# Introduction to AlpsNMR (older API)

The `AlpsNMR` package was written with two purposes in mind:

- to help data analysts and NMR scientists to work with NMR samples.
- to help IT pipeline builders implement automated methods for preprocessing.

Functions from this package written for data analysts and NMR scientists are prefixed with `nmr_`, while higher level functions written for IT pipeline builders are prefixed with `pipe_`. The main reason why all exported functions have a prefix is to make it easy for the user to discover the functions from the package. By typing nmr_ RStudio will return the list of exported functions. In the R terminal, nmr_ followed by the tab key (⇥) twice will have the same effect. Other popular packages, follow similar approaches (e.g: `forcats`: `fct_*`, `stringr`: `str_*`).

This vignette is written for the first group. It assumes some prior basic knowledge of NMR and data analysis, as well as some basic R programming. In case you are interested in building pipelines with this package, you may want to open the file saved in this directory (run it on your computer):

    pipeline_example <- system.file("pipeline-rmd", "pipeline_example.R", package = "AlpsNMR")
    pipeline_example

    library(BiocParallel)
    library(AlpsNMR)
    library(ggplot2)

# Enable parallellization

This package is able to parallellize several functions through the use of the `BiocParallel` package. Whether to parallelize or not is left to the user that can control the parallellization registering backends. Please check the `BiocParallel` introduction for further details

    library(BiocParallel)
    #register(SerialParam(), default = TRUE)  # disable parallellization
    register(SnowParam(workers = 2, exportglobals = FALSE), default = TRUE)  # enable parallellization with 2 workers

# Data: The `MeOH_plasma_extraction` dataset

To explore the basics of the AlpsNMR package, we have included four NMR samples acquired in a 600 MHz Bruker instrument bundled with the package. The samples are pooled quality control plasma samples, that were extracted with methanol, and therefore only contain small molecules.

If you have installed this package, you can obtain the directory where the four samples are with the command

    MeOH_plasma_extraction_dir <- system.file("dataset-demo", package = "AlpsNMR")
    MeOH_plasma_extraction_dir
    #> [1] "/tmp/Rtmp5CIzwG/Rinst20c63a1d07dd/AlpsNMR/dataset-demo"

The demo directory includes four samples (zipped) and a dummy Excel metadata file.

    fs::dir_ls(MeOH_plasma_extraction_dir)
    #> /tmp/Rtmp5CIzwG/Rinst20c63a1d07dd/AlpsNMR/dataset-demo/10.zip
    #> /tmp/Rtmp5CIzwG/Rinst20c63a1d07dd/AlpsNMR/dataset-demo/20.zip
    #> /tmp/Rtmp5CIzwG/Rinst20c63a1d07dd/AlpsNMR/dataset-demo/30.zip
    #> /tmp/Rtmp5CIzwG/Rinst20c63a1d07dd/AlpsNMR/dataset-demo/README.txt
    #> /tmp/Rtmp5CIzwG/Rinst20c63a1d07dd/AlpsNMR/dataset-demo/dummy_metadata.xlsx

Given the name of the dataset, one may guess that the dataset was used to check the Methanol extraction in serum samples. The dummy metadata consists of dummy information, just for the sake of showing how this package can integrate external metadata. The excel file consists of two tidy tables, in two sheets.

    MeOH_plasma_extraction_xlsx <- file.path(MeOH_plasma_extraction_dir, "dummy_metadata.xlsx")
    exp_subj_id <- readxl::read_excel(MeOH_plasma_extraction_xlsx, sheet = 1)
    subj_id_age <- readxl::read_excel(MeOH_plasma_extraction_xlsx, sheet = 2)
    exp_subj_id
    #> # A tibble: 3 × 3
    #>   NMRExperiment SubjectID TimePoint
    #>   <chr>         <chr>     <chr>
    #> 1 10            Ana       baseline
    #> 2 20            Ana       3 months
    #> 3 30            Elia      baseline
    subj_id_age
    #> # A tibble: 2 × 2
    #>   SubjectID   Age
    #>   <chr>     <dbl>
    #> 1 Ana          29
    #> 2 Elia          0

# Loading samples

The function to read samples is called `nmr_read_samples`. It expects a character vector with the samples to load that can be paths to directories of Bruker format samples or paths to JDX files.

Additionally, this function can filter by pulse sequences (e.g. load only NOESY samples) or loading only metadata.

    zip_files <- fs::dir_ls(MeOH_plasma_extraction_dir, glob = "*.zip")
    zip_files
    #> /tmp/Rtmp5CIzwG/Rinst20c63a1d07dd/AlpsNMR/dataset-demo/10.zip
    #> /tmp/Rtmp5CIzwG/Rinst20c63a1d07dd/AlpsNMR/dataset-demo/20.zip
    #> /tmp/Rtmp5CIzwG/Rinst20c63a1d07dd/AlpsNMR/dataset-demo/30.zip
    dataset <- nmr_read_samples(sample_names = zip_files)
    dataset
    #> An nmr_dataset (3 samples)

As we have not added any metadata to this dataset, the only column we see is the `NMRExperiment`:

    nmr_meta_get(dataset, groups = "external")
    #> # A tibble: 3 × 1
    #>   NMRExperiment
    #>   <chr>
    #> 1 10
    #> 2 20
    #> 3 30

# Adding metadata

Initally our dataset only has the `NMRExperiment` column:

    nmr_meta_get(dataset, groups = "external")
    #> # A tibble: 3 × 1
    #>   NMRExperiment
    #>   <chr>
    #> 1 10
    #> 2 20
    #> 3 30

The `exp_subj_id` table we loaded links the `NMRExperiment` to the `SubjectID`.

As we already have the `NMRExperiment` column, we can use it as the merging column (note that both columns have the same column name to match the metadata such as group class, age, BMI…):

    dataset <- nmr_meta_add(dataset, metadata = exp_subj_id, by = "NMRExperiment")
    nmr_meta_get(dataset, groups = "external")
    #> # A tibble: 3 × 3
    #>   NMRExperiment SubjectID TimePoint
    #>   <chr>         <chr>     <chr>
    #> 1 10            Ana       baseline
    #> 2 20            Ana       3 months
    #> 3 30            Elia      baseline

If we have info from different files we can match them. For instance, now we have the `SubjectID` information so we can add the table that adds the `SubjectID` to the `Age`.

    dataset <- nmr_meta_add(dataset, metadata = subj_id_age, by = "SubjectID")
    nmr_meta_get(dataset, groups = "external")
    #> # A tibble: 3 × 4
    #>   NMRExperiment SubjectID TimePoint   Age
    #>   <chr>         <chr>     <chr>     <dbl>
    #> 1 10            Ana       baseline     29
    #> 2 20            Ana       3 months     29
    #> 3 30            Elia      baseline      0

Now we have our metadata integrated in the dataset and we can make use of it in further data analysis steps.

# Interpolation

1D NMR samples can be interpolated together, in order to arrange all the spectra into a matrix, with one row per sample. The main parameters we would need is the range of ppm values that we want to interpolate and the resolution.

We can see the ppm resolution by looking at the ppm axis of one sample:

    ppm_res <- nmr_ppm_resolution(dataset)[[1]]
    message("The ppm resolution is: ", format(ppm_res, digits = 2), " ppm")
    #> The ppm resolution is: 0.00023 ppm

We can interpolate the dataset, obtaining an `nmr_dataset_1D` object:

    dataset <- nmr_interpolate_1D(dataset, axis = c(min = -0.5, max = 10, by = 2.3E-4))

This operation changes the class of the object, as now the data is on a matrix. The dataset is now of class `nmr_dataset_1D`. The `axis` element is now a numeric vector and the `data_1r` element is a matrix.

# Plotting samples

The `AlpsNMR` package offers the possibility to plot `nmr_dataset_1D` objects. Plotting many spectra with so many points is quite expensive so it is possible to include only some regions of the spectra or plot only some samples.

Use `?plot.nmr_dataset_1D` to check the parameters, among them:

- `NMRExperiment`: A character vector with the NMR experiments to plot
- `chemshift_range`: A ppm range to plot only a small region, or to reduce the resolution
- `interactive`: To make the plot interactive - `...`: Can be used to pass additional parameters such as `color = "SubjectID"` that are passed as aesthetics to ggplot.

    plot(dataset, NMRExperiment = c("10", "30"), chemshift_range = c(2.2, 2.8))

## Creating interactive plots

The option `interactive = TRUE` described above has some performance limitations. As high performance workaround, you can make many plots interactive with the function `plot_interactive`.

This function will use WebGL technologies to create a webpage that, once opened, allows you to interact with the plot.

Due to technical limitations, these plots need to be opened manually and can’t be embedded in RMarkdown documents. Therefore, the function saves the plot in the directory for further exploration. Additionally, some old web browsers may not be able to display these interactive plots correctly.

    plt <- plot(dataset, NMRExperiment = c("10", "30"), chemshift_range = c(2.2, 2.8))
    plot_interactive(plt, "plot_region.html")

# Exclude regions

Some regions can easily be excluded from the spectra with `nmr_exclude_region`. Note that the regions are fully removed and not zeroed, as using zeros complicates a lot the implementation1 and has little advantages.

    regions_to_exclude <- list(water = c(4.6, 5), methanol = c(3.33, 3.39))
    dataset <- nmr_exclude_region(dataset, exclude = regions_to_exclude)
    plot(dataset, chemshift_range = c(4.2, 5.5))

# Filter samples

Maybe we just want to analyze a subset of the data, e.g., only a class group or a particular gender. We can filter some samples according to their metadata as follows:

    samples_10_20 <- filter(dataset, SubjectID == "Ana")
    nmr_meta_get(samples_10_20, groups = "external")
    #> # A tibble: 2 × 4
    #>   NMRExperiment SubjectID TimePoint   Age
    #>   <chr>         <chr>     <chr>     <dbl>
    #> 1 10            Ana       baseline     29
    #> 2 20            Ana       3 months     29

# Robust PCA for outlier detection

The AlpsNMR package includes robust PCA analysis for outlier detection. With such a small demo dataset, it is not practical to use, but check out the documentation of `nmr_pca_outliers_*` functions.

    pca_outliers_rob <- nmr_pca_outliers_robust(dataset, ncomp = 3)
    nmr_pca_outliers_plot(dataset, pca_outliers_rob)

# Baseline removal

Spectra may display an unstable baseline, specially when processing blood/fecal blood/fecal samples. If so, `nmr_baseline_removal` subtract the baseline by means of Asymmetric Least Squares method.

See before:

    plot(dataset, chemshift_range = c(3.5,3.8))

And after:

    dataset = nmr_baseline_removal(dataset, lambda = 6, p = 0.01)
    plot(dataset, chemshift_range = c(3.5,3.8))

# Peak detection

The peak detection is performed on short spectra segments using a continuous wavelet transform. See `?nmr_detect_peaks` for more information.

Our current approach relies on the use of the baseline threshold (`baselineThresh`) automatic calculated (see `?nmr_baseline_threshold`) and the Signal to Noise Threshold (`SNR.Th`) to discriminate valid peaks from noise.

The combination of the `baselineThresh` and the `SNR.Th` optimizes the number of actual peaks from noise.

The advantage of the `SNR.Th` method is that it estimates the noise level on each spectra region independently, so in practice it can be used as a dynamic baseline threshold level.

    peak_table <- nmr_detect_peaks(dataset,
                                   nDivRange_ppm = 0.1,
                                   scales = seq(1, 16, 2),
                                   baselineThresh = NULL, SNR.Th = 3)
    NMRExp_ref <- nmr_align_find_ref(dataset, peak_table)
    message("Your reference is NMRExperiment ", NMRExp_ref)
    #> Your reference is NMRExperiment 30
    nmr_detect_peaks_plot(dataset, peak_table, NMRExperiment = "20", chemshift_range = c(3.5,3.8))

# Spectra alignment

To align the sample, we use the `nmr_align` function, which in turn uses a hierarchical clustering method (see `?nmr_align` for further details).

The `maxShift_ppm` limits the maximum shift allowed for the spectra.

    nmr_exp_ref <- nmr_align_find_ref(dataset, peak_table)
    dataset_align <- nmr_align(dataset, peak_table, nmr_exp_ref, maxShift_ppm = 0.0015, acceptLostPeak = FALSE)

    plot(dataset, chemshift_range = c(3.025, 3.063))
    plot(dataset_align, chemshift_range = c(3.025, 3.063))

# Normalization

There are multiple normalization techniques available. The most strongly recommended is the `pqn` normalization, but it may not be fully reliable when the number of samples is small, as it needs a computation of the median spectra. Nevertheless, it is possible to compute it:

    dataset_norm <- nmr_normalize(dataset_align, method = "pqn")
    #> Warning: There are not enough samples for reliably estimating the median spectra
    #> ℹ The Probabalistic Quotient Normalization requires several samples to compute the median spectra. Your number of samples is low
    #> ℹ Review your peaks before and after normalization to ensure there are no big distortions

The `AlpsNMR` package offers the possibility to extract additional normalization information with `nmr_normalize_extra_info(dataset)`, to explore the normalization factors applied to each sample:

The plot shows the dispersion with respect to the median of the normalization factors, and can highlight samples with abnormaly large or small normalization factors.

    diagnostic <- nmr_normalize_extra_info(dataset_norm)
    diagnostic$norm_factor
    #>   NMRExperiment norm_factor norm_factor_norm
    #> 1            10   0.8706322        0.8706322
    #> 2            20   1.1523450        1.1523450
    #> 3            30   1.0000000        1.0000000
    diagnostic$plot

# Peak integration

## 1. Integration based on peak center and width

If we want to integrate the whole spectra, we need ppm from the `peak_table`. See `Peak detection` section. The function `nmr_integrate_peak_positions` generates a new `nmr_dataset_1D` object containing the integrals from the `peak_table` (ppm values corresponding to detected peaks).

    peak_table_integration = nmr_integrate_peak_positions(
      samples = dataset_norm,
      peak_pos_ppm = peak_table$ppm,
      peak_width_ppm = 0.006)
    #> New names:
    #> • `ppm_-0.0002` -> `ppm_-0.0002...6`
    #> • `ppm_0.8607` -> `ppm_0.8607...22`
    #> • `ppm_0.8655` -> `ppm_0.8655...23`
    #> • `ppm_0.8729` -> `ppm_0.8729...24`
    #> • `ppm_0.8784` -> `ppm_0.8784...25`
    #> • `ppm_0.8910` -> `ppm_0.8910...26`
    #> • `ppm_0.9032` -> `ppm_0.9032...27`
    #> • `ppm_0.9085` -> `ppm_0.9085...28`
    #> • `ppm_0.9196` -> `ppm_0.9196...30`
    #> • `ppm_0.9313` -> `ppm_0.9313...31`
    #> • `ppm_0.9437` -> `ppm_0.9437...32`
    #> • `ppm_0.9554` -> `ppm_0.9554...33`
    #> • `ppm_0.9658` -> `ppm_0.9658...34`
    #> • `ppm_0.9902` -> `ppm_0.9902...36`
    #> • `ppm_1.0019` -> `ppm_1.0019...37`
    #> • `ppm_1.0093` -> `ppm_1.0093...38`
    #> • `ppm_1.0208` -> `ppm_1.0208...39`
    #> • `ppm_1.0410` -> `ppm_1.0410...40`
    #> • `ppm_1.0716` -> `ppm_1.0716...43`
    #> • `ppm_1.0803` -> `ppm_1.0803...44`
    #> • `ppm_1.1505` -> `ppm_1.1505...46`
    #> • `ppm_1.1979` -> `ppm_1.1979...47`
    #> • `ppm_1.2084` -> `ppm_1.2084...48`
    #> • `ppm_1.2179` -> `ppm_1.2179...49`
    #> • `ppm_1.3262` -> `ppm_1.3262...54`
    #> • `ppm_1.3377` -> `ppm_1.3377...55`
    #> • `ppm_1.4424` -> `ppm_1.4424...57`
    #> • `ppm_1.4913` -> `ppm_1.4913...59`
    #> • `ppm_1.5693` -> `ppm_1.5693...66`
    #> • `ppm_1.6195` -> `ppm_1.6195...72`
    #> • `ppm_1.6443` -> `ppm_1.6443...75`
    #> • `ppm_1.6562` -> `ppm_1.6562...77`
    #> • `ppm_1.6799` -> `ppm_1.6799...80`
    #> • `ppm_1.6990` -> `ppm_1.6990...86`
    #> • `ppm_1.7351` -> `ppm_1.7351...92`
    #> • `ppm_1.7614` -> `ppm_1.7614...95`
    #> • `ppm_1.8131` -> `ppm_1.8131...97`
    #> • `ppm_1.8748` -> `ppm_1.8748...103`
    #> • `ppm_1.9210` -> `ppm_1.9210...106`
    #> • `ppm_1.9313` -> `ppm_1.9313...107`
    #> • `ppm_2.0026` -> `ppm_2.0026...114`
    #> • `ppm_2.0194` -> `ppm_2.0194...117`
    #> • `ppm_2.0307` -> `ppm_2.0307...118`
    #> • `ppm_2.0422` -> `ppm_2.0422...119`
    #> • `ppm_2.0806` -> `ppm_2.0806...123`
    #> • `ppm_2.1149` -> `ppm_2.1149...126`
    #> • `ppm_2.1192` -> `ppm_2.1192...127`
    #> • `ppm_2.1273` -> `ppm_2.1273...128`
    #> • `ppm_2.1324` -> `ppm_2.1324...129`
    #> • `ppm_2.1572` -> `ppm_2.1572...134`
    #> • `ppm_2.1655` -> `ppm_2.1655...135`
    #> • `ppm_2.2835` -> `ppm_2.2835...143`
    #> • `ppm_2.2950` -> `ppm_2.2950...144`
    #> • `ppm_2.3251` -> `ppm_2.3251...147`
    #> • `ppm_2.3451` -> `ppm_2.3451...149`
    #> • `ppm_2.3513` -> `ppm_2.3513...150`
    #> • `ppm_2.3575` -> `ppm_2.3575...151`
    #> • `ppm_2.3644` -> `ppm_2.3644...152`
    #> • `ppm_2.3711` -> `ppm_2.3711...153`
    #> • `ppm_2.4196` -> `ppm_2.4196...157`
    #> • `ppm_2.4912` -> `ppm_2.4912...166`
    #> • `ppm_2.4953` -> `ppm_2.4953...167`
    #> • `ppm_2.5284` -> `ppm_2.5284...171`
    #> • `ppm_2.6216` -> `ppm_2.6216...177`
    #> • `ppm_2.7577` -> `ppm_2.7577...188`
    #> • `ppm_2.9332` -> `ppm_2.9332...199`
    #> • `ppm_3.0305` -> `ppm_3.0305...206`
    #> • `ppm_3.0404` -> `ppm_3.0404...207`
    #> • `ppm_3.0489` -> `ppm_3.0489...208`
    #> • `ppm_3.2081` -> `ppm_3.2081...218`
    #> • `ppm_3.2154` -> `ppm_3.2154...219`
    #> • `ppm_3.2320` -> `ppm_3.2320...220`
    #> • `ppm_3.2382` -> `ppm_3.2382...221`
    #> • `ppm_3.2672` -> `ppm_3.2672...223`
    #> • `ppm_3.2706` -> `ppm_3.2706...224`
    #> • `ppm_3.4031` -> `ppm_3.4031...228`
    #> • `ppm_3.4243` -> `ppm_3.4243...231`
    #> • `ppm_3.4829` -> `ppm_3.4829...238`
    #> • `ppm_3.4981` -> `ppm_3.4981...240`
    #> • `ppm_3.5135` -> `ppm_3.5135...241`
    #> • `ppm_3.5372` -> `ppm_3.5372...243`
    #> • `ppm_3.7042` -> `ppm_3.7042...254`
    #> • `ppm_3.7141` -> `ppm_3.7141...255`
    #> • `ppm_3.7200` -> `ppm_3.7200...256`
    #> • `ppm_3.7237` -> `ppm_3.7237...257`
    #> • `ppm_3.7348` -> `ppm_3.7348...258`
    #> • `ppm_3.7442` -> `ppm_3.7442...259`
    #> • `ppm_3.7757` -> `ppm_3.7757...262`
    #> • `ppm_3.8233` -> `ppm_3.8233...265`
    #> • `ppm_3.8438` -> `ppm_3.8438...268`
    #> • `ppm_3.8528` -> `ppm_3.8528...270`
    #> • `ppm_3.8886` -> `ppm_3.8886...273`
    #> • `ppm_3.8923` -> `ppm_3.8923...274`
    #> • `ppm_3.9091` -> `ppm_3.9091...275`
    #> • `ppm_3.9128` -> `ppm_3.9128...276`
    #> • `ppm_3.9333` -> `ppm_3.9333...277`
    #> • `ppm_3.9493` -> `ppm_3.9493...278`
    #> • `ppm_3.9917` -> `ppm_3.9917...282`
    #> • `ppm_4.0020` -> `ppm_4.0020...283`
    #> • `ppm_4.0108` -> `ppm_4.0108...284`
    #> • `ppm_4.1205` -> `ppm_4.1205...288`
    #> • `ppm_4.1320` -> `ppm_4.1320...289`
    #> • `ppm_4.1927` -> `ppm_4.1927...291`
    #> • `ppm_4.2281` -> `ppm_4.2281...293`
    #> • `ppm_4.2398` -> `ppm_4.2398...295`
    #> • `ppm_4.2566` -> `ppm_4.2566...299`
    #> • `ppm_4.4376` -> `ppm_4.4376...315`
    #> • `ppm_4.4531` -> `ppm_4.4531...317`
    #> • `ppm_4.5218` -> `ppm_4.5218...320`
    #> • `ppm_4.5221` -> `ppm_4.5221...321`
    #> • `ppm_4.5906` -> `ppm_4.5906...324`
    #> • `ppm_4.5991` -> `ppm_4.5991...325`
    #> • `ppm_5.2369` -> `ppm_5.2369...329`
    #> • `ppm_5.4154` -> `ppm_5.4154...336`
    #> • `ppm_6.1026` -> `ppm_6.1026...350`
    #> • `ppm_6.9009` -> `ppm_6.9009...361`
    #> • `ppm_6.9154` -> `ppm_6.9154...362`
    #> • `ppm_7.2894` -> `ppm_7.2894...372`
    #> • `ppm_7.3014` -> `ppm_7.3014...373`
    #> • `ppm_7.3409` -> `ppm_7.3409...376`
    #> • `ppm_7.3816` -> `ppm_7.3816...380`
    #> • `ppm_7.4881` -> `ppm_7.4881...387`
    #> • `ppm_7.5553` -> `ppm_7.5553...390`
    #> • `ppm_8.2458` -> `ppm_8.2458...407`
    #> • `ppm_8.3515` -> `ppm_8.3515...409`
    #> • `ppm_8.4608` -> `ppm_8.4608...411`
    #> • `ppm_-0.0002` -> `ppm_-0.0002...422`
    #> • `ppm_0.8110` -> `ppm_0.8110...438`
    #> • `ppm_0.8552` -> `ppm_0.8552...442`
    #> • `ppm_0.8607` -> `ppm_0.8607...443`
    #> • `ppm_0.8784` -> `ppm_0.8784...446`
    #> • `ppm_0.8910` -> `ppm_0.8910...447`
    #> • `ppm_0.9085` -> `ppm_0.9085...449`
    #> • `ppm_0.9196` -> `ppm_0.9196...450`
    #> • `ppm_0.9313` -> `ppm_0.9313...451`
    #> • `ppm_0.9437` -> `ppm_0.9437...452`
    #> • `ppm_0.9554` -> `ppm_0.9554...453`
    #> • `ppm_0.9658` -> `ppm_0.9658...454`
    #> • `ppm_1.0093` -> `ppm_1.0093...458`
    #> • `ppm_1.0205` -> `ppm_1.0205...459`
    #> • `ppm_1.0716` -> `ppm_1.0716...463`
    #> • `ppm_1.0803` -> `ppm_1.0803...464`
    #> • `ppm_1.1505` -> `ppm_1.1505...468`
    #> • `ppm_1.2084` -> `ppm_1.2084...470`
    #> • `ppm_1.2179` -> `ppm_1.2179...471`
    #> • `ppm_1.2294` -> `ppm_1.2294...472`
    #> • `ppm_1.3262` -> `ppm_1.3262...476`
    #> • `ppm_1.3377` -> `ppm_1.3377...477`
    #> • `ppm_1.4308` -> `ppm_1.4308...478`
    #> • `ppm_1.4424` -> `ppm_1.4424...479`
    #> • `ppm_1.4794` -> `ppm_1.4794...480`
    #> • `ppm_1.5693` -> `ppm_1.5693...487`
    #> • `ppm_1.6195` -> `ppm_1.6195...491`
    #> • `ppm_1.6319` -> `ppm_1.6319...492`
    #> • `ppm_1.6441` -> `ppm_1.6441...494`
    #> • `ppm_1.6560` -> `ppm_1.6560...495`
    #> • `ppm_1.6562` -> `ppm_1.6562...496`
    #> • `ppm_1.6675` -> `ppm_1.6675...498`
    #> • `ppm_1.6747` -> `ppm_1.6747...499`
    #> • `ppm_1.6799` -> `ppm_1.6799...501`
    #> • `ppm_1.6990` -> `ppm_1.6990...503`
    #> • `ppm_1.7075` -> `ppm_1.7075...504`
    #> • `ppm_1.7614` -> `ppm_1.7614...511`
    #> • `ppm_1.8752` -> `ppm_1.8752...522`
    #> • `ppm_1.9812` -> `ppm_1.9812...530`
    #> • `ppm_2.0422` -> `ppm_2.0422...537`
    #> • `ppm_2.0923` -> `ppm_2.0923...542`
    #> • `ppm_2.1192` -> `ppm_2.1192...545`
    #> • `ppm_2.1273` -> `ppm_2.1273...546`
    #> • `ppm_2.1324` -> `ppm_2.1324...547`
    #> • `ppm_2.1572` -> `ppm_2.1572...550`
    #> • `ppm_2.1655` -> `ppm_2.1655...551`
    #> • `ppm_2.2515` -> `ppm_2.2515...558`
    #> • `ppm_2.2717` -> `ppm_2.2717...561`
    #> • `ppm_2.3451` -> `ppm_2.3451...570`
    #> • `ppm_2.3513` -> `ppm_2.3513...571`
    #> • `ppm_2.3575` -> `ppm_2.3575...572`
    #> • `ppm_2.3644` -> `ppm_2.3644...573`
    #> • `ppm_2.4063` -> `ppm_2.4063...577`
    #> • `ppm_2.4196` -> `ppm_2.4196...578`
    #> • `ppm_2.4456` -> `ppm_2.4456...581`
    #> • `ppm_2.4953` -> `ppm_2.4953...586`
    #> • `ppm_2.5284` -> `ppm_2.5284...590`
    #> • `ppm_2.7994` -> `ppm_2.7994...606`
    #> • `ppm_2.9339` -> `ppm_2.9339...615`
    #> • `ppm_3.2154` -> `ppm_3.2154...638`
    #> • `ppm_3.2706` -> `ppm_3.2706...643`
    #> • `ppm_3.3930` -> `ppm_3.3930...646`
    #> • `ppm_3.4185` -> `ppm_3.4185...648`
    #> • `ppm_3.5793` -> `ppm_3.5793...663`
    #> • `ppm_3.6428` -> `ppm_3.6428...668`
    #> • `ppm_3.6499` -> `ppm_3.6499...669`
    #> • `ppm_3.7237` -> `ppm_3.7237...673`
    #> • `ppm_3.7348` -> `ppm_3.7348...674`
    #> • `ppm_3.7642` -> `ppm_3.7642...677`
    #> • `ppm_3.7757` -> `ppm_3.7757...678`
    #> • `ppm_3.8438` -> `ppm_3.8438...683`
    #> • `ppm_3.8923` -> `ppm_3.8923...689`
    #> • `ppm_3.9128` -> `ppm_3.9128...691`
    #> • `ppm_3.9917` -> `ppm_3.9917...698`
    #> • `ppm_4.0020` -> `ppm_4.0020...699`
    #> • `ppm_4.2281` -> `ppm_4.2281...712`
    #> • `ppm_4.2398` -> `ppm_4.2398...714`
    #> • `ppm_4.2566` -> `ppm_4.2566...717`
    #> • `ppm_4.5089` -> `ppm_4.5089...735`
    #> • `ppm_4.5092` -> `ppm_4.5092...736`
    #> • `ppm_4.5221` -> `ppm_4.5221...737`
    #> • `ppm_4.5991` -> `ppm_4.5991...742`
    #> • `ppm_5.0973` -> `ppm_5.0973...744`
    #> • `ppm_5.4154` -> `ppm_5.4154...753`
    #> • `ppm_5.4220` -> `ppm_5.4220...754`
    #> • `ppm_6.1026` -> `ppm_6.1026...771`
    #> • `ppm_6.9154` -> `ppm_6.9154...790`
    #> • `ppm_6.9626` -> `ppm_6.9626...791`
    #> • `ppm_7.2066` -> `ppm_7.2066...798`
    #> • `ppm_7.3014` -> `ppm_7.3014...803`
    #> • `ppm_7.3409` -> `ppm_7.3409...806`
    #> • `ppm_7.4881` -> `ppm_7.4881...816`
    #> • `ppm_7.5415` -> `ppm_7.5415...819`
    #> • `ppm_8.2458` -> `ppm_8.2458...839`
    #> • `ppm_8.4608` -> `ppm_8.4608...842`
    #> • `ppm_-0.0002` -> `ppm_-0.0002...853`
    #> • `ppm_0.8110` -> `ppm_0.8110...865`
    #> • `ppm_0.8552` -> `ppm_0.8552...869`
    #> • `ppm_0.8655` -> `ppm_0.8655...871`
    #> • `ppm_0.8729` -> `ppm_0.8729...872`
    #> • `ppm_0.9032` -> `ppm_0.9032...875`
    #> • `ppm_0.9085` -> `ppm_0.9085...876`
    #> • `ppm_0.9196` -> `ppm_0.9196...877`
    #> • `ppm_0.9313` -> `ppm_0.9313...878`
    #> • `ppm_0.9902` -> `ppm_0.9902...883`
    #> • `ppm_1.0019` -> `ppm_1.0019...884`
    #> • `ppm_1.0205` -> `ppm_1.0205...886`
    #> • `ppm_1.0208` -> `ppm_1.0208...887`
    #> • `ppm_1.0410` -> `ppm_1.0410...888`
    #> • `ppm_1.0716` -> `ppm_1.0716...891`
    #> • `ppm_1.0803` -> `ppm_1.0803...892`
    #> • `ppm_1.1505` -> `ppm_1.1505...896`
    #> • `ppm_1.1979` -> `ppm_1.1979...897`
    #> • `ppm_1.2084` -> `ppm_1.2084...898`
    #> • `ppm_1.2179` -> `ppm_1.2179...899`
    #> • `ppm_1.2294` -> `ppm_1.2294...900`
    #> • `ppm_1.3262` -> `ppm_1.3262...904`
    #> • `ppm_1.3377` -> `ppm_1.3377...905`
    #> • `ppm_1.4308` -> `ppm_1.4308...906`
    #> • `ppm_1.4424` -> `ppm_1.4424...907`
    #> • `ppm_1.4794` -> `ppm_1.4794...908`
    #> • `ppm_1.4913` -> `ppm_1.4913...909`
    #> • `ppm_1.6319` -> `ppm_1.6319...919`
    #> • `ppm_1.6441` -> `ppm_1.6441...920`
    #> • `ppm_1.6443` -> `ppm_1.6443...921`
    #> • `ppm_1.6560` -> `ppm_1.6560...922`
    #> • `ppm_1.6562` -> `ppm_1.6562...923`
    #> • `ppm_1.6675` -> `ppm_1.6675...924`
    #> • `ppm_1.6747` -> `ppm_1.6747...925`
    #> • `ppm_1.6799` -> `ppm_1.6799...927`
    #> • `ppm_1.6990` -> `ppm_1.6990...930`
    #> • `ppm_1.7075` -> `ppm_1.7075...933`
    #> • `ppm_1.7351` -> `ppm_1.7351...937`
    #> • `ppm_1.7614` -> `ppm_1.7614...940`
    #> • `ppm_1.8131` -> `ppm_1.8131...945`
    #> • `ppm_1.8748` -> `ppm_1.8748...952`
    #> • `ppm_1.8752` -> `ppm_1.8752...953`
    #> • `ppm_1.9210` -> `ppm_1.9210...956`
    #> • `ppm_1.9313` -> `ppm_1.9313...957`
    #> • `ppm_1.9812` -> `ppm_1.9812...962`
    #> • `ppm_2.0026` -> `ppm_2.0026...964`
    #> • `ppm_2.0194` -> `ppm_2.0194...967`
    #> • `ppm_2.0307` -> `ppm_2.0307...968`
    #> • `ppm_2.0806` -> `ppm_2.0806...974`
    #> • `ppm_2.0923` -> `ppm_2.0923...975`
    #> • `ppm_2.1149` -> `ppm_2.1149...977`
    #> • `ppm_2.1192` -> `ppm_2.1192...978`
    #> • `ppm_2.1273` -> `ppm_2.1273...979`
    #> • `ppm_2.2515` -> `ppm_2.2515...992`
    #> • `ppm_2.2717` -> `ppm_2.2717...995`
    #> • `ppm_2.2835` -> `ppm_2.2835...997`
    #> • `ppm_2.2950` -> `ppm_2.2950...999`
    #> • `ppm_2.3251` -> `ppm_2.3251...1002`
    #> • `ppm_2.3513` -> `ppm_2.3513...1005`
    #> • `ppm_2.3575` -> `ppm_2.3575...1006`
    #> • `ppm_2.3711` -> `ppm_2.3711...1008`
    #> • `ppm_2.4063` -> `ppm_2.4063...1011`
    #> • `ppm_2.4196` -> `ppm_2.4196...1012`
    #> • `ppm_2.4456` -> `ppm_2.4456...1015`
    #> • `ppm_2.4912` -> `ppm_2.4912...1020`
    #> • `ppm_2.6216` -> `ppm_2.6216...1029`
    #> • `ppm_2.7577` -> `ppm_2.7577...1039`
    #> • `ppm_2.7994` -> `ppm_2.7994...1041`
    #> • `ppm_2.9332` -> `ppm_2.9332...1050`
    #> • `ppm_2.9339` -> `ppm_2.9339...1051`
    #> • `ppm_3.0305` -> `ppm_3.0305...1058`
    #> • `ppm_3.0404` -> `ppm_3.0404...1059`
    #> • `ppm_3.0489` -> `ppm_3.0489...1060`
    #> • `ppm_3.2081` -> `ppm_3.2081...1070`
    #> • `ppm_3.2320` -> `ppm_3.2320...1072`
    #> • `ppm_3.2382` -> `ppm_3.2382...1073`
    #> • `ppm_3.2672` -> `ppm_3.2672...1075`
    #> • `ppm_3.3930` -> `ppm_3.3930...1079`
    #> • `ppm_3.4031` -> `ppm_3.4031...1080`
    #> • `ppm_3.4185` -> `ppm_3.4185...1082`
    #> • `ppm_3.4243` -> `ppm_3.4243...1083`
    #> • `ppm_3.4829` -> `ppm_3.4829...1090`
    #> • `ppm_3.4981` -> `ppm_3.4981...1092`
    #> • `ppm_3.5135` -> `ppm_3.5135...1093`
    #> • `ppm_3.5372` -> `ppm_3.5372...1095`
    #> • `ppm_3.5793` -> `ppm_3.5793...1099`
    #> • `ppm_3.6428` -> `ppm_3.6428...1104`
    #> • `ppm_3.6499` -> `ppm_3.6499...1105`
    #> • `ppm_3.7042` -> `ppm_3.7042...1106`
    #> • `ppm_3.7141` -> `ppm_3.7141...1107`
    #> • `ppm_3.7200` -> `ppm_3.7200...1108`
    #> • `ppm_3.7348` -> `ppm_3.7348...1110`
    #> • `ppm_3.7442` -> `ppm_3.7442...1111`
    #> • `ppm_3.7642` -> `ppm_3.7642...1113`
    #> • `ppm_3.8233` -> `ppm_3.8233...1117`
    #> • `ppm_3.8528` -> `ppm_3.8528...1121`
    #> • `ppm_3.8886` -> `ppm_3.8886...1124`
    #> • `ppm_3.9091` -> `ppm_3.9091...1126`
    #> • `ppm_3.9333` -> `ppm_3.9333...1128`
    #> • `ppm_3.9493` -> `ppm_3.9493...1129`
    #> • `ppm_4.0108` -> `ppm_4.0108...1136`
    #> • `ppm_4.1205` -> `ppm_4.1205...1141`
    #> • `ppm_4.1320` -> `ppm_4.1320...1142`
    #> • `ppm_4.1927` -> `ppm_4.1927...1145`
    #> • `ppm_4.2281` -> `ppm_4.2281...1148`
    #> • `ppm_4.2566` -> `ppm_4.2566...1154`
    #> • `ppm_4.4376` -> `ppm_4.4376...1166`
    #> • `ppm_4.4531` -> `ppm_4.4531...1168`
    #> • `ppm_4.5089` -> `ppm_4.5089...1171`
    #> • `ppm_4.5092` -> `ppm_4.5092...1172`
    #> • `ppm_4.5218` -> `ppm_4.5218...1173`
    #> • `ppm_4.5906` -> `ppm_4.5906...1178`
    #> • `ppm_5.0973` -> `ppm_5.0973...1184`
    #> • `ppm_5.2369` -> `ppm_5.2369...1187`
    #> • `ppm_5.4220` -> `ppm_5.4220...1195`
    #> • `ppm_6.9009` -> `ppm_6.9009...1225`
    #> • `ppm_6.9154` -> `ppm_6.9154...1226`
    #> • `ppm_6.9626` -> `ppm_6.9626...1227`
    #> • `ppm_7.2066` -> `ppm_7.2066...1234`
    #> • `ppm_7.2894` -> `ppm_7.2894...1237`
    #> • `ppm_7.3409` -> `ppm_7.3409...1241`
    #> • `ppm_7.3816` -> `ppm_7.3816...1243`
    #> • `ppm_7.5415` -> `ppm_7.5415...1250`
    #> • `ppm_7.5553` -> `ppm_7.5553...1251`
    #> • `ppm_8.3515` -> `ppm_8.3515...1273`
    #> • `ppm_8.4608` -> `ppm_8.4608...1274`

    peak_table_integration = get_integration_with_metadata(peak_table_integration)

We can also integrate with a specific peak position and some arbitrary width:

    nmr_data(
      nmr_integrate_peak_positions(samples = dataset_norm,
                                peak_pos_ppm = c(4.1925, 4.183, 4.1775, 4.17),
                                peak_width_ppm = 0.006)
    )
    #>    ppm_4.1925 ppm_4.1830 ppm_4.1775 ppm_4.1700
    #> 10   47.88901   53.53241   50.49386   45.62978
    #> 20   44.18572   51.07377   49.52798   44.53178
    #> 30   42.39196   47.99196   46.34812   39.07835

## 2. Integration based on peak boundaries

Imagine we only want to integrate the four peaks corresponding to the pyroglutamic acid:

    pyroglutamic_acid_region <- c(4.15, 4.20)
    plot(dataset_norm, chemshift_range = pyroglutamic_acid_region) +
      ggplot2::ggtitle("Pyroglutamic acid region")

We define the peak regions and integrate them. Note how we can correct the baseline or not. If we correct the baseline, the limits of the integration will be connected with a straight line and that line will be used as the baseline, that will be subtracted.

    pyroglutamic_acid <- list(pyroglutamic_acid1 = c(4.19, 4.195),
                              pyroglutamic_acid2 = c(4.18, 4.186),
                              pyroglutamic_acid3 = c(4.175, 4.18),
                              pyroglutamic_acid4 = c(4.165, 4.172))
    regions_basel_corr_ds <- nmr_integrate_regions(dataset_norm, pyroglutamic_acid, fix_baseline = TRUE)
    regions_basel_corr_matrix <- nmr_data(regions_basel_corr_ds)
    regions_basel_corr_matrix
    #>    pyroglutamic_acid1 pyroglutamic_acid2 pyroglutamic_acid3 pyroglutamic_acid4
    #> 10           15.61810           20.36002          16.018972           32.99148
    #> 20           12.89344           10.63232           7.196728           30.74094
    #> 30           12.65231           14.56388          11.226573           31.03655

    regions_basel_not_corr_ds <- nmr_integrate_regions(dataset_norm, pyroglutamic_acid, fix_baseline = FALSE)
    regions_basel_not_corr_matrix <- nmr_data(regions_basel_not_corr_ds)
    regions_basel_not_corr_matrix
    #>    pyroglutamic_acid1 pyroglutamic_acid2 pyroglutamic_acid3 pyroglutamic_acid4
    #> 10           43.51763           53.53241           43.93533           52.67311
    #> 20           39.61702           51.07377           42.00336           53.07528
    #> 30           38.10972           47.99196           39.99623           46.40013

We may plot the integral values to explore variation based on the baseline subtraction.

    dplyr::bind_rows(
      regions_basel_corr_matrix %>%
        as.data.frame() %>%
        tibble::rownames_to_column("NMRExperiment") %>%
        tidyr::gather("metabolite_peak", "area", -NMRExperiment) %>%
        dplyr::mutate(BaselineCorrected = TRUE),
      regions_basel_not_corr_matrix %>%
        as.data.frame() %>%
        tibble::rownames_to_column("NMRExperiment") %>%
        tidyr::gather("metabolite_peak", "area", -NMRExperiment) %>%
        dplyr::mutate(BaselineCorrected = FALSE)
    ) %>% ggplot() + geom_point(aes(x = NMRExperiment, y = area, color = metabolite_peak)) +
      facet_wrap(~BaselineCorrected)

# Identification

After applying any feature selection or machine learning, Alps allows the identification of features of interest through `nmr_identify_regions_blood`. The function gives 3 possibilities sorted by the most probable metabolite (see `nmr_identify_regions_blood` for details).

    ppm_to_assign <- c(4.060960203, 3.048970634,2.405935596,0.990616851,0.986520147, 1.044258467)
    identification <- nmr_identify_regions_blood (ppm_to_assign)
    identification[!is.na(identification$Metabolite), ]
    #>                     Metabolite HMDB_code Shift_ppm Type  J_Hz Height
    #> 1781                  L-Valine HMDB00883     0.991    d  7.01 1.0000
    #> 1031 L-Alpha-aminobutyric acid HMDB00452     0.997    t  7.58 1.0000
    #> 178                   L-Valine HMDB00883     0.991    d  7.01 1.0000
    #> 103  L-Alpha-aminobutyric acid HMDB00452     0.997    t  7.58 1.0000
    #> 179                   L-Valine HMDB00883     1.044    d  7.05 0.9614
    #> 195          Pyroglutamic acid HMDB00267     2.405    m       0.7466
    #> 201              Succinic acid HMDB00254     2.405    s       1.0000
    #> 148                   L-Lysine HMDB00182     3.035    t       1.0000
    #> 47                  Creatinine HMDB00562     3.045    s       1.0000
    #> 44                    Creatine HMDB00064     3.035    s       1.0000
    #> 46                  Creatinine HMDB00562     4.065    s       0.4374
    #> 182                Myoinositol HMDB00211     4.068    t 2.839     NA
    #> 40                     Choline HMDB00097     4.071  ddd       0.0607
    #>      Blood_concentration n_reported_in_Blood ppm_to_assign
    #> 1781           179.04615                  13     0.9865201
    #> 1031            22.80000                   6     0.9865201
    #> 178            179.04615                  13     0.9906169
    #> 103             22.80000                   6     0.9906169
    #> 179            179.04615                  13     1.0442585
    #> 195             19.50000                   2     2.4059356
    #> 201             16.10000                   8     2.4059356
    #> 148            168.20000                  14     3.0489706
    #> 47              51.25167                  14     3.0489706
    #> 44              48.47000                   6     3.0489706
    #> 46              51.25167                  14     4.0609602
    #> 182             23.52500                   5     4.0609602
    #> 40              13.08000                   5     4.0609602

# Final thoughts

This vignette shows many of the features of the package, some features have room for improvement, others are not fully described, and the reader will need to browse the documentation. Hopefully it is a good starting point for using the package.

    sessionInfo()
    #> R version 4.6.0 (2026-04-24)
    #> Platform: x86_64-pc-linux-gnu
    #> Running under: Ubuntu 24.04.4 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=en_US.UTF-8
    #>  [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] AlpsNMR_4.14.0      BiocParallel_1.46.0 readxl_1.4.5
    #> [4] ggplot2_4.0.3       dplyr_1.2.1         BiocStyle_2.40.0
    #>
    #> loaded via a namespace (and not attached):
    #>  [1] tidyselect_1.2.1       farver_2.1.2           S7_0.2.2
    #>  [4] fastmap_1.2.0          MassSpecWavelet_1.78.0 digest_0.6.39
    #>  [7] lifecycle_1.0.5        cluster_2.1.8.2        magrittr_2.0.5
    #> [10] compiler_4.6.0         rngtools_1.5.2         rlang_1.2.0
    #> [13] sass_0.4.10            tools_4.6.0            doSNOW_1.0.20
    #> [16] igraph_2.3.0           utf8_1.2.6             yaml_2.3.12
    #> [19] data.table_1.18.2.1    knitr_1.51             doRNG_1.8.6.3
    #> [22] labeling_0.4.3         rARPACK_0.11-0         xml2_1.5.2
    #> [25] plyr_1.8.9             RColorBrewer_1.1-3     withr_3.0.2
    #> [28] purrr_1.2.2            itertools_0.1-3        sys_3.4.3
    #> [31] grid_4.6.0             pcaPP_2.0-5            progressr_0.19.0
    #> [34] scales_1.4.0           iterators_1.0.14       MASS_7.3-65
    #> [37] signal_1.8-1           cli_3.6.6              mvtnorm_1.3-7
    #> [40] ellipse_0.5.0          rmarkdown_2.31         crayon_1.5.3
    #> [43] generics_0.1.4         otel_0.2.0             RcppParallel_5.1.11-2
    #> [46] RSpectra_0.16-2        httr_1.4.8             reshape2_1.4.5
    #> [49] cachem_1.1.0           stringr_1.6.0          rvest_1.0.5
    #> [52] parallel_4.6.0         impute_1.86.0          BiocManager_1.30.27
    #> [55] cellranger_1.1.0       matrixStats_1.5.0      vctrs_0.7.3
    #> [58] Matrix_1.7-5           jsonlite_2.0.0         speaq_2.7.0
    #> [61] SparseM_1.84-2         ggrepel_0.9.8          baseline_1.3-7
    #> [64] maketools_1.3.2        foreach_1.5.2          tidyr_1.3.2
    #> [67] jquerylib_0.1.4        snow_0.4-4             missForest_1.6.1
    #> [70] glue_1.8.1             codetools_0.2-20       mixOmics_6.36.0
    #> [73] cowplot_1.2.0          stringi_1.8.7          gtable_0.3.6
    #> [76] tibble_3.3.1           pillar_1.11.1          htmltools_0.5.9
    #> [79] randomForest_4.7-1.2   R6_2.6.1               Rdpack_2.6.6
    #> [82] zigg_0.0.2             evaluate_1.0.5         lattice_0.22-9
    #> [85] rbibutils_2.4.1        Rfast_2.1.5.2          corpcor_1.6.10
    #> [88] bslib_0.10.0           Rcpp_1.1.1-1.1         gridExtra_2.3
    #> [91] ranger_0.18.0          xfun_0.57              fs_2.1.0
    #> [94] buildtools_1.0.0       pkgconfig_2.0.3

1. e.g. it can inadvertedly distort the PQN normalization results↩︎