capillaryPCR[, i + 1] - mean(capillaryPCR[1L:150, i + 1])
  Ct.tmp <- th.cyc(capillaryPCR[, i], y.tmp, r = 50, linear = FALSE)
  abline(v = Ct.tmp[1])
  text(Ct.tmp[1] * 1.1, 1200, paste(round(Ct.tmp[1], 1), "\nmin"))
  lines(capillaryPCR[, i], y.tmp, type = "b", pch = 20 - i) 
  points(Ct.tmp@input, col = "red", pch = 19)
}
abline(h = 50)
legend(5,800, c("Run 1", "Run 2", "Run 3", "Control"), pch = c(19, 17, 15, 13), 
      lwd = 1.5)