mcaSmoother
(Package: MBmca) :
Function to pre-process melting curve data.

The function mcaSmoother() is used for data pre-processing. 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. In addition equal distances (e.g., 50 -> 50.5 -> 60 degree Celsius) rather than unequal distances (e.g., 50 -> 50.4 -> 60.1 degree Celsius) for the temperature steps 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.

mcaPeaks
(Package: MBmca) :
Function to estimate the approximate local minima and maxima of melting

The mcaPeaks() is used to estimate the approximate local minima and maxima of melting curve data. This can be useful to define a temperature range for melting curve analysis, quality control of the melting curve or to define a threshold of peak heights. Melting curves may consist of multiple significant and insignificant melting peaks. mcaPeaks() uses estimated the temperatures and fluorescence values of the local minima and maxima. The original data remain unchanged and only the approximate local minima and maxima are returned. mcaPeaks() takes modified code proposed earlier by Brian Ripley (https://stat.ethz.ch/pipermail/r-help/2002-May/021934.html).

diffQ2
(Package: MBmca) :
Calculation of the melting temperatures (Tm, Tm1D2 and Tm2D2) from the first

diffQ2() calls instances of diffQ() to calculate the Tm1D2 and Tm2D2. The options are similar to diffQ(). Both diffQ() and diffQ2() return objects of the class list. To accessing components of lists is done as described elsewhere either be name or by number. diffQ2 has no standalone plot function. For sophisticated analysis and plots its recommended to use diffQ2 as presented in the examples as part of algorithms.

diffQ
(Package: MBmca) :
Calculation of the melting temperature (Tm) from the first derivative

diffQ is used to calculate the melting temperature (Tm) but also for elementary graphical operations (e.g., show the Tm or the derivative). It does not require smoothed data for the MCA. The parameter rsm can be used to double the temperature resolution by calculation of the mean temperature and mean fluorescence. Note: mcaSmoother has the n parameter with a similar functionality. First the approximate Tm is determined as the min() and/or max() from the first derivative. The first numeric derivative (Forward Difference) is estimated from the values of the function values obtained during an experiment since the exact function of the melting curve is unknown. The method used in diffQ is suitable for independent variables that are equally and unequally spaced. Alternatives for the numerical differentiation include Backward Differences, Central Differences or Three-Point (Forward or Backward) Difference based on Lagrange Estimation (currently not implemented in diffQ). The approximate peak value is the starting-point for a function based calculation. The function takes a defined number n (maximum 8) of the left and the right neighbor values and fits a quadratic polynomial. The quadratic regression of the X (temperature) against the Y (fluorescence) range gives the coefficients. The optimal quadratic polynomial is chosen based on the highest adjusted R-squared value. diffQ returns an objects of the class list. To accessing components of lists is done as described elsewhere either by name or by number. diffQ has a simple plot function. However, for sophisticated analysis and plots its recommended to use diffQ as presented in the examples as part of algorithms.

MFIerror
(Package: MBmca) :
Multiple comparison of the temperature dependent variance of the refMFI

MFIerror is used for a fast multiple comparison of the temperature dependent variance of the refMFI. MFIerror returns an object of the class data.frame with columns “Temperature”, “Location” (Mean, Median), “Deviation” (Standard Deviation, Median Absolute Deviation) and “Coefficient of Variation”.

Nucleic acid Melting Curve Analysis is a powerful method to investigate the interaction of double stranded nucleic acids. The MBmca package provides data sets and lightweight utilities for nucleic acid melting curve analysis and presentation on microbead surfaces. However, the function of the package can also be used for the analysis of reactions in solution (e.g., qPCR). Methods include melting curve data pre-processing (smooth, normalize, rotate, background subtraction), data inspection (comparison of multiplex melting curves) with location parameters (mean, median), deviation parameters (standard of the melting peaks including the second derivative. The second derivative melting peaks is implemented as parameter to further characterize the melting behavior. Plot functions to illustrate data quality, smoothed curves and derivatives are available too.