Skip to contents

Deconvolute 1D NMR spectrum

Source: R/70_depr.R
MetaboDecon1D.Rd

Automatic deconvolution of a 1D NMR spectrum into several Lorentz curves and the integration of them. The NMR file needs to be in Bruker format or jcamp-dx format.

Deprecated since metabodecon v1.2.0. Please use generate_lorentz_curves() instead. See examples below for usage.

[Deprecated]

Usage

MetaboDecon1D(
  filepath,
  filename = NA,
  file_format = "bruker",
  number_iterations = 10,
  range_water_signal_ppm = 0.1527692,
  signal_free_region = c(11.44494, -1.8828),
  smoothing_param = c(2, 5),
  delta = 6.4,
  scale_factor = c(1000, 1e+06),
  debug = FALSE,
  store_results = NULL
)

Arguments

filepath

Complete path of the file folder (Notice for Bruker format: filepath needs to be the spectrum folder containing one or more different spectra (e.g."C:/Users/Username/Desktop/spectra_from_bruker"))

filename

Name of the NMR file. (Notice for Bruker format: filename need to be the name of your spectrum which is also the name of the folder) (Default: filename = NA to analyze more spectra at once)

file_format

Format (bruker or jcampdx) of the NMR file. (Default: file_format = "bruker")

number_iterations

Number of iterations for the approximation of the parameters for the Lorentz curves (Default: number_iterations=10)

range_water_signal_ppm

Half width of the water artefact in ppm (Default: range_water_signal=0.1527692 (e.g. for urine NMR spectra))

signal_free_region

Row vector with two entries consisting of the ppm positions for the left and right border of the signal free region of the spectrum. (Default: signal_free_region=c(11.44494, -1.8828))

smoothing_param

Row vector with two entries consisting of the number of smoothing repeats for the whole spectrum and the number of data points (uneven) for the mean calculation (Default: smoothing_param=c(2,5))

delta

Defines the threshold value to distinguish between signal and noise (Default: delta=6.4)

scale_factor

Row vector with two entries consisting of the factor to scale the x-axis and the factor to scale the y-axis (Default: scale_factor=c(1000,1000000))

debug

Logical value to activate the debug mode (Default: debug=FALSE)

store_results

Specifies whether the lorentz curve parameters A, lambda and x_0 and the approximated spectrum should be stored on disk (in addition to returning them). If store_results is NULL (default), the user is asked interactively where the files should be stored. If FALSE, the results are not stored. If TRUE, the results are stored in a subdirectory of R's per-session temporary directory.

Value

A decon0 object as described in metabodecon_classes.

References

Haeckl, M.; Tauber, P.; Schweda, F.; Zacharias, H.U.; Altenbuchinger, M.; Oefner, P.J.; Gronwald, W. An R-Package for the Deconvolution and Integration of 1D NMR Data: MetaboDecon1D. Metabolites 2021, 11, 452. https://doi.org/10.3390/metabo11070452

See also

calculate_lorentz_curves(), plot_triplets(), plot_lorentz_curves_save_as_png(), plot_spectrum_superposition_save_as_png()

Author

Martina Haeckl, 2021: Initial version. Tobias Schmidt, 2024: Added parameters debug and store_results. Added minor improvements to pass CRAN checks.

Examples


## ATTENTION: using MetaboDecon1D() for deconvolution is deprecated. Please use
## generate_lorentz_curves() instead.

## The following example shows how a subset of the Sim dataset, consisting
## of two spectrum objects, can be deconvoluted using `MetaboDecon1D()`. The
## whole example code is wrapped into `evalwith()` to simulate user input.
## When using the function interactively, you should type in the answers to
## the questions manually.
expected_answers <- c(
     "10",   # Subfolder of your filepath, i.e. the experiment number?
     "10",   # Subsubsubfolder of filepath, i.e. the processing number?
     "y",    # Use same parameters for all spectra?
     "1",    # File to adjust all parameters.
     "n",    # Signal free region borders correct selected?
     "3.55", # Left border.
     "3.35", # Right border.
     "y",    # Signal free region borders correct selected?
     "n",    # Water artefact fully inside red vertical lines?
     "0",    # Half width range (in ppm) for the water artefact.
     "y",    # Water artefact fully inside red vertical lines?
     "n"     # Save results as text documents?
)
sim <- metabodecon_file("bruker/sim_subset")
evalwith(answers = expected_answers, {
     sim_decon <- MetaboDecon1D(sim)
})
#> What is the name of the subfolder of your filepath: 
#> [e.g. 10 for C:/Users/Username/Desktop/spectra_folder/spectrum_name/10] 
#> 10
#> What is the name of the subsubsubfolder of your filepath: 
#> [e.g. 10 for C:/Users/Username/Desktop/spectra_folder/spectrum_name/10/pdata/10] 
#> 10
#> Do you want to use the same parameters (signal_free_region, range_water_signal_ppm) for all spectra? (y/n) 
#> y
#> [1] "sim_01" "sim_02"
#> Choose number of file which is used to adjust all parameters: [e.g. 1] 
#> 1
#> The selected file to adjust all parameters for all spectra is:  sim_01
#> Start deconvolution of sim_01:

#> Signal free region borders correct selected? (Area left and right of the green lines) (y/n): 
#> n
#> Choose another left border: [e.g. 12] 
#> 3.55
#> Choose another right border: [e.g. -2] 
#> 3.35

#> Signal free region borders correct selected? (Area left and right of the green lines) (y/n): 
#> y

#> Water artefact fully inside red vertical lines? (y/n): 
#> n
#> Choose another half width range (in ppm) for the water artefact: [e.g. 0.1222154] 
#> 0

#> Water artefact fully inside red vertical lines? (y/n): 
#> y
#> 
#> Normed MSE value of iteration 1 is: 
#> [1] 4.996559e-09
#> 
#> Normed MSE value of iteration 2 is: 
#> [1] 2.17297e-09
#> 
#> Normed MSE value of iteration 3 is: 
#> [1] 1.778563e-09
#> 
#> Normed MSE value of iteration 4 is: 
#> [1] 1.652956e-09
#> 
#> Normed MSE value of iteration 5 is: 
#> [1] 1.571827e-09
#> 
#> Normed MSE value of iteration 6 is: 
#> [1] 1.539285e-09
#> 
#> Normed MSE value of iteration 7 is: 
#> [1] 1.507361e-09
#> 
#> Normed MSE value of iteration 8 is: 
#> [1] 1.491203e-09
#> 
#> Normed MSE value of iteration 9 is: 
#> [1] 1.476108e-09
#> 
#> Normed MSE value of iteration 10 is: 
#> [1] 1.460549e-09
#> Save results as text documents at /home/runner/work/_temp/Library/metabodecon/example_datasets/bruker/sim_subset? (y/n) 
#> n
#> Skipping saving of results.
#> Start deconvolution of sim_02:
#> 
#> Normed MSE value of iteration 1 is: 
#> [1] 4.449633e-09
#> 
#> Normed MSE value of iteration 2 is: 
#> [1] 1.688869e-09
#> 
#> Normed MSE value of iteration 3 is: 
#> [1] 1.276977e-09
#> 
#> Normed MSE value of iteration 4 is: 
#> [1] 1.135132e-09
#> 
#> Normed MSE value of iteration 5 is: 
#> [1] 1.085964e-09
#> 
#> Normed MSE value of iteration 6 is: 
#> [1] 1.056171e-09
#> 
#> Normed MSE value of iteration 7 is: 
#> [1] 1.037138e-09
#> 
#> Normed MSE value of iteration 8 is: 
#> [1] 1.02442e-09
#> 
#> Normed MSE value of iteration 9 is: 
#> [1] 1.004947e-09
#> 
#> Normed MSE value of iteration 10 is: 
#> [1] 1.002143e-09
#> Skipping saving of results.

## Deconvolute only the first spectrum of the folder "bruker/sim_subset" into
evalwith(answers = expected_answers[-(3:4)], {
     sim_decon <- MetaboDecon1D(sim, filename = "sim_01")
})
#> Start deconvolution of sim_01:
#> What is the name of the subfolder of your filepath: 
#> [e.g. 10 for C:/Users/Username/Desktop/spectra_folder/spectrum_name/10] 
#> 10
#> What is the name of the subsubsubfolder of your filepath: 
#> [e.g. 10 for C:/Users/Username/Desktop/spectra_folder/spectrum_name/10/pdata/10] 
#> 10

#> Signal free region borders correct selected? (Area left and right of the green lines) (y/n): 
#> n
#> Choose another left border: [e.g. 12] 
#> 3.55
#> Choose another right border: [e.g. -2] 
#> 3.35

#> Signal free region borders correct selected? (Area left and right of the green lines) (y/n): 
#> y

#> Water artefact fully inside red vertical lines? (y/n): 
#> n
#> Choose another half width range (in ppm) for the water artefact: [e.g. 0.1222154] 
#> 0

#> Water artefact fully inside red vertical lines? (y/n): 
#> y
#> 
#> Normed MSE value of iteration 1 is: 
#> [1] 4.996559e-09
#> 
#> Normed MSE value of iteration 2 is: 
#> [1] 2.17297e-09
#> 
#> Normed MSE value of iteration 3 is: 
#> [1] 1.778563e-09
#> 
#> Normed MSE value of iteration 4 is: 
#> [1] 1.652956e-09
#> 
#> Normed MSE value of iteration 5 is: 
#> [1] 1.571827e-09
#> 
#> Normed MSE value of iteration 6 is: 
#> [1] 1.539285e-09
#> 
#> Normed MSE value of iteration 7 is: 
#> [1] 1.507361e-09
#> 
#> Normed MSE value of iteration 8 is: 
#> [1] 1.491203e-09
#> 
#> Normed MSE value of iteration 9 is: 
#> [1] 1.476108e-09
#> 
#> Normed MSE value of iteration 10 is: 
#> [1] 1.460549e-09
#> Save results as text documents at /home/runner/work/_temp/Library/metabodecon/example_datasets/bruker/sim_subset? (y/n) 
#> n
#> Skipping saving of results.