# Application of PepsNMR on the Human Serum dataset
Author: Manon Martin
Date: 2020-04-27

Manon Martin

#### 2020-04-27

#### Package

PepsNMR 1.6.0

# 1 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.

# 2 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")

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

    library(PepsNMR)

The package development version is available on Github: 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.

# 3 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:

Figure 1: Accepted directory structures for the raw Bruker files

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

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

# 4 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:

Table 1: 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.
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).

# 5 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 automatically installed with PepsNMR.

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] 0-371030i 0-362686i 0-216899i ...
    #>  - 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).

# 6 Demo of the pre-processing functions on the Human Serum dataset

## 6.1 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"

## 6.2 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

Figure 2: Raw FID

## 6.3 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)

Figure 3: Signal before and after the Group Delay removal

## 6.4 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"]]

Figure 4: Signal before and after the Residual Solvent Removal

## 6.5 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)

Figure 5: Signal before and after Apodization

## 6.6 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))

    par(mar = c(4, 4, 1.5, 1), mfrow = c(2, 1))
    plot(time, Re(Fid_data.A[spectrIndex, ]), col = col1, type = "l", ylab = "Intensity",
        xlab = "", main = "FID before Zero Filling")

    time <- as.numeric(colnames(Fid_data.ZF)) * 1000
    plot(time, Re(Fid_data.ZF[spectrIndex, ]), col = col1, type = "l", ylab = "Intensity",
        xlab = expression(paste("Time (", mu, "s)")), main = "FID after Zero Filling")

## 6.7 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)

Figure 6: Spectrum after FT

## 6.8 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)

Figure 7: Spectrum after Zero Order Phase Correction

## 6.9 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
    Spectrum_data.IR <- InternalReferencing(Spectrum_data.ZOPC, Fid_info, ppm.value = target.value)

Figure 8: Spectrum after Internal Referencing

## 6.10 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 = 1e+08,
        p.bc = 0.01)

Figure 9: Spectrum before and after the Baseline Correction

## 6.11 Negative Values Zeroing

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

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

Figure 10: Spectrum after Negative Values Zeroing

## 6.12 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"]]

Figure 11: Spectrum before and after the Baseline Correction

## 6.13 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)

Figure 12: Spectrum after Window Selection

## 6.14 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")

Figure 13: Spectrum before and after Bucketing

## 6.15 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")

Figure 14: Spectrum after Region Removal

## 6.16 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")

Figure 15: 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.

# 7 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"))

Figure 16: 4 first pre-processed spectra

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

Figure 17: PCA scores of the pre-processed spectra

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

Figure 18: PCA loadings of the pre-processed spectra

# 8 Session info

    sessionInfo()
    #> R version 4.0.0 (2020-04-24)
    #> Platform: x86_64-pc-linux-gnu (64-bit)
    #> Running under: Ubuntu 18.04.4 LTS
    #>
    #> Matrix products: default
    #> BLAS:   /home/biocbuild/bbs-3.11-bioc/R/lib/libRblas.so
    #> LAPACK: /home/biocbuild/bbs-3.11-bioc/R/lib/libRlapack.so
    #>
    #> 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
    #>
    #> attached base packages:
    #> [1] stats     graphics  grDevices utils     datasets  methods   base
    #>
    #> other attached packages:
    #> [1] PepsNMRData_1.5.0 PepsNMR_1.6.0     knitr_1.28        BiocStyle_2.16.0
    #>
    #> loaded via a namespace (and not attached):
    #>  [1] Rcpp_1.0.4.6        highr_0.8           plyr_1.8.6
    #>  [4] nloptr_1.2.2.1      compiler_4.0.0      pillar_1.4.3
    #>  [7] formatR_1.7         BiocManager_1.30.10 tools_4.0.0
    #> [10] digest_0.6.25       evaluate_0.14       lifecycle_0.2.0
    #> [13] tibble_3.0.1        gtable_0.3.0        lattice_0.20-41
    #> [16] png_0.1-7           pkgconfig_2.0.3     rlang_0.4.5
    #> [19] Matrix_1.2-18       magick_2.3          yaml_2.2.1
    #> [22] xfun_0.13           gridExtra_2.3       stringr_1.4.0
    #> [25] dplyr_0.8.5         vctrs_0.2.4         grid_4.0.0
    #> [28] tidyselect_1.0.0    glue_1.4.0          R6_2.4.1
    #> [31] ptw_1.9-15          rmarkdown_2.1       bookdown_0.18
    #> [34] farver_2.0.3        reshape2_1.4.4      ggplot2_3.3.0
    #> [37] purrr_0.3.4         magrittr_1.5        matrixStats_0.56.0
    #> [40] scales_1.1.0        htmltools_0.4.0     ellipsis_0.3.0
    #> [43] assertthat_0.2.1    colorspace_1.4-1    labeling_0.3
    #> [46] stringi_1.4.6       munsell_0.5.0       crayon_1.3.4

# 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.