getBucketsDataset | R Documentation |
Generates the matrix including the integrations of the areas defined by the buckets (columns) on each spectrum (rows)
getBucketsDataset(specObj, norm_meth = "none", zoneref = NA)
specObj |
a complex list return by |
norm_meth |
Normalization method. The possible values are : 'none', 'CSN' or 'PDN'. See below. |
zoneref |
Specify the ppm zone of the internal reference (i.e. ERETIC) if applicable. default is NA. |
Before bucket data export in order to make all spectra comparable with each other, the variations of the overall concentrations of samples have to be taken into account. We propose two normalization methods. In NMR metabolomics, the total intensity normalization (called the Constant Sum Normalization) is often used so that all spectra correspond to the same overall concentration. It simply consists in normalizing the total intensity of each individual spectrum to a same value. An other method called Probabilistic Quotient Normalization (Dieterle et al. 2006) assumes that biologically interesting concentration changes influence only parts of the NMR spectrum, while dilution effects will affect all metabolites signals. Probabilistic Quotient Normalization (PQN) starts by the calculation of a reference spectrum based on the median spectrum. Next, for each variable of interest the quotient of a given test spectrum and reference spectrum is calculated and the median of all quotients is estimated. Finally, all variables of the test spectrum are divided by the median quotient. An internal reference can be used to normalize the data. For example, an electronic reference (ERETIC, see Akoka et al. 1999, or ERETIC2 generated with TopSpin software) can be used for this purpose. The integral value of each bucket will be divided by the integral value of the ppm range given as reference.
the data matrix
Akoka S1, Barantin L, Trierweiler M. (1999) Concentration Measurement by Proton NMR Using the ERETIC Method, Anal. Chem 71(13):2554-7. doi: 10.1021/ac981422i.
Dieterle F., Ross A., Schlotterbeck G. and Senn H. (2006). Probabilistic Quotient Normalization as Robust Method to Account for Dilution of Complex Biological Mixtures. Application in 1H NMR Metabonomics. Analytical Chemistry, 78:4281-4290.doi: 10.1021/ac051632c
data_dir <- system.file("extra", package = "Rnmr1D")
cmdfile <- file.path(data_dir, "NP_macro_cmd.txt")
samplefile <- file.path(data_dir, "Samples.txt")
out <- Rnmr1D::doProcessing(data_dir, cmdfile=cmdfile,
samplefile=samplefile, ncpu=2)
outMat <- getBucketsDataset(out, norm_meth='CSN')
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