mcaSmoother: Function to pre-process melting curve data.
In MBmca: Nucleic Acid Melting Curve Analysis
Description
Usage
Arguments
Value
Author(s)
References
See Also
Examples
Description
The function mcaSmoother() is used for data preprocessing.
Measurements from experimental systems may occasionally include missing
values (NA). mcaSmoother() uses approx() to fill up NAs under
the assumption that all measurements were equidistant. The original data
remain unchanged and only the NAs are substituted. Following it calls
smooth.spline() to smooth the curve. Different strengths can be set
using the option df.fact (f default~0.95). Internally it takes the
degree of freedom value from the spline and multiplies it with a factor
between 0.6 and 1.1. Values lower than 1 result in stronger smoothed curves.
The outcome of the differentiation depends on the temperature resolution of
the melting curve. It is recommended to use a temperature resolution of at
least 0.5 degree Celsius. Besides, for the temperature steps equal distances
60 degree Celsius) rather than unequal distances (e.g., 50 -> 50.4 -> 60.1
(e.g., 50 -> 50.5 -> degree Celsius) are recommended. The parameter
n can be used to increase the temperature resolution of the melting
curve data. mcaSmoother uses the spline function for this purpose. A
temperature range for a simple linear background correction. The linear
trend is estimated by a robust linear regression using lmrob(). In
case criteria for a robust linear regression are violated lm() is
automatically used. The parameter n can be combined with the
parameter Trange to make transform all melting curves of question to
have the #same range and similar resolution. Optionally a Min-Max
normalization between 0 and 1 can be used by setting the option
minmax to TRUE. This is useful in many situations. For
example, if the fluorescence values between samples vary considerably (e.g.,
due to high background, different reporter dyes, ...), particularly in
solution or for better comparison of results.
Usage
1
2
3
4
5
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8
9
10
Arguments
x
is the column of a data frame for the temperature.
y
is the column of a data frame for the fluorescence values.
bgadj
is used to adjust the background signal. This causes
mcaSmoother to use the data subset defined by bg for the
linear regression and background correction.
bg
is used to define the range for the background reduction (e.g.,
bg = c(50,55), between 50 and 55 degree Celsius)).
Trange
is used to define the temperature range (e.g., Trange =
c(50,95), between 50 and 95 degree Celsius) for melting curve analysis.
minmax
is used to scale the fluorescence a Min-Max normalization
between 0 and 1 can be used by setting the option minmax to
TRUE.
df.fact
is a factor to smooth the curve. Different strengths can be
set using the option df.fact (f default ~ 0.95). Internally it takes
the degree of freedom value from the spline and multiplies it with a factor
between 0.6 and 1.1. Values lower than 1 result in stronger smoothed curves.
n
is number of interpolations to take place. This parameter uses the
spline function and increases the temperature resolution of the melting
curve data.
Value
xy
returns a data.frame with the temperature ("x") in
the first and the preprocessed fluorescence values ("y.sp") in the second
column.
Author(s)
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. https://pubmed.ncbi.nlm.nih.gov/22437246/
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.
See Also
MFIerror, lmrob,
smooth.spline, spline, lm,
approx
Examples
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default.par <- par(no.readonly = TRUE)
# First Example
# Use mcaSmoother with different n to increase the temperature
# resolution of the melting curve artificially. Compare the
# influence of the n on the Tm and fluoTm values
data(MultiMelt)
Tm <- vector()
fluo <- vector()
for (i in seq(1,3.5,0.5)) {
res.smooth <- mcaSmoother(MultiMelt[, 1], MultiMelt[, 14], n = i)
res <- diffQ(res.smooth)
Tm <- c(Tm, res$Tm)
fluo <- c(fluo, res$fluoTm)
}
plot(fluo, Tm, ylim = c(76,76.2))
abline(h = mean(Tm))
text(fluo, seq(76.1,76.05,-0.02),
paste("n:", seq(3.5,1,-0.5), sep = " "), col = 2)
abline(h = c(mean(Tm) + sd(Tm), mean(Tm) - sd(Tm)), col = 2)
legend(-0.22, 76.2, c("mean Tm", "mean Tm +/- SD Tm"),
col = c(1,2), lwd = 2)
# Second Example
# Use mcaSmoother with different strengths of smoothing
# (f, 0.6 = strongest, 1 = weakest).
data(DMP)
plot(DMP[, 1], DMP[,6],
xlim = c(20,95), xlab = "Temperature",
ylab = "refMFI", pch = 19, col = 8)
f <- c(0.6, 0.8, 1.0)
for (i in c(1:3)) {
lines(mcaSmoother(DMP[, 1],
DMP[,6], df.fact = f[i]),
col = i, lwd = 2)
}
legend(20, 1.5, paste("f", f, sep = ": "),
cex = 1.2, col = 1:3, bty = "n",
lty = 1, lwd = 4)
# Third Example
# Plot the smoothed and trimmed melting curve
data(MultiMelt)
tmp <- mcaSmoother(MultiMelt[, 1], MultiMelt[, 14])
tmp.trimmed <- mcaSmoother(MultiMelt[, 1], MultiMelt[, 14],
Trange = c(49,85))
plot(tmp, pch = 19, xlab = "Temperature", ylab = "refMFI",
main = "MLC-2v, mcaSmoother using Trange")
points(tmp.trimmed, col = 2, type = "b", pch = 19)
legend(50, 1, c("smoothed values",
"trimmed smoothed values"),
pch = c(19,19), col = c(1,2))
# Fourth Example
# Use mcaSmoother with different n to increase the temperature
# resolution of the melting curve. Caution, this operation may
# affect your data negatively if the resolution is set to high.
# Higher resolutions will just give the impression of better
# data quality. res.st uses the default resolution (no
# alteration)
# res.high uses the double resolution.
data(MultiMelt)
res.st <- mcaSmoother(MultiMelt[, 1], MultiMelt[, 14])
res.high <- mcaSmoother(MultiMelt[, 1], MultiMelt[, 14], n = 2)
par(fig = c(0,1,0.5,1))
plot(res.st, xlab = "Temperature", ylab = "F",
main = "Effect of n parameter on the temperature
resolution")
points(res.high, col = 2, pch = 2)
legend(50, 1, c(paste("default resolution.", nrow(res.st),
"Temperature steps", sep = " "),
paste("double resolution.", nrow(res.high),
"Temperature steps", sep = " ")),
pch = c(1,2), col = c(1,2))
par(fig = c(0,0.5,0,0.5), new = TRUE)
diffQ(res.st, plot = TRUE)
text(65, 0.025, paste("default resolution.", nrow(res.st),
"Temperature steps", sep = " "))
par(fig = c(0.5,1,0,0.5), new = TRUE)
diffQ(res.high, plot = TRUE)
text(65, 0.025, paste("double resolution.", nrow(res.high),
"Temperature steps", sep = " "))
# Fifth example
# Different experiments may have different temperature
# resolutions and temperature ranges. The example uses a
# simulated melting curve with a temperature resolution of
# 0.5 and 1 degree Celsius and a temperature range of
# 35 to 95 degree Celsius.
#
# Coefficients of a 3 parameter sigmoid model. Note:
# The off-set, temperature range and temperature resolution
# differ between both simulations. However, the melting
# temperatures should be very
# similar finally.
b <- -0.5; e <- 77
# Simulate first melting curve with a temperature
# between 35 - 95 degree Celsius and 1 degree Celsius
# per step temperature resolution.
t1 <- seq(35, 95, 1)
f1 <- 0.3 + 4 / (1 + exp(b * (t1 - e)))
# Simulate second melting curve with a temperature
# between 41.5 - 92.1 degree Celsius and 0.5 degree Celsius
# per step temperature resolution.
t2 <- seq(41.5, 92.1, 0.5)
f2 <- 0.2 + 2 / (1 + exp(b * (t2 - e)))
# Plot both simulated melting curves
plot(t1, f1, pch = 15, ylab = "MFI",
main = "Simulated Melting Curves",
xlab = "Temperature", col = 1)
points(t2, f2, pch = 19, col = 2)
legend(50, 1,
c("35 - 95 degree Celsius, 1 degree Celsius per step",
"41.5 - 92.1 degree Celsius, 0.5 degree Celsius per step",
sep = " "), pch = c(15,19), col = c(1,2))
# Use mcaSmoother with n = 2 to increase the temperature
# resolution of the first simulated melting curve. The minmax
# parameter is used to make the peak heights compareable. The
# temperature range was limited between 45 to 90 degree Celsius for
# both simulations
t1f1 <- mcaSmoother(t1, f1, Trange= c(45, 90), minmax = TRUE, n = 2)
t2f2 <- mcaSmoother(t2, f2, Trange= c(45, 90), minmax = TRUE, n = 1)
# Perform a MCA on both altered simulations. As expected, the melting
# temperature are almost identical.
par(mfrow = c(2,1))
# Tm 77.00263, fluoTm -0.1245848
diffQ(t1f1, plot = TRUE)
text(60, -0.08,
"Raw data: 35 - 95 degree Celsius,\n 1 degree Celsius per step")
# Tm 77.00069, fluoTm -0.1245394
diffQ(t2f2, plot = TRUE)
text(60, -0.08, "Raw data: 41.5 - 92.1 degree Celsius,
\n 0.5 degree Celsius per step")
par(default.par)
MBmca documentation built on Sept. 5, 2021, 5:38 p.m.
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Description Usage Arguments Value Author(s) References See Also Examples
Description
The function mcaSmoother() is used for data preprocessing.
Measurements from experimental systems may occasionally include missing
values (NA). mcaSmoother() uses approx() to fill up NAs under
the assumption that all measurements were equidistant. The original data
remain unchanged and only the NAs are substituted. Following it calls
smooth.spline() to smooth the curve. Different strengths can be set
using the option df.fact (f default~0.95). Internally it takes the
degree of freedom value from the spline and multiplies it with a factor
between 0.6 and 1.1. Values lower than 1 result in stronger smoothed curves.
The outcome of the differentiation depends on the temperature resolution of
the melting curve. It is recommended to use a temperature resolution of at
least 0.5 degree Celsius. Besides, for the temperature steps equal distances
60 degree Celsius) rather than unequal distances (e.g., 50 -> 50.4 -> 60.1
(e.g., 50 -> 50.5 -> degree Celsius) are recommended. The parameter
n can be used to increase the temperature resolution of the melting
curve data. mcaSmoother uses the spline function for this purpose. A
temperature range for a simple linear background correction. The linear
trend is estimated by a robust linear regression using lmrob(). In
case criteria for a robust linear regression are violated lm() is
automatically used. The parameter n can be combined with the
parameter Trange to make transform all melting curves of question to
have the #same range and similar resolution. Optionally a Min-Max
normalization between 0 and 1 can be used by setting the option
minmax to TRUE. This is useful in many situations. For
example, if the fluorescence values between samples vary considerably (e.g.,
due to high background, different reporter dyes, ...), particularly in
solution or for better comparison of results.
Usage
1 2 3 4 5 6 7 8 9 10 |
Arguments
x |
is the column of a data frame for the temperature. |
y |
is the column of a data frame for the fluorescence values. |
bgadj |
is used to adjust the background signal. This causes
|
bg |
is used to define the range for the background reduction (e.g.,
|
Trange |
is used to define the temperature range (e.g., |
minmax |
is used to scale the fluorescence a Min-Max normalization
between 0 and 1 can be used by setting the option |
df.fact |
is a factor to smooth the curve. Different strengths can be
set using the option |
n |
is number of interpolations to take place. This parameter uses the spline function and increases the temperature resolution of the melting curve data. |
Value
xy |
returns a |
Author(s)
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. https://pubmed.ncbi.nlm.nih.gov/22437246/
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.
See Also
MFIerror, lmrob,
smooth.spline, spline, lm,
approx
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 | default.par <- par(no.readonly = TRUE)
# First Example
# Use mcaSmoother with different n to increase the temperature
# resolution of the melting curve artificially. Compare the
# influence of the n on the Tm and fluoTm values
data(MultiMelt)
Tm <- vector()
fluo <- vector()
for (i in seq(1,3.5,0.5)) {
res.smooth <- mcaSmoother(MultiMelt[, 1], MultiMelt[, 14], n = i)
res <- diffQ(res.smooth)
Tm <- c(Tm, res$Tm)
fluo <- c(fluo, res$fluoTm)
}
plot(fluo, Tm, ylim = c(76,76.2))
abline(h = mean(Tm))
text(fluo, seq(76.1,76.05,-0.02),
paste("n:", seq(3.5,1,-0.5), sep = " "), col = 2)
abline(h = c(mean(Tm) + sd(Tm), mean(Tm) - sd(Tm)), col = 2)
legend(-0.22, 76.2, c("mean Tm", "mean Tm +/- SD Tm"),
col = c(1,2), lwd = 2)
# Second Example
# Use mcaSmoother with different strengths of smoothing
# (f, 0.6 = strongest, 1 = weakest).
data(DMP)
plot(DMP[, 1], DMP[,6],
xlim = c(20,95), xlab = "Temperature",
ylab = "refMFI", pch = 19, col = 8)
f <- c(0.6, 0.8, 1.0)
for (i in c(1:3)) {
lines(mcaSmoother(DMP[, 1],
DMP[,6], df.fact = f[i]),
col = i, lwd = 2)
}
legend(20, 1.5, paste("f", f, sep = ": "),
cex = 1.2, col = 1:3, bty = "n",
lty = 1, lwd = 4)
# Third Example
# Plot the smoothed and trimmed melting curve
data(MultiMelt)
tmp <- mcaSmoother(MultiMelt[, 1], MultiMelt[, 14])
tmp.trimmed <- mcaSmoother(MultiMelt[, 1], MultiMelt[, 14],
Trange = c(49,85))
plot(tmp, pch = 19, xlab = "Temperature", ylab = "refMFI",
main = "MLC-2v, mcaSmoother using Trange")
points(tmp.trimmed, col = 2, type = "b", pch = 19)
legend(50, 1, c("smoothed values",
"trimmed smoothed values"),
pch = c(19,19), col = c(1,2))
# Fourth Example
# Use mcaSmoother with different n to increase the temperature
# resolution of the melting curve. Caution, this operation may
# affect your data negatively if the resolution is set to high.
# Higher resolutions will just give the impression of better
# data quality. res.st uses the default resolution (no
# alteration)
# res.high uses the double resolution.
data(MultiMelt)
res.st <- mcaSmoother(MultiMelt[, 1], MultiMelt[, 14])
res.high <- mcaSmoother(MultiMelt[, 1], MultiMelt[, 14], n = 2)
par(fig = c(0,1,0.5,1))
plot(res.st, xlab = "Temperature", ylab = "F",
main = "Effect of n parameter on the temperature
resolution")
points(res.high, col = 2, pch = 2)
legend(50, 1, c(paste("default resolution.", nrow(res.st),
"Temperature steps", sep = " "),
paste("double resolution.", nrow(res.high),
"Temperature steps", sep = " ")),
pch = c(1,2), col = c(1,2))
par(fig = c(0,0.5,0,0.5), new = TRUE)
diffQ(res.st, plot = TRUE)
text(65, 0.025, paste("default resolution.", nrow(res.st),
"Temperature steps", sep = " "))
par(fig = c(0.5,1,0,0.5), new = TRUE)
diffQ(res.high, plot = TRUE)
text(65, 0.025, paste("double resolution.", nrow(res.high),
"Temperature steps", sep = " "))
# Fifth example
# Different experiments may have different temperature
# resolutions and temperature ranges. The example uses a
# simulated melting curve with a temperature resolution of
# 0.5 and 1 degree Celsius and a temperature range of
# 35 to 95 degree Celsius.
#
# Coefficients of a 3 parameter sigmoid model. Note:
# The off-set, temperature range and temperature resolution
# differ between both simulations. However, the melting
# temperatures should be very
# similar finally.
b <- -0.5; e <- 77
# Simulate first melting curve with a temperature
# between 35 - 95 degree Celsius and 1 degree Celsius
# per step temperature resolution.
t1 <- seq(35, 95, 1)
f1 <- 0.3 + 4 / (1 + exp(b * (t1 - e)))
# Simulate second melting curve with a temperature
# between 41.5 - 92.1 degree Celsius and 0.5 degree Celsius
# per step temperature resolution.
t2 <- seq(41.5, 92.1, 0.5)
f2 <- 0.2 + 2 / (1 + exp(b * (t2 - e)))
# Plot both simulated melting curves
plot(t1, f1, pch = 15, ylab = "MFI",
main = "Simulated Melting Curves",
xlab = "Temperature", col = 1)
points(t2, f2, pch = 19, col = 2)
legend(50, 1,
c("35 - 95 degree Celsius, 1 degree Celsius per step",
"41.5 - 92.1 degree Celsius, 0.5 degree Celsius per step",
sep = " "), pch = c(15,19), col = c(1,2))
# Use mcaSmoother with n = 2 to increase the temperature
# resolution of the first simulated melting curve. The minmax
# parameter is used to make the peak heights compareable. The
# temperature range was limited between 45 to 90 degree Celsius for
# both simulations
t1f1 <- mcaSmoother(t1, f1, Trange= c(45, 90), minmax = TRUE, n = 2)
t2f2 <- mcaSmoother(t2, f2, Trange= c(45, 90), minmax = TRUE, n = 1)
# Perform a MCA on both altered simulations. As expected, the melting
# temperature are almost identical.
par(mfrow = c(2,1))
# Tm 77.00263, fluoTm -0.1245848
diffQ(t1f1, plot = TRUE)
text(60, -0.08,
"Raw data: 35 - 95 degree Celsius,\n 1 degree Celsius per step")
# Tm 77.00069, fluoTm -0.1245394
diffQ(t2f2, plot = TRUE)
text(60, -0.08, "Raw data: 41.5 - 92.1 degree Celsius,
\n 0.5 degree Celsius per step")
par(default.par)
|
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