capillaryPCR: capillary convective PCR

In chipPCR: Toolkit of Helper Functions to Pre-Process Amplification Data

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

default.par <- par(no.readonly = TRUE)
# 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(default.par)

chipPCR documentation built on March 5, 2021, 9:06 a.m.