Wrapper for several techniques to perform data transformation of the original fingerprint matrix.
A numeric data frame or matrix to be pre-processed.
A method used to pre-process the data set. The following methods are supported:
A numeric value for addition used in the logarithmic transformations
A factor specifying the class for each observation. It is only used by the
Purpose of normalisation is to remove inter-spectrum sources of variability
that come mainly from different sample concentration, loss of sensitivity of
the detector over time or degradation of certain samples. Global normalisation
by rescaling each measurement within a spectrum by a constant factor, such as
the sum of all the spectra intensities (
TICnorm method). However, the
default use of TIC normalisation can lead to the generation of spurious
knowledge if the overall sample intensity is class/factor dependent. If this
is the case, a straightforward approach is to remove the difference, between
the average scale of the corresponding class and the average scale, from the
scale factor by providing the class/factor of interest (argument
Transformed sample matrix.
Berg, R., Hoefsloot, H., Westerhuis, J., Smilde, A. and Werf, M. (2006), Centering, scaling, and transformations: improving the biological information content of metabolomics data, BMC Genomics, 7:142
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data(abr1) cl <- factor(abr1$fact$class) dat <- abr1$pos[,110:2000] ## normalise data set using class dependent "TICnorm" z.1 <- preproc(dat, y=cl, method="TICnorm") ## scale data set using "log10" z.2 <- preproc(dat,method="log10", add=1) ## or scale data set using "log" and "TICnorm" sequentially z.3 <- preproc(dat,method=c("log","TICnorm"), add=0.1)
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