# First Example
# Plot the first derivative of different samples for single melting curve
# data. Note that the argument "plot" is TRUE.
data(MultiMelt)
par(mfrow = c(1,2))
for (i in c(2,14)) {
tmp <- mcaSmoother(MultiMelt[, 1], MultiMelt[, i])
diffQ(tmp, plot = TRUE)
}
par(mfrow = c(1,1))
# Second example
# Plot the first derivative of different samples from MultiMelt
# in a single plot.
data(MultiMelt)
# First create an empty plot
plot(NA, NA, xlab= "Temperature", ylab="-d(refMFI)/d(T)",
main="Multiple melting peaks in a single plot", xlim=c(65,85),
ylim=c(-0.4,0.01), pch=19, cex=1.8)
# Prepossess the selected melting curve data (2,6,12) with mcaSmoother
# and apply them to diffQ. Note that the argument "plot" is FALSE
# while other arguments like derivlimitsline or peak are TRUE.
for (i in c(2,6,12)) {
tmp <- mcaSmoother(MultiMelt[, 1], MultiMelt[, i],
bg = c(41,61), bgadj = TRUE)
diffQ(tmp, plot = FALSE, derivlimitsline = TRUE, deriv = TRUE,
peak = TRUE, derivlimits = TRUE, col = i, vertiline = TRUE)
}
legend(65, -0.1, colnames(MultiMelt[, c(2,6,12)]), pch = c(15,15,15),
col = c(2,6,12))
# Third example
# First create an empty plot
plot(NA, NA, xlim = c(50,85), ylim = c(-0.4,2.5),
xlab = "Temperature",
ylab ="-refMFI(T) | refMFI'(T) | refMFI''(T)",
main = "1st and 2nd Derivatives",
pch = 19, cex = 1.8)
# Prepossess the selected melting curve data with mcaSmoother
# and apply them to diffQ and diffQ2. Note that
# the argument "plot" is FALSE while other
# arguments like derivlimitsline or peak are TRUE.
tmp <- mcaSmoother(MultiMelt[, 1], MultiMelt[, 2],
bg = c(41,61), bgadj = TRUE)
lines(tmp, col= 1, lwd = 2)
# Note the different use of the argument derivlimits in diffQ and diffQ2
diffQ(tmp, fct = min, derivlimitsline = TRUE, deriv = TRUE,
peak = TRUE, derivlimits = FALSE, col = 2, vertiline = TRUE)
diffQ2(tmp, fct = min, derivlimitsline = TRUE, deriv = TRUE,
peak = TRUE, derivlimits = TRUE, col = 3, vertiline = TRUE)
# Add a legend to the plot
legend(65, 1.5, c("Melting curve",
"1st Derivative",
"2nd Derivative"),
pch = c(19,19,19), col = c(1,2,3))
# Fourth example
# Different curves may potentially have different quality in practice.
# For example, using data from MultiMelt should return a
# valid result and plot.
data(MultiMelt)
diffQ(cbind(MultiMelt[, 1], MultiMelt[, 2]), plot = TRUE)$Tm
# limits_xQ
# 77.88139
# Imagine an experiment that went terribly wrong. You would
# still get an estimate for the Tm. The output from diffQ,
# with an error attached, lets the user know that this Tm
# is potentially meaningless. diffQ() will give several
# warning messages.
set.seed(1)
y = rnorm(55,1.5,.8)
diffQ(cbind(MultiMelt[, 1],y), plot = TRUE)$Tm
# The distribution of the curve data indicates noise.
# The data should be visually inspected with a plot
# (see examples of diffQ). The Tm calculation (fit,
# adj. R squared ~ 0.157, NRMSE ~ 0.279) is not optimal
# presumably due to noisy data. Check raw melting
# curve (see examples of diffQ).
# Calculated Tm
# 56.16755
# Sixth example
# Curves may potentially have a low temperature resolution. The rsm
# parameter can be used to double the temperature resolution.
# Use data from MultiMelt column 15 (MLC2v2).
data(MultiMelt)
tmp <- cbind(MultiMelt[, 1], MultiMelt[, 15])
# Use diffQ without and with the rsm parameter and plot
# the results in a single row
par(mfrow = c(1,2))
diffQ(tmp, plot = TRUE)$Tm
text(60, -0.15, "without rsm parameter")
diffQ(tmp, plot = TRUE, rsm = TRUE)$Tm
text(60, -0.15, "with rsm parameter")
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