getClusters: getClusters

Description Usage Arguments Details Value References Examples

View source: R/StatTools.R

Description

From the data matrix generated from the integration of all bucket zones (columns) for each spectrum (rows), we can take advantage of the concentration variability of each compound in a series of samples by performing a clustering based on significant correlations that link these buckets together into clusters. Bucket Clustering based on either a lower threshold applied on correlations or a cutting value applied on a hierarchical tree of the variables (buckets) generated by an Hierarchical Clustering Analysis (HCA).

Usage

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getClusters(data, method = "hca", ...)

Arguments

data

the matrix including the integrations of the areas defined by the buckets (columns) on each spectrum (rows)

method

Clustering method of the buckets. Either 'corr' for 'correlation' or 'hca' for 'hierarchical clustering analysis'.

...

Depending on the chosen method:

  • corr : cval, dC, ncpu

  • hca : vcutusr

Details

At the bucketing step (see above), we have chosen the intelligent bucketing, it means that each bucket exact matches with one resonance peak. Thanks to this, the buckets now have a strong chemical meaning, since the resonance peaks are the fingerprints of chemical compounds. However, to assign a chemical compound, several resonance peaks are generally required in 1D 1 H-NMR metabolic profiling. To generate relevant clusters (i.e. clusters possibly matching to chemical compounds), two approaches have been implemented:

Value

getClusters returns a list containing the following components:

References

Jacob D., Deborde C. and Moing A. (2013) An efficient spectra processing method for metabolite identification from 1H-NMR metabolomics data. Analytical and Bioanalytical Chemistry 405(15) 5049-5061 doi: 10.1007/s00216-013-6852-y

Examples

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  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')
  clustcorr <- getClusters(outMat, method='corr', cval=0, dC=0.003, ncpu=2)
  clusthca <- getClusters(outMat, method='hca', vcutusr=0)
 

Rnmr1D documentation built on Nov. 10, 2021, 9:07 a.m.