Part 0. Setup

library(nmrrr)

library(ggplot2)
theme_set(theme_bw()) # set the default ggplot theme

SPECTRA_FILES <- system.file("extdata", "kfp_hysteresis", "spectra_mnova", package = "nmrrr")
PEAKS_FILES <- system.file("extdata", "kfp_hysteresis", "peaks_mnova_multiple", package = "nmrrr")

Part 1: Importing spectra files: nmr_import_spectra()

This function will:

• import all the .csv files from the SPECTRA_FILES location,
• combine them into a single dataframe, and
• generate a dataframe with columns for ppm-shift, intensity, and sample-ID

spectra_df <- nmr_import_spectra(path = SPECTRA_FILES,
                                 method = "mnova") 

str(spectra_df)
#> tibble [65,589 × 3] (S3: tbl_df/tbl/data.frame)
#>  $ ppm      : num [1:65589] 0.00392 0.00422 0.00453 0.00483 0.00514 ...
#>  $ intensity: num [1:65589] 0.00202 0.00202 0.00203 0.00203 0.00204 ...
#>  $ sampleID : chr [1:65589] "29" "29" "29" "29" ...

Further cleaning of the dataframe may be done by the user, according to specific needs. For instance, including only certain ranges of ppm shift. For this dataset, we include only points between 0 and 10 ppm.

spectra_df <- subset(spectra_df, ppm >= 0 & ppm <= 10)

str(spectra_df)
#> tibble [65,440 × 3] (S3: tbl_df/tbl/data.frame)
#>  $ ppm      : num [1:65440] 0.00392 0.00422 0.00453 0.00483 0.00514 ...
#>  $ intensity: num [1:65440] 0.00202 0.00202 0.00203 0.00203 0.00204 ...
#>  $ sampleID : chr [1:65440] "29" "29" "29" "29" ...

Part 2: Plotting the spectra: nmr_plot_spectra()

This function will plot all the spectra present in the spectra_df file. The spectra will be stacked and offset vertically (this can be customized).

• The compound classes (bins) are labelled in the graph.
• Adjust the position of the labels (along the y-axis) using LABEL_POSITION = ....
• Overlaid spectra can be staggered along the y-axis using STAGGER = ....
• This function makes use of ggplot2 capabilities, and the plot can therefore be customized using ggplot2 nomenclature.

nmr_plot_spectra(dat = spectra_df,
                 binset = bins_Clemente2012,
                 label_position = 5,
                 mapping = aes(x = ppm, 
                               y = intensity, 
                               group = sampleID, 
                               color = sampleID),
                 stagger = 0.5) +
  # OPTIONAL PARAMETERS/LAYERS
  geom_rect(aes(xmin = 2, xmax = 4, ymin = 0, ymax = 5.5), 
            fill = "white", color = NA, alpha = 0.8)+
  labs(subtitle = "binset: Clemente et al. 2012")+
  ylim(0, 5.5)

Notes:

• This example uses the bins from Clemente et al. 2012. For more details, including bin descriptions, see ?binset_Clemente2012 or vignette("nmrrr_binsets).
• The region 2.0 to 4.0 ppm includes solvent regions (DMSO-d6 and H2O) and is therefore not included in the analyses. [USER CUSTOMIZATION]

Part 3: Assigning compound classes: nmr_assign_bins()

This function will assign bins/compound classes to the peaks based on the preferred bin set.

This package provides bin sets for DMSO-d6, D2O, and MeOD solvents. Users can choose from the available options, or can import their own preferred bin set. See vignette("nmrrr_binsets") for more details.

spectra_bins <- nmr_assign_bins(dat = spectra_df,
                                binset = bins_Clemente2012)

Note: The user may want to assign additional filtering steps to filter certain flagged data points, e.g. impurities, weak peaks, etc.

For this current dataset, because of the strong influence of water peaks in the o-alkyl region, we exclude that region from our calculations.

spectra_bins <- subset(spectra_bins, group != "oalkyl")

Part 4: Calculating relative abundance of compound classes: nmr_relabund()

Method 1: Integrating area under the curve from processed spectra files

relabund_integration <- nmr_relabund(dat = spectra_bins,
                                     method = "AUC")

Method 2: Calculating from peaks data

This method is specific to MNova-processed data. Users may pick peaks within MNova and export these as a table. In this case, users can simply add the area counts for each peak to calculate the relative contribution of the peak/bin type to the total area.

The peaks data can be exported one of two ways, giving two different formats of data files (“single columns” and “multiple columns”); this package can handle both versions. More details can be found in the repository wiki.

For both types, however, we first need to import and combine the files, then assign bin classes, and then add the areas.

peaks_df <- nmr_import_peaks(path = PEAKS_FILES, 
                             method = "multiple columns")

str(peaks_df)
#> tibble [207 × 10] (S3: tbl_df/tbl/data.frame)
#>  $ Obs              : int [1:207] 1 2 3 4 5 6 7 8 9 10 ...
#>  $ ppm              : num [1:207] 15.37 14.27 14.09 7.08 7.07 ...
#>  $ Intensity        : num [1:207] 0 0 0 0.3 0 0.1 0.5 0 0 0 ...
#>  $ Width            : num [1:207] 0.63 0.61 0.71 43.83 15.82 ...
#>  $ Area             : num [1:207] 0.08 0.07 0.07 179.46 3.01 ...
#>  $ Type             : chr [1:207] "Artifact" "Artifact" "Artifact" "Compound" ...
#>  $ Flags            : chr [1:207] "Weak" "Weak" "Weak" "None" ...
#>  $ Impurity/Compound: chr [1:207] NA NA NA NA ...
#>  $ Annotation       : chr [1:207] "" "" "" "" ...
#>  $ sampleID         : chr [1:207] "29" "29" "29" "29" ...

peaks_df <- subset(peaks_df, Type == "Compound")

peaks_bins <- nmr_assign_bins(dat = peaks_df,
                              binset = bins_Clemente2012)

peaks_bins <- subset(peaks_bins, group != "oalkyl")

relabund_peaks <- nmr_relabund(dat = peaks_bins,
                               method = "peaks")

Part 5: Visualizing the processed relative abundance data

Users may then plot the relative abundance data using stacked bar plots, for example:

ggplot(relabund_integration,
       aes(x = sampleID, y = relabund, fill = group))+
  geom_bar(stat = "identity")+
  labs(title = "Relative abundance by AUC",
       subtitle = "binset: Clemente et al. 2012")