Introduction

This document provides a brief summary on how to use the PepsNMR package. In this package, pre-processing functions transform raw FID signals from 1H NMR spectroscopy into a set of interpretable spectra.

Installation

The PepsNMR package is available on Bioconductor and can be installed via BiocManager::install:

if (!requireNamespace("BiocManager", quietly = TRUE))
  install.packages("BiocManager")

BiocManager::install("PepsNMR", 
                     dependencies = c("Depends", "Imports", "Suggests"))

Note tha installing the Suggests dependencies will install the PepsNMRData package to run the demo below.

The package needs to be loaded once installed to be used:

library(PepsNMR)

The package development version is available on Github (Master branch): https://github.com/ManonMartin/PepsNMR, although it is highly recommended to rely on the Bioconductor release version of the package to avoid any package version mismatch.

Data importation

The first step is meant to access the raw data files. To import Free Induction Decays (FIDs) in Bruker format, use the ReadFids function. This function will return a list with the FID data matrix (saved in Fid_data) and metadata about these FIDs (saved in Fid_info).

fidList <- ReadFids(file.path(path,dataset_name)) 

Fid_data <- fidList[["Fid_data"]]
Fid_info <- fidList[["Fid_info"]]

The possible directory structures are illustrated here:

Accepted directory structures for the raw Bruker files

And is to be linked with the possible options of the ReadFids function:

If use of title file and presence of sub-directories: set the FID Title line, subdirs = TRUE, dirs.names = FALSE
If use of title file and no sub-directories: set the FID Title line, subdirs = FALSE, dirs.names = FALSE
If no use of title file and presence of sub-directories: subdirs = TRUE, dirs.names = TRUE
If no use of title file and no sub-directories: subdirs = FALSE, dirs.names = TRUE

Pre-processing steps

Here is the recommended pre-processing workflow on the FIDs and the spectra once the raw data have been loaded in R:

Pre-processing steps. They are presented in the suggested order.
Steps	Description
Group Delay Correction	Correct for the Bruker Group Delay.
Solvent Suppression	Remove the solvent signal from the FIDs.
Apodization	Increase the Signal-to-Noise ratio of the FIDs.
ZeroFilling	Improve the visual representation of the spectra.
Fourier Transform	Transform the FIDs into a spectrum and convert the frequency scale (Hz -> ppm).
Zero Order Phase Correction	Correct for the zero order phase dephasing.
Internal Referencing	Calibrate the spectra with an internal reference compound. Referencing with an internal (e.g. TMSP at 0 ppm)
Baseline Correction	Remove the spectral baseline.
Negative Values Zeroing	Set negatives values to 0.
Warping	Warp the spectra according to a reference spectrum.
Window Selection	Select the informative part of the spectrum.
Bucketing	Data reduction.
Region Removal	Set a desired spectral region to 0.
Zone Aggregation	Aggregate a spectral region into a single peak.
Normalization	Normalize the spectra.

Information on each function is provided in R, (e.g. type ?ReadFids in the R console) and methodological details are found in Martin et al. (2018).

Available datasets

Human serum (HS) and urine (HU) datasets are available as raw data (FIDs in Bruker format) and as (partially) pre-processed signals/spectra in the ad hoc PepsNMRData package that is automatically installed with PepsNMR (through ).

Here are examples of available datasets:

library(PepsNMRData)

str(FidData_HU)
#>  cplx [1:24, 1:29411] 0+0i 0+0i 0+0i ...
#>  - attr(*, "dimnames")=List of 2
#>   ..$ : chr [1:24] "S1-D0-E1" "S1-D0-E2" "S1-D1-E2" "S2-D0-E2" ...
#>   ..$ : chr [1:29411] "0" "5.1e-05" "0.000102" "0.000153" ...

str(FinalSpectra_HS)
#>  cplx [1:32, 1:500] 0.0769-371030i 0.0466-362686i 0.0639-216899i ...
#>  - attr(*, "dimnames")=List of 2
#>   ..$ : chr [1:32] "J1-D1-1D-T1" "J3-D2-1D-T8" "J3-D3-1D-T9" "J3-D4-1D-T14" ...
#>   ..$ : chr [1:500] "9.98986984692192" "9.97027064485524" "9.95067144278856" "9.93107224072187" ...

Information for each dataset is available, (e.g. enter ?FidData_HS in the R Console).

Demo on the HSerum dataset

Load the data

Raw Bruker FIDs can be loaded from where PepsNMRData has been intalled:

data_path <-  system.file("extdata", package = "PepsNMRData")
dir(data_path)
#> [1] "Group_HS.csv" "HumanSerum"

Read the FID data file

To import FIDs in Bruker format, the ReadFids function is used. This function will return a list with the FID data matrix (here saved as Fid_data) and information about these FIDs (here saved as Fid_info).

# ==== read the FIDs and their metadata =================
fidList <- ReadFids(file.path(data_path, "HumanSerum"))
Fid_data0 <- fidList[["Fid_data"]]
Fid_info <- fidList[["Fid_info"]]
kable(head(Fid_info))

	TD	BYTORDA	DIGMOD	DECIM	DSPFVS	SW_h	SW	O1	DT
J1-D1-1D-T1	65536	1	1	16	12	10245.9	20.48638	2352.222	4.88e-05
J3-D2-1D-T8	65536	1	1	16	12	10245.9	20.48638	2352.222	4.88e-05
J3-D3-1D-T9	65536	1	1	16	12	10245.9	20.48638	2352.222	4.88e-05
J3-D4-1D-T14	65536	1	1	16	12	10245.9	20.48638	2352.222	4.88e-05
J4-D1-1D-T4	65536	1	1	16	12	10245.9	20.48638	2352.222	4.88e-05
J4-D2-1D-T7	65536	1	1	16	12	10245.9	20.48638	2352.222	4.88e-05

Raw FID.

Group Delay Correction

The Bruker’s digital filter originally produces a Group Delay that is removed during this step.

# ==== GroupDelayCorrection =================
Fid_data.GDC <- GroupDelayCorrection(Fid_data0, Fid_info)

Signal before and after the Group Delay removal.

Solvent Suppression

Pre-acquisition techniques for solvent suppression can be unsufficient to remove the solvent signal from the FIDs. SolventSuppression estimates and removes this residual solvent signal based on a Whittaker smoother.

# ====  SolventSuppression =================
SS.res <- SolventSuppression(Fid_data.GDC, returnSolvent=TRUE)

Fid_data.SS <- SS.res[["Fid_data"]]
SolventRe <- SS.res[["SolventRe"]]

Signal before and after the Residual Solvent Removal.

Apodization

The apodization step increases the spectral Signal-to-Noise ratio by multiplying the FIDs by an appropriate factor (by default a negative exponential).

# ==== Apodization =================
Fid_data.A <- Apodization(Fid_data.SS, Fid_info)

Signal before and after Apodization.

Zero Filling

The zero filling adds 0 to the end of the FIDs to improve the visual representation of spectra.

# ==== Zero Filling =================
Fid_data.ZF <- ZeroFilling(Fid_data.A, fn = ncol(Fid_data.A))

Signal before and after Apodization.

FourierTransform

The Fourier Transform is applied to the FIDs to obtain spectra expressed in the frequency domain. The FourierTransform function further converts this frequency scale originally in hertz into parts per million (ppm).

# ==== FourierTransform =================
RawSpect_data.FT <- FourierTransform(Fid_data.ZF, Fid_info)

Spectrum after FT.

Zero Order Phase Correction

After Fourier Transform, the spectra need to be phased for the real part to be in pure absoptive mode (i.e. with positive intensities).

# ==== ZeroOrderPhaseCorrection =================
Spectrum_data.ZOPC <- ZeroOrderPhaseCorrection(RawSpect_data.FT)

# with a graph of the criterion to maximize over 2pi
Spectrum_data.ZOPC <- ZeroOrderPhaseCorrection(RawSpect_data.FT,
                                               plot_rms = "J1-D1-1D-T1")

Spectrum after Zero Order Phase Correction.

Internal Referencing

During this step, the spectra are aligned with an internal reference compound (e.g. with the TMSP). The user determines the ppm value of the reference compound which is by default 0.

# ==== InternalReferencing =================
target.value <- 0

IR.res <- InternalReferencing(Spectrum_data.ZOPC, Fid_info,
                                        ppm.value = target.value,
                                        rowindex_graph = c(1,2))
# draws Peak search zone and location of the 2 first spectra
IR.res$plots
#> [[1]]


Spectrum_data.IR <- IR.res$Spectrum_data

Spectrum after Internal Referencing.

Baseline Correction

The spectral baseline is estimated and removed from the spectral profiles with the Asymetric Least Squares smoothing algorithm.

# ==== BaselineCorrection =================
BC.res <- BaselineCorrection(Spectrum_data.IR, returnBaseline = TRUE,
                             lambda.bc = 1e8, p.bc = 0.01)

Spectrum before and after the Baseline Correction.

Negative Values Zeroing

The remaining negative values are set to 0 since they cannot be interpreted.

# ==== NegativeValuesZeroing =================
Spectrum_data.NVZ <- NegativeValuesZeroing(Spectrum_data.BC)

Spectrum after Negative Values Zeroing.

Warping

The spectra are realigned based on a reference spectrum with a Semi-Parametric Time Warping technique.

# ==== Warping =================
W.res <- Warping(Spectrum_data.NVZ, returnWarpFunc = TRUE, 
                 reference.choice = "fixed")

Spectrum_data.W <- W.res[["Spectrum_data"]]
warp_func <- W.res[["Warp.func"]]

Spectrum before and after the Baseline Correction.

Window Selection

During this step the user selects the part of the spectrum that is of interest and the other parts are removed from the spectral matrix.

# ==== WindowSelection =================
Spectrum_data.WS <- WindowSelection(Spectrum_data.W, from.ws = 10, 
                                    to.ws = 0.2)

Spectrum after Window Selection.

Bucketing

The Bucketing function reduces the number of spectral descriptors by aggregating intensities into a series of buckets.

# ==== Bucketing =================
Spectrum_data.B <- Bucketing(Spectrum_data.WS, intmeth = "t")

Spectrum before and after Bucketing.

Region Removal

By default, this step sets to zero spectral areas that are of no interest or have a sigificant and unwanted amount of variation (e.g. the water area).

# ==== RegionRemoval =================
Spectrum_data.RR <- RegionRemoval(Spectrum_data.B, 
                                  typeofspectra = "serum")

Spectrum after Region Removal

Normalization

The normalization copes with the dilution factor and other issues that render the spectral profiles non-comparable to each other.

# ==== Normalization =================
Spectrum_data.N <- Normalization(Spectrum_data.RR, type.norm = "mean")

Spectrum before and after Normalization

Note: the function ZoneAggregation is not used here, but is can be applied e.g. to urine spectra to aggregate the citrate peak.

Final spectra visualisation

The Draw function enables to produce line plots with type.draw = "signal" or PCA results with type.draw = "pca" of FIDs or spectra. These plots can be saved as pdf files with the option output = "pdf", see ?Draw, ?DrawSignal and ?DrawPCA for more details on the other available options.

Draw(Spectrum_data.N[1:4,], type.draw = c("signal"), 
     subtype= "stacked", output = c("default"))

4 first pre-processed spectra

Draw(Spectrum_data.N, type.draw = c("pca"), 
     output = c("default"), Class = Group_HS, 
     type.pca = "scores")

PCA scores of the pre-processed spectra

Draw(Spectrum_data.N, type.draw = c("pca"), 
     output = c("default"),
     Class = Group_HS, type.pca = "loadings")

PCA loadings of the pre-processed spectra

Session info

sessionInfo()
#> R version 4.4.2 (2024-10-31)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 24.04.1 LTS
#> 
#> Matrix products: default
#> BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
#> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so;  LAPACK version 3.12.0
#> 
#> locale:
#>  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
#>  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=C              
#>  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
#>  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
#>  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
#> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
#> 
#> time zone: Etc/UTC
#> tzcode source: system (glibc)
#> 
#> attached base packages:
#> [1] stats     graphics  grDevices utils     datasets  methods   base     
#> 
#> other attached packages:
#> [1] PepsNMRData_1.24.0 PepsNMR_1.25.0     knitr_1.49         BiocStyle_2.35.0  
#> 
#> loaded via a namespace (and not attached):
#>  [1] Matrix_1.7-1        gtable_0.3.6        jsonlite_1.8.9     
#>  [4] compiler_4.4.2      BiocManager_1.30.25 Rcpp_1.0.13-1      
#>  [7] stringr_1.5.1       gridExtra_2.3       jquerylib_0.1.4    
#> [10] scales_1.3.0        yaml_2.3.10         fastmap_1.2.0      
#> [13] lattice_0.22-6      plyr_1.8.9          ggplot2_3.5.1      
#> [16] R6_2.5.1            labeling_0.4.3      ptw_1.9-16         
#> [19] tibble_3.2.1        maketools_1.3.1     munsell_0.5.1      
#> [22] bslib_0.8.0         pillar_1.9.0        rlang_1.1.4        
#> [25] utf8_1.2.4          stringi_1.8.4       cachem_1.1.0       
#> [28] xfun_0.49           sass_0.4.9          sys_3.4.3          
#> [31] cli_3.6.3           withr_3.0.2         magrittr_2.0.3     
#> [34] digest_0.6.37       grid_4.4.2          lifecycle_1.0.4    
#> [37] vctrs_0.6.5         evaluate_1.0.1      glue_1.8.0         
#> [40] farver_2.1.2        buildtools_1.0.0    RcppDE_0.1.7       
#> [43] fansi_1.0.6         colorspace_2.1-1    reshape2_1.4.4     
#> [46] rmarkdown_2.29      matrixStats_1.4.1   tools_4.4.2        
#> [49] pkgconfig_2.0.3     htmltools_0.5.8.1

References

Martin, Manon, Benoît Legat, Justine Leenders, Julien Vanwinsberghe, Réjane Rousseau, Bruno Boulanger, Paul H.C. Eilers, Pascal De Tullio, and Bernadette Govaerts. 2018. “PepsNMR for 1H NMR Metabolomic Data Pre-Processing.” Analytica Chimica Acta 1019: 1–13. https://doi.org/10.1016/j.aca.2018.02.067.

Application of PepsNMR on the Human Serum dataset