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This article shows how Metabodecon can be used for deconvoluting and aligning one-dimensional NMR spectra using the pre-installed Sim dataset as an example. The Sim dataset includes 16 simulated spectra, each with 2048 data points ranging from ≈ 3.6 to 3.3 ppm. These simulated spectra closely mimic the resolution and signal strength of real NMR experiments on blood plasma from 16 individuals. The Sim dataset is used instead of the Blood dataset because it is smaller, faster to process, and comes pre-installed with the package. For more information on the Sim and Blood datasets, see Datasets.

Deconvolute spectra

To find the path to the Sim dataset, you can use the metabodecon_file() function, which returns the path to any file or directory within the package directory. To deconvolute the spectra within the Sim dataset you can use generate_lorentz_curves():

sim_dir <- metabodecon::metabodecon_file("bruker/sim")
deconvoluted_spectra <- metabodecon::generate_lorentz_curves(
    data_path = sim_dir, # Path to directory containing spectra
    sfr = c(3.35, 3.55), # Borders of signal free region (SFR) in ppm
    wshw = 0,            # Half width of water signal (WS) in ppm
    smopts = c(2, 5),    # Configure smoothing parameters
    verbose = FALSE      # Suppress status messages
)

After calling generate_lorentz_curves(), the function will ask you to answer the following questions to determine the optimal deconvolution parameters:

1. Use same parameters for all spectra? (y/n)
2. Number of spectrum for adjusting parameters? (1: sim_01, 2: sim_02, …)
3. Signal free region correctly selected? (y/n)
4. Water artefact fully inside red vertical lines? (y/n)

You can answer questions one and two with y and 1, as the dataset is homogeneous, i.e., all spectra were measured in the same lab with the same acquisition and processing parameters. However, for heterogeneous datasets, it’s advisable to optimize parameters for each batch of spectra individually.

Questions three and four are accompanied by two plots, shown in Figure 1, which help you to verify the accuracy of the selected signal-free region (SFR) and water signal half-width (WSHW) ¹. In this case, the provided parameters are already fine, so you can answer both questions with y. If adjustments are needed, you can respond with n and input the correct values.

Figure 1. The first spectrum of the Sim dataset. The x-Axis gives the chemical shift of each datapoint in parts per million (ppm). The y-Axis gives the signal intensity of each datapoint in arbitrary units (au). The signal free regions are shown as green rectangles in the left plot. The water signal region, usually shown as blue rectangle, is shown in the right plot. Because the water signal half width is set to zero in this case, the rectangle collapses to a vertical line.

When using the function in scripts, where interactive user input is not desired, you can disable the interactive prompting by setting parameter ask to FALSE. In this case, the provided parameters will be used for the deconvolution of all spectra automatically. ²

Visualize deconvoluted spectra

After completing the deconvolution, it is advisable to visualize the extracted signals using plot_spectrum() to assess the quality of the deconvolution.

# Visualize the first spectrum.
metabodecon::plot_spectrum(deconvoluted_spectra[[1]])

# Visualize the second spectrum, this time without the legend.
metabodecon::plot_spectrum(deconvoluted_spectra[[1]], lgd = FALSE)

# Visualize all spectra and save them to a pdf file
pdfpath <- tempfile(fileext = ".pdf")
pdf(pdfpath)
for (x in deconvoluted_spectra) {
    metabodecon::plot_spectrum(x, main = x$filename)
}
dev.off()
cat("Plots saved to", pdfpath, "\n")

Out of the 16 generated plots, the first two are shown as examples in Figure 2. Things to look out for are:

1. That the smoothing does not remove any real signals. If the smoothing is too strong, i.e., the smoothed signal intensity (SI) is very different from the raw SI, you should adjust the smoothing parameters smopts in the call to generate_lorentz_curves().
2. That the superposition of the lorentz curves is a good approximation of the smoothed SI. If major peaks are missed by the algorithm, you should reduce the threshold delta in the call to generate_lorentz_curves().

Figure 2. Deconvolution results for the first two spectra of the Sim dataset. The raw SI (black), smoothed SI (blue), and superposition of Lorentz curves (red) are closely aligned, indicating that smopts and delta were chosen well and that the deconvolution was successful.

Align deconvoluted spectra

The last step in the Metabodecon Workflow is to align the deconvoluted spectra. This is necessary because the chemical shifts of the peaks in the spectra may vary slightly due to differences in the measurement conditions.

To perform the alignment, you can use align(). To visualize the data before and after the alignment, you can use plot_spectra():

# Plot spectra before alignment. Only show spectra 1-8 for clarity.
metabodecon::plot_spectra(deconvoluted_spectra[1:8], lgd = FALSE)

# Align spectra and plot again.
aligned_spectra <- metabodecon::align(deconvoluted_spectra)
metabodecon::plot_spectra(aligned_spectra[1:8])

The resulting plots are shown in Figure 3. Before the alignment, the spectra exhibit generally similar shapes but do not perfectly overlap. After the alignment, the spectra are much more consistent with each other, indicating that the alignment was successful. Notably, spectrum two has been shifted significantly to the left.

Figure 3. Overlay of the first eight deconvoluted spectra from the Sim dataset before alignment (left) and after alignment (right). The x-Axis gives the chemical shift of each datapoint in parts per million (ppm). The y-Axis gives the signal intensity of each datapoint in arbitrary units (au). All specta are pretty similar to each other except for Spectrum 2, which got shifted approx. 0.01 ppm to the right.