Package 'AlpsNMR'

Description

AlpsNMR allows you to import NMR spectra into R and provides automated and efficient signal processing for untargeted NMR metabolomics.

Details

The following functions can be combined with the pipe. They create or modify the nmr_dataset object.

• nmr_read_samples_dir() or nmr_read_samples()

• nmr_interpolate_1D()

• nmr_exclude_region()

• nmr_normalize()

• plot()

There are also functions to extract the metadata and submit the samples to irods, see the example below.

The nmr_dataset object is essentially a list, so it is easy to access its components for further analysis.

Author(s)

Maintainer: Sergio Oller Moreno [email protected] (ORCID)

Authors:

• Ivan Montoliu Roura [email protected]

• Francisco Madrid Gambin [email protected] (ORCID)

• Luis Fernandez [email protected] (ORCID)

• Héctor Gracia Cabrera [email protected]

• Santiago Marco Colás [email protected] (ORCID)

Other contributors:

• Laura López Sánchez [contributor]

• Nestlé Institute of Health Sciences [copyright holder]

• Institute for Bioengineering of Catalonia [copyright holder]

• Miller Jack [email protected] (ORCID) (Autophase wrapper, ASICS export) [contributor]

See Also

Useful links:

• https://sipss.github.io/AlpsNMR/

• https://github.com/sipss/AlpsNMR

• Report bugs at https://github.com/sipss/AlpsNMR/issues

Examples

dir_to_demo_dataset <- system.file("dataset-demo", package = "AlpsNMR")
dataset <- nmr_read_samples_dir(dir_to_demo_dataset)
my_nmr_dataset <- dataset %>%
    nmr_interpolate_1D(axis = c(0.4, 10)) %>%
    nmr_exclude_region(exclude = list(water = c(4.6, 5))) %>%
    nmr_normalize(method = "pqn") %>%
    plot()

Description

Extract parts of an nmr_dataset

Usage

## S3 method for class 'nmr_dataset'
x[i]

Arguments

Value

an nmr_dataset with the extracted samples

See Also

Other subsetting functions: [.nmr_dataset_1D(), [.nmr_dataset_peak_table(), filter.nmr_dataset_family(), nmr_pca_outliers_filter()

Examples

dir_to_demo_dataset <- system.file("dataset-demo", package = "AlpsNMR")
dataset <- nmr_read_samples_dir(dir_to_demo_dataset)
dataset2 <- dataset[1:3] # get the first 3 samples

Description

Extract parts of an nmr_dataset_1D

Usage

## S3 method for class 'nmr_dataset_1D'
x[i]

Arguments

Value

an nmr_dataset_1D with the extracted samples

See Also

Other subsetting functions: [.nmr_dataset(), [.nmr_dataset_peak_table(), filter.nmr_dataset_family(), nmr_pca_outliers_filter()

Other nmr_dataset_1D functions: format.nmr_dataset_1D(), get_integration_with_metadata(), is.nmr_dataset_1D(), nmr_integrate_peak_positions(), nmr_integrate_regions(), nmr_meta_add(), nmr_meta_export(), nmr_meta_get(), nmr_meta_get_column(), nmr_ppm_resolution(), print.nmr_dataset_1D()

Examples

dir_to_demo_dataset <- system.file("dataset-demo", package = "AlpsNMR")
dataset <- nmr_read_samples_dir(dir_to_demo_dataset)
dataset_1D <- nmr_interpolate_1D(dataset, axis = c(min = -0.5, max = 10, by = 2.3E-4))
dataset_1D[0]

Description

Extract parts of an nmr_dataset_peak_table

Usage

## S3 method for class 'nmr_dataset_peak_table'
x[i]

Arguments

Value

an nmr_dataset_peak_table with the extracted samples

See Also

Other subsetting functions: [.nmr_dataset(), [.nmr_dataset_1D(), filter.nmr_dataset_family(), nmr_pca_outliers_filter()

Examples

dir_to_demo_dataset <- system.file("dataset-demo", package = "AlpsNMR")
dataset <- nmr_read_samples_dir(dir_to_demo_dataset)
dataset_1D <- nmr_interpolate_1D(dataset, axis = c(min = -0.5, max = 10, by = 2.3E-4))
meta <- file.path(dir_to_demo_dataset, "dummy_metadata.xlsx")
metadata <- readxl::read_excel(meta, sheet = 1)
dataset_1D <- nmr_meta_add(dataset_1D, metadata = metadata, by = "NMRExperiment")
metadata <- list(external = dataset_1D[["metadata"]][["external"]])
peak_table <- nmr_data(dataset_1D)
new <- new_nmr_dataset_peak_table(peak_table, metadata)
new[0]

Description

Bootstrap and permutation over PLS-VIP on AlpsNMR can be performed on both nmr_dataset_1D full spectra as well as nmr_dataset_peak_table peak tables.

Usage

bp_kfold_VIP_analysis(dataset, y_column, k = 4, ncomp = 3, nbootstrap = 300)

Arguments

Details

Use of the bootstrap and permutation methods for a more robust variable importance in the projection metric for partial least squares regression, in a k-fold cross validation

Value

A list with the following elements:

• important_vips: A list with the important vips selected

• relevant_vips: List of vips with some relevance

• wilcoxon_vips: List of vips that pass a wilcoxon test

• vip_means: Means of the vips scores

• vip_score_plot: plot of the vips scores

• kfold_resuls: results of the k bp_VIP_analysis

• kfold_index: list of index of partitions of the folds

Examples

# Data analysis for a table of integrated peaks
set.seed(42)
## Generate an artificial nmr_dataset_peak_table:
### Generate artificial metadata:
num_samples <- 64 # use an even number in this example
num_peaks <- 10
metadata <- data.frame(
    NMRExperiment = as.character(1:num_samples),
    Condition = sample(rep(c("A", "B"), times = num_samples / 2), num_samples)
)

### The matrix with peaks
peak_means <- runif(n = num_peaks, min = 300, max = 600)
peak_sd <- runif(n = num_peaks, min = 30, max = 60)
peak_matrix <- mapply(function(mu, sd) rnorm(num_samples, mu, sd),
    mu = peak_means, sd = peak_sd
)
colnames(peak_matrix) <- paste0("Peak", 1:num_peaks)
rownames(peak_matrix) <- paste0("Sample", 1:num_samples)

## Artificial differences depending on the condition:
peak_matrix[metadata$Condition == "A", "Peak2"] <-
    peak_matrix[metadata$Condition == "A", "Peak2"] + 70

peak_matrix[metadata$Condition == "A", "Peak6"] <-
    peak_matrix[metadata$Condition == "A", "Peak6"] - 60

### The nmr_dataset_peak_table
peak_table <- new_nmr_dataset_peak_table(
    peak_table = peak_matrix,
    metadata = list(external = metadata)
)

## We will use bootstrap and permutation method for VIPs selection
## in a a k-fold cross validation
bp_results <- bp_kfold_VIP_analysis(peak_table, # Data to be analyzed
    y_column = "Condition", # Label
    k = 2,
    ncomp = 1,
    nbootstrap = 5
)

message("Selected VIPs are: ", bp_results$important_vips)

Description

Bootstrap and permutation over PLS-VIP on AlpsNMR can be performed on both nmr_dataset_1D full spectra as well as nmr_dataset_peak_table peak tables.

Usage

bp_VIP_analysis(dataset, train_index, y_column, ncomp, nbootstrap = 300)

Arguments

Details

Use of the bootstrap and permutation methods for a more robust variable importance in the projection metric for partial least squares regression

Value

A list with the following elements:

• important_vips: A list with the important vips selected

• relevant_vips: List of vips with some relevance

• pls_vip: Pls-VIPs of every bootstrap

• pls_vip_perm: Pls-VIPs of every bootstrap with permuted variables

• pls_vip_means: Pls-VIPs normaliced differences means

• pls_vip_score_diff: Differences of pls_vip and pls_vip_perm

• pls_models: pls models of the diferent bootstraps

• pls_perm_models: pls permuted models of the diferent bootstraps

• classif_rate: classification rate of the bootstrap models

• general_model: pls model trained with all train data

• general_CR: classification rate of the general_model

• vips_model: pls model trained with vips selection over all train data

• vips_CR: classification rate of the vips_model

• error: error spected in a t distribution

• lower_bound: lower bound of the confidence interval

• upper_bound: upper bound of the confidence interval

Examples

# Data analysis for a table of integrated peaks

## Generate an artificial nmr_dataset_peak_table:
### Generate artificial metadata:
num_samples <- 32 # use an even number in this example
num_peaks <- 20
metadata <- data.frame(
    NMRExperiment = as.character(1:num_samples),
    Condition = rep(c("A", "B"), times = num_samples / 2)
)

### The matrix with peaks
peak_means <- runif(n = num_peaks, min = 300, max = 600)
peak_sd <- runif(n = num_peaks, min = 30, max = 60)
peak_matrix <- mapply(function(mu, sd) rnorm(num_samples, mu, sd),
    mu = peak_means, sd = peak_sd
)
colnames(peak_matrix) <- paste0("Peak", 1:num_peaks)

## Artificial differences depending on the condition:
peak_matrix[metadata$Condition == "A", "Peak2"] <-
    peak_matrix[metadata$Condition == "A", "Peak2"] + 70

peak_matrix[metadata$Condition == "A", "Peak6"] <-
    peak_matrix[metadata$Condition == "A", "Peak6"] - 60

### The nmr_dataset_peak_table
peak_table <- new_nmr_dataset_peak_table(
    peak_table = peak_matrix,
    metadata = list(external = metadata)
)

## We will use a double cross validation, splitting the samples with random
## subsampling both in the external and internal validation.
## The classification model will be a PLSDA, exploring at maximum 3 latent
## variables.
## The best model will be selected based on the area under the ROC curve
methodology <- plsda_auroc_vip_method(ncomp = 3)
model <- nmr_data_analysis(
    peak_table,
    y_column = "Condition",
    identity_column = NULL,
    external_val = list(iterations = 1, test_size = 0.25),
    internal_val = list(iterations = 3, test_size = 0.25),
    data_analysis_method = methodology
)
## Area under ROC for each outer cross-validation iteration:
model$outer_cv_results_digested$auroc

## The number of components for the bootstrap models is selected
ncomps <- model$outer_cv_results$`1`$model$ncomp
train_index <- model$train_test_partitions$outer$`1`$outer_train

# Bootstrap and permutation for VIP selection
bp_VIPS <- bp_VIP_analysis(peak_table, # Data to be analyzed
    train_index,
    y_column = "Condition",
    ncomp = ncomps,
    nbootstrap = 10
)

Description

Downloads the MTBLS242 dataset from Gralka et al., 2015. DOI: doi:10.3945/ajcn.115.110536.

Usage

download_MTBLS242(
  dest_dir = "MTBLS242",
  force = FALSE,
  keep_only_CPMG_1r = TRUE,
  keep_only_preop_and_3months = TRUE,
  keep_only_complete_time_points = TRUE
)

Arguments

Details

Besides the destination directory, this function includes three logical parameters to limit the amount of downloaded/saved data. To run the tutorial workflow:

• only the "preop" and "three months" timepoints are used,

• only subjects measured in both preop and three months time points are used

• only the CPMG samples are used.

If you want to run the tutorial, you can set those filters to TRUE. Then, roughly 800MB will be downloaded, and 77MB of disk space will be used, since for each downloaded sample we remove all the data but the CPMG.

If you set those filters to FALSE, roughly 1.8GB of data will be downloaded (since we have more timepoints to download) and 1.8GB of disk space will be used.

Note that we have experienced some sporadic timeouts from Metabolights, when downloading the dataset. If you get those timeouts simply re-run the download function and it will restart from where it stopped.

Note as well, that we observed several files to have incorrect data:

• Obs4_0346s.zip is not present in the FTP server

• Obs0_0110s.zip and Obs1_0256s.zip incorrectly contain sample Obs1_0010s

This function removes all three samples from the samples annotations and doesn't download their data.

Value

Invisibly, the annotations. See the example for how to download the annotations and create a dataset from the downloaded files.

Examples

## Not run: 
  download_MTBLS242("./MTBLS242")
  annot <- readr::read_tsv(annotations_destfile)
  
  dataset <- nmr_read_samples(annot$filename)
  dataset <- nmr_meta_add(dataset, annot)
  dataset

## End(Not run)

Description

The function lists samples from the chosen folder required to import and create a nmr_dataset_1D object. The function is based on the fs::dir_ls() function.

Usage

file_lister(dataset_path_nmr, glob)

Arguments

Value

lists of samples from the chosen folder

Examples

dir_to_demo_dataset <- system.file("dataset-demo", package = "AlpsNMR")
lists_of_samples <- file_lister(dir_to_demo_dataset, "*0")

Description

The rDolphin family functions are introduced to perform automatic targeted metabolite profiling. Therefore, ensure that you interpolated from -0.1 ppm in order to consider the TSP/DSS signal at 0.0 ppm. The function generates a list with the files required by to_rDolphin function. Then, it is required to save them with the save_files_to_rDolphin. to_rDolphin function will read the generated "parameters.csv" file. function.

Usage

files_to_rDolphin(nmr_dataset, biological_origin)

Arguments

Value

a list containing:

• meta_rDolphin: metadata in rDolphin format,

• NMR_spectra: spectra matrix

• ROI: ROI template

• Parameters: parameters file

See Also

Other import/export functions: Pipelines, load_and_save_functions, nmr_data(), nmr_meta_export(), nmr_read_bruker_fid(), nmr_read_samples(), nmr_zip_bruker_samples(), save_files_to_rDolphin(), save_profiling_output(), to_ChemoSpec()

Examples

## Not run: 
# Set the directory in which rDolphin files will be saved
output_dir_10_rDolphin <- file.path(your_path, "10-rDolphin")
fs::dir_create(output_dir_10_rDolphin)

# Generate the files (for plasma/serum)
files_rDolphin <- files_to_rDolphin(nmr_dataset_0_10_ppm, blood)

# Save the files
save_files_to_rDolphin(files_rDolphin, output_dir_10_rDolphin)

# Build the rDolphin object. Do not forget to set the directory
setwd(output_dir_10_rDolphin)
rDolphin_object <- to_rDolphin("Parameters.csv")

# Visualize your spectra
rDolphin_plot(rDolphin_object)

# Run the main profiling function (it takes a while)
targeted_profiling <- Automatic_targeted_profiling(rDolphin_object)

# Save results
save_profiling_output(targeted_profiling, output_dir_10_rDolphin)

save_profiling_plots(
    output_dir_10_rDolphin, targeted_profiling$final_output,
    targeted_profiling$reproducibility_data
)

# Additionally, you can run some stats
intensities <- targeted_profiling$final_output$intensity
group <- as.factor(rDolphin_object$Metadata$type)
model_PLS <- rdCV_PLS_RF(X = intensities, Y = group)

## End(Not run)

Description

Keep samples based on metadata column criteria

Usage

## S3 method for class 'nmr_dataset_family'
filter(.data, ...)

Arguments

Value

The same object, with the matching rows

See Also

Other subsetting functions: [.nmr_dataset(), [.nmr_dataset_1D(), [.nmr_dataset_peak_table(), nmr_pca_outliers_filter()

Examples

dir_to_demo_dataset <- system.file("dataset-demo", package = "AlpsNMR")
dataset <- nmr_read_samples_dir(dir_to_demo_dataset)
dataset_1D <- nmr_interpolate_1D(dataset, axis = c(min = -0.5, max = 10, by = 2.3E-4))

## example 1
sample_10 <- filter(dataset_1D, NMRExperiment == "10")

## example 2
# test_samples <- dataset_1D %>% filter(nmr_peak_table$metadata$external$Group == "placebo")

Description

Format for nmr_dataset

Usage

## S3 method for class 'nmr_dataset'
format(x, ...)

Arguments

Value

Format for nmr_dataset

See Also

Other class helper functions: format.nmr_dataset_1D(), format.nmr_dataset_peak_table(), is.nmr_dataset_1D(), is.nmr_dataset_peak_table(), new_nmr_dataset(), new_nmr_dataset_1D(), new_nmr_dataset_peak_table(), print.nmr_dataset(), print.nmr_dataset_1D(), print.nmr_dataset_peak_table(), validate_nmr_dataset(), validate_nmr_dataset_family(), validate_nmr_dataset_peak_table()

Examples

dir_to_demo_dataset <- system.file("dataset-demo", package = "AlpsNMR")
dataset <- nmr_read_samples_dir(dir_to_demo_dataset)
format(dataset)

Description

format for nmr_dataset_1D

Usage

## S3 method for class 'nmr_dataset_1D'
format(x, ...)

Arguments

Value

format for nmr_dataset_1D

See Also

Other class helper functions: format.nmr_dataset(), format.nmr_dataset_peak_table(), is.nmr_dataset_1D(), is.nmr_dataset_peak_table(), new_nmr_dataset(), new_nmr_dataset_1D(), new_nmr_dataset_peak_table(), print.nmr_dataset(), print.nmr_dataset_1D(), print.nmr_dataset_peak_table(), validate_nmr_dataset(), validate_nmr_dataset_family(), validate_nmr_dataset_peak_table()

Other nmr_dataset_1D functions: [.nmr_dataset_1D(), get_integration_with_metadata(), is.nmr_dataset_1D(), nmr_integrate_peak_positions(), nmr_integrate_regions(), nmr_meta_add(), nmr_meta_export(), nmr_meta_get(), nmr_meta_get_column(), nmr_ppm_resolution(), print.nmr_dataset_1D()

Examples

dir_to_demo_dataset <- system.file("dataset-demo", package = "AlpsNMR")
dataset <- nmr_read_samples_dir(dir_to_demo_dataset)
dataset_1D <- nmr_interpolate_1D(dataset, axis = c(min = -0.5, max = 10, by = 2.3E-4))
format(dataset_1D)

Description

Format for nmr_dataset_peak_table

Usage

## S3 method for class 'nmr_dataset_peak_table'
format(x, ...)

Arguments

Value

Format for nmr_dataset_peak_table

See Also

Other class helper functions: format.nmr_dataset(), format.nmr_dataset_1D(), is.nmr_dataset_1D(), is.nmr_dataset_peak_table(), new_nmr_dataset(), new_nmr_dataset_1D(), new_nmr_dataset_peak_table(), print.nmr_dataset(), print.nmr_dataset_1D(), print.nmr_dataset_peak_table(), validate_nmr_dataset(), validate_nmr_dataset_family(), validate_nmr_dataset_peak_table()

Examples

dir_to_demo_dataset <- system.file("dataset-demo", package = "AlpsNMR")
dataset <- nmr_read_samples_dir(dir_to_demo_dataset)
dataset_1D <- nmr_interpolate_1D(dataset, axis = c(min = -0.5, max = 10, by = 2.3E-4))
meta <- file.path(dir_to_demo_dataset, "dummy_metadata.xlsx")
metadata <- readxl::read_excel(meta, sheet = 1)
dataset_1D <- nmr_meta_add(dataset_1D, metadata = metadata, by = "NMRExperiment")
metadata <- list(external = dataset_1D[["metadata"]][["external"]])
peak_table <- nmr_data(dataset_1D)
new <- new_nmr_dataset_peak_table(peak_table, metadata)
format(new)

Description

Get integrals with metadata from ⁠integrate peak positions⁠

Usage

get_integration_with_metadata(integration_object)

Arguments

Value

Get integrals with metadata from ⁠integrate peak positions⁠

integration dataframe

See Also

Other peak integration functions: Pipelines, nmr_identify_regions_blood(), nmr_identify_regions_cell(), nmr_identify_regions_urine(), nmr_integrate_peak_positions(), nmr_integrate_regions()

Other nmr_dataset_1D functions: [.nmr_dataset_1D(), format.nmr_dataset_1D(), is.nmr_dataset_1D(), nmr_integrate_peak_positions(), nmr_integrate_regions(), nmr_meta_add(), nmr_meta_export(), nmr_meta_get(), nmr_meta_get_column(), nmr_ppm_resolution(), print.nmr_dataset_1D()

Examples

peak_table <- matrix(1:6, nrow = 2, ncol = 3)
rownames(peak_table) <- c("10", "20")
colnames(peak_table) <- c("ppm_1.2", "ppm1.4", "ppm1.6")

dataset <- new_nmr_dataset_peak_table(
    peak_table = peak_table,
    metadata = list(external = data.frame(NMRExperiment = c("10", "20"), Condition = c("A", "B")))
)
get_integration_with_metadata(dataset)

Description

The Human Metabolome DataBase multiplet table

References

https://hmdb.ca/

Examples

# Get all the 1-Methylhistidine peaks:
data("hmdb")
hmdb[hmdb$Metabolite == "1-Methylhistidine", ]

Description

The Human Metabolome DataBase multiplet table: blood metabolites normally found in NMR-based metabolomics

References

https://hmdb.ca/

Examples

data("HMDB_blood")
HMDB_blood[HMDB_blood$Metabolite == "1-Methylhistidine", ]

Description

The Human Metabolome DataBase multiplet table: cell metabolites normally found in NMR-based metabolomics

References

https://hmdb.ca/

Examples

data("HMDB_cell")
HMDB_cell[HMDB_cell$Metabolite == "Acetone", ]

Description

The Human Metabolome DataBase multiplet table: urine metabolites normally found in NMR-based metabolomics

References

https://hmdb.ca/

Examples

data("HMDB_urine")
HMDB_urine[HMDB_urine$Metabolite == "1-Methyladenosine", ]

Object is of nmr_dataset class

Description

Object is of nmr_dataset class

Usage

is.nmr_dataset(x)

Arguments

Value

TRUE if the object is an nmr_dataset, FALSE otherwise

Examples

dir_to_demo_dataset <- system.file("dataset-demo", package = "AlpsNMR")
dataset <- nmr_read_samples_dir(dir_to_demo_dataset)
is(dataset)

Object is of nmr_dataset_1D class

Description

Object is of nmr_dataset_1D class

Usage

is.nmr_dataset_1D(x)

Arguments

Value

TRUE if the object is an nmr_dataset_1D, FALSE otherwise

See Also

Other class helper functions: format.nmr_dataset(), format.nmr_dataset_1D(), format.nmr_dataset_peak_table(), is.nmr_dataset_peak_table(), new_nmr_dataset(), new_nmr_dataset_1D(), new_nmr_dataset_peak_table(), print.nmr_dataset(), print.nmr_dataset_1D(), print.nmr_dataset_peak_table(), validate_nmr_dataset(), validate_nmr_dataset_family(), validate_nmr_dataset_peak_table()

Other nmr_dataset_1D functions: [.nmr_dataset_1D(), format.nmr_dataset_1D(), get_integration_with_metadata(), nmr_integrate_peak_positions(), nmr_integrate_regions(), nmr_meta_add(), nmr_meta_export(), nmr_meta_get(), nmr_meta_get_column(), nmr_ppm_resolution(), print.nmr_dataset_1D()

Examples

dir_to_demo_dataset <- system.file("dataset-demo", package = "AlpsNMR")
dataset <- nmr_read_samples_dir(dir_to_demo_dataset)
dataset_1D <- nmr_interpolate_1D(dataset, axis = c(min = -0.5, max = 10, by = 2.3E-4))
result <- is(dataset_1D)

Object is of nmr_dataset_peak_table class

Description

Object is of nmr_dataset_peak_table class

Usage

is.nmr_dataset_peak_table(x)

Arguments

Value

TRUE if the object is an nmr_dataset_peak_table, FALSE otherwise

See Also

Other class helper functions: format.nmr_dataset(), format.nmr_dataset_1D(), format.nmr_dataset_peak_table(), is.nmr_dataset_1D(), new_nmr_dataset(), new_nmr_dataset_1D(), new_nmr_dataset_peak_table(), print.nmr_dataset(), print.nmr_dataset_1D(), print.nmr_dataset_peak_table(), validate_nmr_dataset(), validate_nmr_dataset_family(), validate_nmr_dataset_peak_table()

Examples

dir_to_demo_dataset <- system.file("dataset-demo", package = "AlpsNMR")
dataset <- nmr_read_samples_dir(dir_to_demo_dataset)
dataset_1D <- nmr_interpolate_1D(dataset, axis = c(min = -0.5, max = 10, by = 2.3E-4))
meta <- file.path(dir_to_demo_dataset, "dummy_metadata.xlsx")
metadata <- readxl::read_excel(meta, sheet = 1)
dataset_1D <- nmr_meta_add(dataset_1D, metadata = metadata, by = "NMRExperiment")
metadata <- list(external = dataset_1D[["metadata"]][["external"]])
peak_table <- nmr_data(dataset_1D)
new <- new_nmr_dataset_peak_table(peak_table, metadata)
is(new)

Description

nmr_dataset_load

nmr_dataset_save

Usage

nmr_dataset_load(file_name)

nmr_dataset_save(nmr_dataset, file_name, ...)

Arguments

Value

Functions to load and save nmr_dataset objects

load nmr dataset

save nmr dataset

See Also

Other import/export functions: Pipelines, files_to_rDolphin(), nmr_data(), nmr_meta_export(), nmr_read_bruker_fid(), nmr_read_samples(), nmr_zip_bruker_samples(), save_files_to_rDolphin(), save_profiling_output(), to_ChemoSpec()

Examples

# dataset <- nmr_dataset_load("test")
nmr_dataset <- nmr_dataset_load(system.file("extdata", "nmr_dataset.rds", package = "AlpsNMR"))

dir_to_demo_dataset <- system.file("dataset-demo", package = "AlpsNMR")
dataset <- nmr_read_samples_dir(dir_to_demo_dataset)
# nmr_dataset_save(dataset, "test")

Description

Plot stability among models of the external cross validation

Usage

models_stability_plot_bootstrap(bp_results)

Arguments

Value

A plot of models stability

Examples

# Data analysis for a table of integrated peaks

## Generate an artificial nmr_dataset_peak_table:
### Generate artificial metadata:
num_samples <- 64 # use an even number in this example
num_peaks <- 20
metadata <- data.frame(
    NMRExperiment = as.character(1:num_samples),
    Condition = rep(c("A", "B"), times = num_samples / 2)
)

### The matrix with peaks
peak_means <- runif(n = num_peaks, min = 300, max = 600)
peak_sd <- runif(n = num_peaks, min = 30, max = 60)
peak_matrix <- mapply(function(mu, sd) rnorm(num_samples, mu, sd),
    mu = peak_means, sd = peak_sd
)
colnames(peak_matrix) <- paste0("Peak", 1:num_peaks)

## Artificial differences depending on the condition:
peak_matrix[metadata$Condition == "A", "Peak2"] <-
    peak_matrix[metadata$Condition == "A", "Peak2"] + 70

peak_matrix[metadata$Condition == "A", "Peak6"] <-
    peak_matrix[metadata$Condition == "A", "Peak6"] - 60

### The nmr_dataset_peak_table
peak_table <- new_nmr_dataset_peak_table(
    peak_table = peak_matrix,
    metadata = list(external = metadata)
)

## We will use bootstrap and permutation method for VIPs selection
## in a a k-fold cross validation
# bp_results <- bp_kfold_VIP_analysis(peak_table, # Data to be analized
#                           y_column = "Condition", # Label
#                           k = 3,
#                           nbootstrap = 10)

# message("Selected VIPs are: ", bp_results$importarn_vips)

# models_stability_plot_bootstrap(bp_results)

Description

Plot stability among models of the external cross validation

Usage

models_stability_plot_plsda(model)

Arguments

Value

A plot of models stability

Examples

# Data analysis for a table of integrated peaks

## Generate an artificial nmr_dataset_peak_table:
### Generate artificial metadata:
num_samples <- 32 # use an even number in this example
num_peaks <- 20
metadata <- data.frame(
    NMRExperiment = as.character(1:num_samples),
    Condition = rep(c("A", "B"), times = num_samples / 2)
)

### The matrix with peaks
peak_means <- runif(n = num_peaks, min = 300, max = 600)
peak_sd <- runif(n = num_peaks, min = 30, max = 60)
peak_matrix <- mapply(function(mu, sd) rnorm(num_samples, mu, sd),
    mu = peak_means, sd = peak_sd
)
colnames(peak_matrix) <- paste0("Peak", 1:num_peaks)

## Artificial differences depending on the condition:
peak_matrix[metadata$Condition == "A", "Peak2"] <-
    peak_matrix[metadata$Condition == "A", "Peak2"] + 70

peak_matrix[metadata$Condition == "A", "Peak6"] <-
    peak_matrix[metadata$Condition == "A", "Peak6"] - 60

### The nmr_dataset_peak_table
peak_table <- new_nmr_dataset_peak_table(
    peak_table = peak_matrix,
    metadata = list(external = metadata)
)

methodology <- plsda_auroc_vip_method(ncomp = 3)
model <- nmr_data_analysis(
    peak_table,
    y_column = "Condition",
    identity_column = NULL,
    external_val = list(iterations = 3, test_size = 0.25),
    internal_val = list(iterations = 3, test_size = 0.25),
    data_analysis_method = methodology
)

# models_stability_plot_plsda(model)

Description

Create an nmr_dataset object

Usage

new_nmr_dataset(metadata, data_fields, axis)

Arguments

Value

Create an nmr_dataset object

Create an nmr_dataset object

See Also

Other class helper functions: format.nmr_dataset(), format.nmr_dataset_1D(), format.nmr_dataset_peak_table(), is.nmr_dataset_1D(), is.nmr_dataset_peak_table(), new_nmr_dataset_1D(), new_nmr_dataset_peak_table(), print.nmr_dataset(), print.nmr_dataset_1D(), print.nmr_dataset_peak_table(), validate_nmr_dataset(), validate_nmr_dataset_family(), validate_nmr_dataset_peak_table()

Examples

#
metadata_1D <- list(external = data.frame(NMRExperiment = c("10", "20")))
# Sample 10 and Sample 20 can have different lengths (due to different setups)
data_fields_1D <- list(data_1r = list(runif(16), runif(32)))
# Each sample has its own axis list, with one element (because this example is 1D)
axis_1D <- list(list(1:16), list(1:32))
my_1D_data <- new_nmr_dataset(metadata_1D, data_fields_1D, axis_1D)

# Example for 2D samples
metadata_2D <- list(external = data.frame(NMRExperiment = c("11", "21")))
data_fields_2D <- list(data_2rr = list(matrix(runif(16 * 3), nrow = 16, ncol = 3),
    runif(32 * 3),
    nrow = 32, ncol = 3
))
# Each sample has its own axis list, with one element (because this example is 1D)
axis_2D <- list(list(1:16, 1:3), list(1:32, 1:3))
my_2D_data <- new_nmr_dataset(metadata_2D, data_fields_2D, axis_2D)

Description

Creates a new 1D nmr_dataset object from scratch

Usage

new_nmr_dataset_1D(ppm_axis, data_1r, metadata)

Arguments

Value

Creates a new 1D nmr_dataset object from scratch

See Also

Other class helper functions: format.nmr_dataset(), format.nmr_dataset_1D(), format.nmr_dataset_peak_table(), is.nmr_dataset_1D(), is.nmr_dataset_peak_table(), new_nmr_dataset(), new_nmr_dataset_peak_table(), print.nmr_dataset(), print.nmr_dataset_1D(), print.nmr_dataset_peak_table(), validate_nmr_dataset(), validate_nmr_dataset_family(), validate_nmr_dataset_peak_table()

Examples

# Create a random spectra matrix
nsamp <- 12
npoints <- 20
dummy_ppm_axis <- seq(from = 0.2, to = 10, length.out = npoints)
dummy_spectra_matrix <- matrix(runif(nsamp * npoints), nrow = nsamp, ncol = npoints)
metadata <- list(external = data.frame(
    NMRExperiment = paste0("Sample", 1:12),
    DummyClass = c("a", "b")
))
dummy_nmr_dataset_1D <- new_nmr_dataset_1D(
    ppm_axis = dummy_ppm_axis,
    data_1r = dummy_spectra_matrix,
    metadata = metadata
)

Description

Creates a new nmr_dataset_peak_table object from scratch

Usage

new_nmr_dataset_peak_table(peak_table, metadata)

Arguments

Value

Creates a new nmr_dataset_peak_table object from scratch

See Also

Other class helper functions: format.nmr_dataset(), format.nmr_dataset_1D(), format.nmr_dataset_peak_table(), is.nmr_dataset_1D(), is.nmr_dataset_peak_table(), new_nmr_dataset(), new_nmr_dataset_1D(), print.nmr_dataset(), print.nmr_dataset_1D(), print.nmr_dataset_peak_table(), validate_nmr_dataset(), validate_nmr_dataset_family(), validate_nmr_dataset_peak_table()

Examples

dir_to_demo_dataset <- system.file("dataset-demo", package = "AlpsNMR")
dataset <- nmr_read_samples_dir(dir_to_demo_dataset)
dataset_1D <- nmr_interpolate_1D(dataset, axis = c(min = -0.5, max = 10, by = 2.3E-4))
meta <- file.path(dir_to_demo_dataset, "dummy_metadata.xlsx")
metadata <- readxl::read_excel(meta, sheet = 1)
dataset_1D <- nmr_meta_add(dataset_1D, metadata = metadata, by = "NMRExperiment")
metadata <- list(external = dataset_1D[["metadata"]][["external"]])
peak_table <- nmr_data(dataset_1D)
new <- new_nmr_dataset_peak_table(peak_table, metadata)

Description

This function is based on speaq::dohCluster.

Usage

nmr_align(
  nmr_dataset,
  peak_data,
  NMRExp_ref = NULL,
  maxShift_ppm = 0.0015,
  acceptLostPeak = FALSE
)

Arguments

Value

An nmr_dataset_1D, with the spectra aligned

See Also

Other alignment functions: Pipelines, nmr_align_find_ref()

Other peak alignment functions: nmr_align_find_ref()

Description

Find alignment reference

Usage

nmr_align_find_ref(nmr_dataset, peak_data)

Arguments

Value

The NMRExperiment of the reference sample

See Also

Other alignment functions: Pipelines, nmr_align()

Other peak alignment functions: nmr_align()

Description

Use phasing algorithms to rephase data in the spectral domain.

This function may improve autophasing processing from instrument vendors. It wraps the NMRphasing::NMRphasing() function, to automatically rephase spectra, allowing you to choose from a number of algorithms of which NLS, MPC_DANM and SPC_DANM are the most recent.

Rephasing should happen before any spectra interpolation.

Please use the all_components = TRUE when calling nmr_read_samples() in order to load the complex spectra and fix NMR phasing correctly.

Usage

nmr_autophase(
  dataset,
  method = c("NLS", "MPC_DANM", "MPC_EMP", "SPC_DANM", "SPC_EMP", "SPC_AAM", "SPC_DSM"),
  withBC = FALSE,
  ...
)

Arguments

Value

A (hopefully better phased) nmr_dataset object, with updated real and imaginary parts.

Examples

if (requireNamespace("NMRphasing", quietly=TRUE)) {
  # Helpers to create a dataset:
  lorentzian <- function(x, x0, gamma, A) {
    A * (1 / (pi * gamma)) * ((gamma^2) / ((x - x0)^2 + gamma^2))
  }
  x <- seq(from=1, to=2, length.out = 300)
  y <- lorentzian(x, 1.3, 0.01, 1) + lorentzian(x, 1.6, 0.01, 1)
  dataset <- new_nmr_dataset(
    metadata = list(external = data.frame(NMRExperiment = "10")),
    data_fields = list(data_1r = list(y)),
    axis = list(list(x))
  )
  # Autophase, interpolate and plot:
  dataset <- nmr_autophase(dataset, method = "NLS")
  dataset <- nmr_interpolate_1D(dataset, axis = c(min = 1, max = 2, by = 0.01))
  plot(dataset)
}

Estimate the baseline on an nmr_dataset_1D object, using baseline::baseline.als.

Description

Estimate the baseline on an nmr_dataset_1D object, using baseline::baseline.als.

Usage

nmr_baseline_estimation(nmr_dataset, lambda = 9, p = 0.05, maxit = 20)

Arguments

Value

The same nmr_dataset_1D object with the data_1r_baseline element.

See Also

baseline::baseline.als

Other baseline removal functions: nmr_baseline_removal()

Examples

dataset_1D <- nmr_dataset_load(system.file("extdata", "nmr_dataset.rds", package = "AlpsNMR"))
dataset_1D <- nmr_baseline_estimation(dataset_1D, lambda = 9, p = 0.01)

Description

Removes the baseline on an nmr_dataset_1D object, using baseline::baseline.als.

Usage

nmr_baseline_removal(nmr_dataset, lambda = 6, p = 0.05, maxit = 20)

Arguments

Value

The same nmr_dataset_1D object after baseline removal.

See Also

baseline::baseline.als

Other baseline removal functions: nmr_baseline_estimation()

Examples

dataset_1D <- nmr_dataset_load(system.file("extdata", "nmr_dataset.rds", package = "AlpsNMR"))
dataset_no_base_line <- nmr_baseline_removal(dataset_1D, lambda = 6, p = 0.01)

Description

Estimates the threshold value for peak detection on an nmr_dataset_1D object by examining a range without peaks, by default the 9.5 - 10 ppm range.

Usage

nmr_baseline_threshold(
  nmr_dataset,
  range_without_peaks = c(9.5, 10),
  method = c("mean3sd", "median3mad")
)

Arguments

Details

Two methods can be used:

• "mean3sd": The mean3sd method computes the mean and the standard deviation of each spectrum in the 9.5 - 10 ppm range. The mean spectrum and the mean standard deviation are both vectors of length equal to the number of points in the given range. The mean of the mean spectrum the noise. The threshold is defined as ⁠center + 3 dispersion⁠, and it is one single threshold for the whole dataset. This is the default for backwards compatibility.

• "median3mad": First we take the data matrix. If we have estimated a baseline already, subtract it. In the defined region without peaks, estimate the median of each sample and its median absolute deviation. Return a vector of length equal to the number of samples with the ⁠median+3mad⁠ for each sample. This is a new more robust method.

Value

Numerical. A threshold value in intensity below that no peak is detected.

See Also

Other peak detection functions: Pipelines, nmr_detect_peaks(), nmr_detect_peaks_plot(), nmr_detect_peaks_plot_overview(), nmr_detect_peaks_tune_snr(), nmr_identify_regions_blood(), nmr_identify_regions_cell(), nmr_identify_regions_urine(), nmr_integrate_regions()

Examples

ppm_axis <- seq(from = 0, to = 10, length.out = 1000)
data_1r <- matrix(runif(1000, 0, 10), nrow = 1) + 100
dataset_1D <- new_nmr_dataset_1D(
    ppm_axis = ppm_axis,
    data_1r = data_1r,
    metadata = list(external=data.frame(NMRExperiment = "10"))
)
bl_threshold <- nmr_baseline_threshold(dataset_1D, range_without_peaks = c(9.5,10))

Description

If you have a lot of samples you can make the plot window bigger (or use "⁠ ```{r fig.height=10, fig.width=10}⁠" in notebooks), or choose some NMRExperiments.

Usage

nmr_baseline_threshold_plot(
  nmr_dataset,
  thresholds,
  NMRExperiment = "all",
  chemshift_range = c(9.5, 10),
  ...
)

Arguments

Value

A plot.

Examples

ppm_axis <- seq(from = 0, to = 10, length.out = 1000)
data_1r <- matrix(runif(1000, 0, 10), nrow = 1) + 100
dataset_1D <- new_nmr_dataset_1D(
    ppm_axis = ppm_axis,
    data_1r = data_1r,
    metadata = list(external=data.frame(NMRExperiment = "10"))
)
bl_threshold <- nmr_baseline_threshold(dataset_1D, range_without_peaks = c(9.5,10))
baselineThresh <- nmr_baseline_threshold(dataset_1D)
nmr_baseline_threshold_plot(dataset_1D, bl_threshold)

Description

Batman helpers

Usage

nmr_batman_write_options(
  bopts,
  batman_dir = "BatmanInput",
  filename = "batmanOptions.txt"
)

nmr_batman_export_dataset(
  nmr_dataset,
  batman_dir = "BatmanInput",
  filename = "NMRdata.txt"
)

nmr_batman_multi_data_user_hmdb(
  batman_dir = "BatmanInput",
  filename = "multi_data_user.csv"
)

nmr_batman_multi_data_user(
  multiplet_table,
  batman_dir = "BatmanInput",
  filename = "multi_data_user.csv"
)

nmr_batman_metabolites_list(
  metabolite_names,
  batman_dir = "BatmanInput",
  filename = "metabolitesList.csv"
)

Arguments

Value

These are helper functions to make Batman tests easier

See Also

Other batman functions: nmr_batman_options()

Examples

bopts <- nmr_batman_options()
# nmr_batman_write_options(bopts)

dataset_1D <- nmr_dataset_load(system.file("extdata", "nmr_dataset.rds", package = "AlpsNMR"))
# nmr_batman_export_dataset(dataset_1D)

message("Use of multi_data_user_hmdb")
# multi_data_user_hmdb <- nmr_batman_multi_data_user_hmdb()
hmdb <- NULL
# utils::data("hmdb", package = "AlpsNMR", envir = environment())
# hmdb <- nmr_batman_multi_data_user(hmbd)

metabolite_names <- c("alanine", "glucose")
# metabolite_names <- nmr_batman_metabolites_list(metabolite_names)

Description

Batman Options helper

Usage

nmr_batman_options(
  ppmRange = matrix(c(3, 3.1, 3.6, 3.7, 3.9, 4, 4, 4.1, 6.95, 7.05, 7.6, 7.7, 7.8, 7.9),
    ncol = 2, byrow = TRUE),
  specNo = "1",
  paraProc = 4L,
  negThresh = -0.5,
  scaleFac = 1e+06,
  downSamp = 1,
  hiresFlag = 1,
  randSeed = 100025L,
  nItBurnin = 200L,
  nItPostBurnin = 5000L,
  multFile = 2L,
  thinning = 50L,
  cfeFlag = 0,
  nItRerun = 5000L,
  startTemp = 1000,
  specFreq = 600,
  a = 1e-05,
  b = 1e-09,
  muMean = 1.1,
  muVar = 0.2,
  muVar_prop = 0.002,
  nuMVar = 0.0025,
  nuMVarProp = 0.1,
  tauMean = -0.05,
  tauPrec = 2,
  rdelta = 0.02,
  csFlag = 0
)

Arguments

Value

A batman_options object with the Batman Options

See Also

Other batman functions: nmr_batman

Examples

bopts <- nmr_batman_options()

Description

Build a peak table from the clustered peak list

Usage

nmr_build_peak_table(peak_data, dataset = NULL)

Arguments

Value

An nmr_dataset_peak_table, containing the peak table and the annotations

Examples

peak_data <- data.frame(
    NMRExperiment = c("10", "10", "20", "20"),
    peak_id = paste0("Peak", 1:4),
    ppm = c(1, 2, 1.1, 2.1),
    area = c(10, 20, 12, 22)
)
clustering_result <- nmr_peak_clustering(peak_data, num_clusters = 2)
peak_data <- clustering_result$peak_data
peak_table <- nmr_build_peak_table(peak_data)
stopifnot(ncol(peak_table) == 2)

Description

Set/Return the full spectra matrix

Usage

nmr_data(nmr_dataset, ...)

## S3 method for class 'nmr_dataset_1D'
nmr_data(nmr_dataset, what = "data_1r", ...)

nmr_data(nmr_dataset, ...) <- value

## S3 replacement method for class 'nmr_dataset_1D'
nmr_data(nmr_dataset, what = "data_1r", ...) <- value

Arguments

Value

a matrix

The given nmr_dataset

See Also

Other import/export functions: Pipelines, files_to_rDolphin(), load_and_save_functions, nmr_meta_export(), nmr_read_bruker_fid(), nmr_read_samples(), nmr_zip_bruker_samples(), save_files_to_rDolphin(), save_profiling_output(), to_ChemoSpec()

Examples

dataset_rds <- system.file("extdata", "nmr_dataset.rds", package = "AlpsNMR")
dataset_1D <- nmr_dataset_load(dataset_rds)
dataset_data <- nmr_data(dataset_1D)
dataset_rds <- system.file("extdata", "nmr_dataset.rds", package = "AlpsNMR")
dataset_1D <- nmr_dataset_load(dataset_rds)
dataset_1D_data <- nmr_data(dataset_1D)

Description

Export 1D NMR data to SummarizedExperiment

Usage

nmr_data_1r_to_SummarizedExperiment(nmr_dataset)

Arguments

Value

SummarizedExperiment An SummarizedExperiment object (unmodified)

Examples

dir_to_demo_dataset <- system.file("dataset-demo", package = "AlpsNMR")
dataset <- nmr_read_samples_dir(dir_to_demo_dataset)
dataset_1D <- nmr_interpolate_1D(dataset, axis = c(min = -0.5, max = 10, by = 2.3E-4))
se <- nmr_data_1r_to_SummarizedExperiment(dataset_1D)

Description

Data analysis on AlpsNMR can be performed on both nmr_dataset_1D full spectra as well as nmr_dataset_peak_table peak tables.

Usage

nmr_data_analysis(
  dataset,
  y_column,
  identity_column,
  external_val,
  internal_val,
  data_analysis_method,
  .enable_parallel = TRUE
)

Arguments

Details

The workflow consists of a double cross validation strategy using random subsampling for splitting into train and test sets. The classification model and the metric to choose the best model can be customized (see new_nmr_data_analysis_method()), but for now only a PLSDA classification model with a best area under ROC curve metric is implemented (see the examples here and plsda_auroc_vip_method)

Value

A list with the following elements:

• train_test_partitions: A list with the indices used in train and test on each of the cross-validation iterations

• inner_cv_results: The output returned by train_evaluate_model on each inner cross-validation

• inner_cv_results_digested: The output returned by choose_best_inner.

• outer_cv_results: The output returned by train_evaluate_model on each outer cross-validation

• outer_cv_results_digested: The output returned by train_evaluate_model_digest_outer.

Examples

# Data analysis for a table of integrated peaks

## Generate an artificial nmr_dataset_peak_table:
### Generate artificial metadata:
num_samples <- 32 # use an even number in this example
num_peaks <- 20
metadata <- data.frame(
    NMRExperiment = as.character(1:num_samples),
    Condition = rep(c("A", "B"), times = num_samples / 2)
)

### The matrix with peaks
peak_means <- runif(n = num_peaks, min = 300, max = 600)
peak_sd <- runif(n = num_peaks, min = 30, max = 60)
peak_matrix <- mapply(function(mu, sd) rnorm(num_samples, mu, sd),
    mu = peak_means, sd = peak_sd
)
colnames(peak_matrix) <- paste0("Peak", 1:num_peaks)

## Artificial differences depending on the condition:
peak_matrix[metadata$Condition == "A", "Peak2"] <-
    peak_matrix[metadata$Condition == "A", "Peak2"] + 70

peak_matrix[metadata$Condition == "A", "Peak6"] <-
    peak_matrix[metadata$Condition == "A", "Peak6"] - 60

### The nmr_dataset_peak_table
peak_table <- new_nmr_dataset_peak_table(
    peak_table = peak_matrix,
    metadata = list(external = metadata)
)

## We will use a double cross validation, splitting the samples with random
## subsampling both in the external and internal validation.
## The classification model will be a PLSDA, exploring at maximum 3 latent
## variables.
## The best model will be selected based on the area under the ROC curve
methodology <- plsda_auroc_vip_method(ncomp = 3)
model <- nmr_data_analysis(
    peak_table,
    y_column = "Condition",
    identity_column = NULL,
    external_val = list(iterations = 3, test_size = 0.25),
    internal_val = list(iterations = 3, test_size = 0.25),
    data_analysis_method = methodology
)
## Area under ROC for each outer cross-validation iteration:
model$outer_cv_results_digested$auroc
## Rank Product of the Variable Importance in the Projection
## (Lower means more important)
sort(model$outer_cv_results_digested$vip_rankproducts)

Description

Create method for NMR data analysis

Usage

new_nmr_data_analysis_method(
  train_evaluate_model,
  train_evaluate_model_params_inner,
  choose_best_inner,
  train_evaluate_model_params_outer,
  train_evaluate_model_digest_outer
)

Arguments

    function(x_train, y_train, identity_train, x_test, y_test, identity_test, ...)

function(inner_cv_results)

Value

An object encapsulating the method dependent functions that can be used with nmr_data_analysis

Description

An nmr_dataset represents a set of NMR samples. It is defined as an S3 class, and it can be treated as a regular list.

Details

It currently has the following elements:

• metadata: A list of data frames. Each data frame contains metadata of a given area (acquisition parameters, preprocessing parameters, general sample information...)

• axis: A list with length equal to the dimensionality of the data. For 1D spectra it is a list with a numeric vector

• ⁠data_*⁠: Data arrays with the actual spectra. The first index represents the sample, the rest of the indices match the length of each axis. Typically data_1r is a matrix with one sample on each row and the chemical shifts in the columns.

• num_samples: The number of samples in the dataset

See Also

Functions to save and load these objects

Other AlpsNMR dataset objects: nmr_dataset_family

Examples

metadata_1D <- list(external = data.frame(NMRExperiment = c("10", "20")))
# Sample 10 and Sample 20 can have different lengths (due to different setups)
data_fields_1D <- list(data_1r = list(runif(16), runif(32)))
# Each sample has its own axis list, with one element (because this example is 1D)
axis_1D <- list(list(1:16), list(1:32))
my_1D_data <- new_nmr_dataset(metadata_1D, data_fields_1D, axis_1D)

Description

An nmr_dataset_1D represents a set of 1D interpolated NMR samples. It is defined as an S3 class, and it can be treated as a regular list.

Details

It currently has the following elements:

• metadata: A list of data frames. Each data frame contains metadata of a given area (acquisition parameters, preprocessing parameters, general sample information...)

• axis: A numeric vector with the chemical shift axis in ppm.

• data_1r: A matrix with one sample on each row and the chemical shifts in the columns.

Examples

# Create a random spectra matrix
nsamp <- 12
npoints <- 20
dummy_ppm_axis <- seq(from = 0.2, to = 10, length.out = npoints)
dummy_spectra_matrix <- matrix(runif(nsamp * npoints), nrow = nsamp, ncol = npoints)
metadata <- list(external = data.frame(
    NMRExperiment = paste0("Sample", 1:12),
    DummyClass = c("a", "b")
))
dummy_nmr_dataset_1D <- new_nmr_dataset_1D(
    ppm_axis = dummy_ppm_axis,
    data_1r = dummy_spectra_matrix,
    metadata = metadata
)

Description

The AlpsNMR package defines and uses several objects to manage NMR Data.

Details

These objects share some structure and functions, so it makes sense to have an abstract class to ensure that the shared structures are compatible

See Also

Functions to save and load these objects

Other AlpsNMR dataset objects: nmr_dataset

Description

An nmr_dataset_peak_table represents a peak table with metadata. It is defined as an S3 class, and it can be treated as a regular list.

Usage

## S3 method for class 'nmr_dataset_peak_table'
as.data.frame(x, ...)

Arguments

Details

• metadata: A list of data frames. Each data frame contains metadata. Usually the list only has one data frame named "external".

• peak_table: A matrix with one sample on each row and the peaks in the columns

Value

A data frame with the sample metadata and the peak table

Methods (by generic)

• as.data.frame(nmr_dataset_peak_table): Convert to a data frame

Description

Export nmr_dataset_peak_table to SummarizedExperiment

Usage

nmr_dataset_peak_table_to_SummarizedExperiment(nmr_peak_table)

Arguments

Value

SummarizedExperiment object (unmodified)

Examples

dir_to_demo_dataset <- system.file("dataset-demo", package = "AlpsNMR")
dataset <- nmr_read_samples_dir(dir_to_demo_dataset)
dataset_1D <- nmr_interpolate_1D(dataset, axis = c(min = -0.5, max = 10, by = 2.3E-4))
meta <- file.path(dir_to_demo_dataset, "dummy_metadata.xlsx")
metadata <- readxl::read_excel(meta, sheet = 1)
dataset_1D <- nmr_meta_add(dataset_1D, metadata = metadata, by = "NMRExperiment")
metadata <- list(external = dataset_1D[["metadata"]][["external"]])
peak_table <- nmr_data(dataset_1D)
nmr_peak_table <- new_nmr_dataset_peak_table(peak_table, metadata)
se <- nmr_dataset_peak_table_to_SummarizedExperiment(nmr_peak_table)

Description

The function detects peaks on an nmr_dataset_1D object, using speaq::detectSpecPeaks. detectSpecPeaks divides the whole spectra into smaller segments and uses MassSpecWavelet::peakDetectionCWT for peak detection.

Usage

nmr_detect_peaks(
  nmr_dataset,
  nDivRange_ppm = 0.1,
  scales = seq(1, 16, 2),
  baselineThresh = NULL,
  SNR.Th = 3,
  range_without_peaks = c(9.5, 10),
  fit_lorentzians = FALSE,
  verbose = FALSE
)

Arguments

Details

Optionally afterwards, the peak apex and the peak inflection points are used to efficiently adjust a lorentzian to each peak, and compute the peak area and width, as well as the error of the fit. These peak features can be used afterwards to reject false detections.

Value

A data frame with the NMRExperiment, the sample index, the position in ppm and index and the peak intensity

See Also

nmr_align for peak alignment with the detected peak table

Peak_detection

Other peak detection functions: Pipelines, nmr_baseline_threshold(), nmr_detect_peaks_plot(), nmr_detect_peaks_plot_overview(), nmr_detect_peaks_tune_snr(), nmr_identify_regions_blood(), nmr_identify_regions_cell(), nmr_identify_regions_urine(), nmr_integrate_regions()

Description

Plot peak detection results

Usage

nmr_detect_peaks_plot(
  nmr_dataset,
  peak_data,
  NMRExperiment = NULL,
  peak_id = NULL,
  accepted_only = NULL,
  ...
)

Arguments

Value

Plot peak detection results

See Also

Peak_detection nmr_detect_peaks

Other peak detection functions: Pipelines, nmr_baseline_threshold(), nmr_detect_peaks(), nmr_detect_peaks_plot_overview(), nmr_detect_peaks_tune_snr(), nmr_identify_regions_blood(), nmr_identify_regions_cell(), nmr_identify_regions_urine(), nmr_integrate_regions()

Other peak detection functions: Pipelines, nmr_baseline_threshold(), nmr_detect_peaks(), nmr_detect_peaks_plot_overview(), nmr_detect_peaks_tune_snr(), nmr_identify_regions_blood(), nmr_identify_regions_cell(), nmr_identify_regions_urine(), nmr_integrate_regions()

Description

This plot allows to explore the performance of the peak detection across all the samples, by summarizing how many peaks are detected on each sample at each chemical shift range.

Usage

nmr_detect_peaks_plot_overview(
  peak_data,
  ppm_breaks = pretty(range(peak_data$ppm), n = 20),
  accepted_only = TRUE
)

Arguments

Details

You can use this plot to find differences in the number of detected peaks across your dataset, and then use nmr_detect_peaks_plot() to have a finer look at specific samples and chemical shifts, and assess graphically that the peak detection results that you have are correct.

Value

A scatter plot, with samples on one axis and chemical shift bins in the other axis. The size of each dot represents the number of peaks found on a sample within a chemical shift range.

See Also

Peak_detection

Other peak detection functions: Pipelines, nmr_baseline_threshold(), nmr_detect_peaks(), nmr_detect_peaks_plot(), nmr_detect_peaks_tune_snr(), nmr_identify_regions_blood(), nmr_identify_regions_cell(), nmr_identify_regions_urine(), nmr_integrate_regions()

Description

Plot multiple peaks from a peak list

Usage

nmr_detect_peaks_plot_peaks(
  nmr_dataset,
  peak_data,
  peak_ids,
  caption = paste("{peak_id}", "(NMRExp.\u00A0{NMRExperiment},",
    "\u03B3(ppb)\u00a0=\u00a0{gamma_ppb},", "\narea\u00a0=\u00a0{area},",
    "nrmse\u00a0=\u00a0{norm_rmse})")
)

Arguments

Value

A plot object

Description

Diagnose SNR threshold in peak detection

Usage

nmr_detect_peaks_tune_snr(
  ds,
  NMRExperiment = NULL,
  SNR_thresholds = seq(from = 2, to = 6, by = 0.1),
  ...
)

Arguments

Value

A list with the following elements:

• peaks_detected: A data frame with the columns from the nmr_detect_peaks output and an additional column SNR_threshold with the threshold used on each row.

• num_peaks_per_region: A summary of the peaks_detected table, with the number of peaks detected on each chemical shift region

• plot_num_peaks_per_region: A visual representation of num_peaks_per_region

• plot_spectrum_and_detections: A visual representation of the spectrum and the peaks detected with each SNR threshold. Use plotly::ggplotly or plot_interactive on this to zoom and explore the results.

See Also

nmr_detect_peaks

Other peak detection functions: Pipelines, nmr_baseline_threshold(), nmr_detect_peaks(), nmr_detect_peaks_plot(), nmr_detect_peaks_plot_overview(), nmr_identify_regions_blood(), nmr_identify_regions_cell(), nmr_identify_regions_urine(), nmr_integrate_regions()

Description

Excludes a given region (for instance to remove the water peak)

Usage

nmr_exclude_region(samples, exclude = list(water = c(4.7, 5)))

## S3 method for class 'nmr_dataset_1D'
nmr_exclude_region(samples, exclude = list(water = c(4.7, 5)))

Arguments

Value

The same object, with the regions excluded

See Also

Other basic functions: nmr_normalize()

Examples

nmr_dataset <- nmr_dataset_load(system.file("extdata", "nmr_dataset.rds", package = "AlpsNMR"))
exclude_regions <- list(water = c(5.1, 4.5))
nmr_dataset <- nmr_exclude_region(nmr_dataset, exclude = exclude_regions)

nmr_dataset <- nmr_dataset_load(system.file("extdata", "nmr_dataset.rds", package = "AlpsNMR"))
exclude_regions <- list(water = c(5.1, 4.5))
nmr_dataset <- nmr_exclude_region(nmr_dataset, exclude = exclude_regions)

Description

Export 1D NMR data to a CSV file

Usage

nmr_export_data_1r(nmr_dataset, filename)

Arguments

Value

The nmr_dataset object (unmodified)

Examples

dir_to_demo_dataset <- system.file("dataset-demo", package = "AlpsNMR")
dataset <- nmr_read_samples_dir(dir_to_demo_dataset)
dataset_1D <- nmr_interpolate_1D(dataset, axis = c(min = -0.5, max = 10, by = 2.3E-4))
# nmr_export_data_1r(dataset_1D, "exported_nmr_dataset")

Description

Compute peak to peak distances

Usage

nmr_get_peak_distances(peak_data, same_sample_dist_factor = 3)

Arguments

Value

A dist object with the peak2peak distances

Examples

peak_data <- data.frame(
    NMRExperiment = c("10", "10", "20", "20"),
    peak_id = paste0("Peak", 1:4),
    ppm = c(1, 2, 1.1, 3)
)
peak2peak_dist <- nmr_get_peak_distances(peak_data)
stopifnot(abs(as.numeric(peak2peak_dist) - c(6, 0.1, 2, 0.9, 1, 6)) < 1E-8)

Description

Identify given regions and return a data frame with plausible assignations in human plasma/serum samples.

Usage

nmr_identify_regions_blood(
  ppm_to_assign,
  num_proposed_compounds = 3,
  verbose = FALSE
)

Arguments

Value

a data frame with plausible assignations.

See Also

Other peak detection functions: Pipelines, nmr_baseline_threshold(), nmr_detect_peaks(), nmr_detect_peaks_plot(), nmr_detect_peaks_plot_overview(), nmr_detect_peaks_tune_snr(), nmr_identify_regions_cell(), nmr_identify_regions_urine(), nmr_integrate_regions()

Other peak integration functions: Pipelines, get_integration_with_metadata(), nmr_identify_regions_cell(), nmr_identify_regions_urine(), nmr_integrate_peak_positions(), nmr_integrate_regions()

Examples

# We identify regions from from the corresponding ppm storaged in a vector.
ppm_to_assign <- c(
    4.060960203, 3.048970634, 2.405935596,
    3.24146865, 0.990616851, 1.002075066, 0.955325548
)
identification <- nmr_identify_regions_blood(ppm_to_assign)

Description

Identify given regions and return a data frame with plausible assignations in cell samples.

Usage

nmr_identify_regions_cell(
  ppm_to_assign,
  num_proposed_compounds = 3,
  verbose = FALSE
)

Arguments

Value

a data frame with plausible assignations.

See Also

Other peak detection functions: Pipelines, nmr_baseline_threshold(), nmr_detect_peaks(), nmr_detect_peaks_plot(), nmr_detect_peaks_plot_overview(), nmr_detect_peaks_tune_snr(), nmr_identify_regions_blood(), nmr_identify_regions_urine(), nmr_integrate_regions()

Other peak integration functions: Pipelines, get_integration_with_metadata(), nmr_identify_regions_blood(), nmr_identify_regions_urine(), nmr_integrate_peak_positions(), nmr_integrate_regions()

Examples

# We identify regions from from the corresponding ppm storaged in a vector.
ppm_to_assign <- c(
    4.060960203, 3.048970634, 2.405935596,
    3.24146865, 0.990616851, 1.002075066, 0.955325548
)
identification <- nmr_identify_regions_cell(ppm_to_assign, num_proposed_compounds = 3)

Description

Identify given regions and return a data frame with plausible assignations in human urine samples. The data frame contains the column "Bouatra_2013" showing if the proposed metabolite was reported in this publication as regular urinary metabolite.

Usage

nmr_identify_regions_urine(
  ppm_to_assign,
  num_proposed_compounds = 5,
  verbose = FALSE
)

Arguments

Value

a data frame with plausible assignations.

See Also

Other peak detection functions: Pipelines, nmr_baseline_threshold(), nmr_detect_peaks(), nmr_detect_peaks_plot(), nmr_detect_peaks_plot_overview(), nmr_detect_peaks_tune_snr(), nmr_identify_regions_blood(), nmr_identify_regions_cell(), nmr_integrate_regions()

Other peak integration functions: Pipelines, get_integration_with_metadata(), nmr_identify_regions_blood(), nmr_identify_regions_cell(), nmr_integrate_peak_positions(), nmr_integrate_regions()

Examples

# We identify regions from from the corresponding ppm storaged in a vector.
ppm_to_assign <- c(
    4.060960203, 3.048970634, 2.405935596,
    3.24146865, 0.990616851, 1.002075066, 0.955325548
)
identification <- nmr_identify_regions_urine(ppm_to_assign, num_proposed_compounds = 5)

Description

The function allows the integration of a given ppm vector with a specific width.

Usage

nmr_integrate_peak_positions(
  samples,
  peak_pos_ppm,
  peak_width_ppm = 0.006,
  ...
)

Arguments

Value

Integrate peak positions

See Also

Other peak integration functions: Pipelines, get_integration_with_metadata(), nmr_identify_regions_blood(), nmr_identify_regions_cell(), nmr_identify_regions_urine(), nmr_integrate_regions()

Other nmr_dataset_1D functions: [.nmr_dataset_1D(), format.nmr_dataset_1D(), get_integration_with_metadata(), is.nmr_dataset_1D(), nmr_integrate_regions(), nmr_meta_add(), nmr_meta_export(), nmr_meta_get(), nmr_meta_get_column(), nmr_ppm_resolution(), print.nmr_dataset_1D()

Description

Integrate given regions and return a data frame with them

Usage

nmr_integrate_regions(samples, regions, ...)

## S3 method for class 'nmr_dataset_1D'
nmr_integrate_regions(
  samples,
  regions,
  fix_baseline = FALSE,
  excluded_regions_as_zero = FALSE,
  set_negative_areas_to_zero = FALSE,
  ...
)

Arguments

Value

An nmr_dataset_peak_table object

See Also

Other peak detection functions: Pipelines, nmr_baseline_threshold(), nmr_detect_peaks(), nmr_detect_peaks_plot(), nmr_detect_peaks_plot_overview(), nmr_detect_peaks_tune_snr(), nmr_identify_regions_blood(), nmr_identify_regions_cell(), nmr_identify_regions_urine()

Other peak integration functions: Pipelines, get_integration_with_metadata(), nmr_identify_regions_blood(), nmr_identify_regions_cell(), nmr_identify_regions_urine(), nmr_integrate_peak_positions()

Other nmr_dataset_1D functions: [.nmr_dataset_1D(), format.nmr_dataset_1D(), get_integration_with_metadata(), is.nmr_dataset_1D(), nmr_integrate_peak_positions(), nmr_meta_add(), nmr_meta_export(), nmr_meta_get(), nmr_meta_get_column(), nmr_ppm_resolution(), print.nmr_dataset_1D()

Examples

# Creating a dataset
dataset <- new_nmr_dataset_1D(
    ppm_axis = 1:10,
    data_1r = matrix(sample(0:99, replace = TRUE), nrow = 10),
    metadata = list(external = data.frame(NMRExperiment = c(
        "10",
        "20", "30", "40", "50", "60", "70", "80", "90", "100"
    )))
)

# Integrating selected regions
peak_table_integration <- nmr_integrate_regions(
    samples = dataset,
    regions = list(ppm = c(2, 5))
)

# Creating a dataset
dataset <- new_nmr_dataset_1D(
    ppm_axis = 1:10,
    data_1r = matrix(sample(0:99, replace = TRUE), nrow = 10),
    metadata = list(external = data.frame(NMRExperiment = c(
        "10",
        "20", "30", "40", "50", "60", "70", "80", "90", "100"
    )))
)

# Integrating selected regions
peak_table_integration <- nmr_integrate_regions(
    samples = dataset,
    regions = list(ppm = c(2, 5)),
    fix_baseline = FALSE
)

Description

Interpolate a set of 1D NMR Spectra

Usage

nmr_interpolate_1D(samples, axis = c(min = 0.2, max = 10, by = 8e-04))

## S3 method for class 'nmr_dataset'
nmr_interpolate_1D(samples, axis = c(min = 0.2, max = 10, by = 8e-04))

Arguments

Value

Interpolate a set of 1D NMR Spectra

Examples

dir_to_demo_dataset <- system.file("dataset-demo", package = "AlpsNMR")
dataset <- nmr_read_samples_dir(dir_to_demo_dataset)
dataset_1D <- nmr_interpolate_1D(dataset, axis = c(min = -0.5, max = 10, by = 2.3E-4))

dir_to_demo_dataset <- system.file("dataset-demo", package = "AlpsNMR")
dataset <- nmr_read_samples_dir(dir_to_demo_dataset)
dataset_1D <- nmr_interpolate_1D(dataset, axis = c(min = -0.5, max = 10, by = 2.3E-4))

Description

This is useful to add metadata to datasets that can be later used for plotting spectra or further analysis (PCA...).

Usage

nmr_meta_add(nmr_data, metadata, by = "NMRExperiment")

nmr_meta_add_tidy_excel(nmr_data, excel_file)

Arguments

Value

The nmr_dataset_family object with the added metadata

See Also

Other metadata functions: Pipelines, nmr_meta_export(), nmr_meta_get(), nmr_meta_get_column(), nmr_meta_groups()

Other nmr_dataset functions: nmr_meta_export(), nmr_meta_get(), nmr_meta_get_column()

Other nmr_dataset_1D functions: [.nmr_dataset_1D(), format.nmr_dataset_1D(), get_integration_with_metadata(), is.nmr_dataset_1D(), nmr_integrate_peak_positions(), nmr_integrate_regions(), nmr_meta_export(), nmr_meta_get(), nmr_meta_get_column(), nmr_ppm_resolution(), print.nmr_dataset_1D()

Other nmr_dataset_peak_table functions: nmr_meta_export(), nmr_meta_get(), nmr_meta_get_column()

Examples

# Load a demo dataset with four samples:
dataset <- system.file("dataset-demo", package = "AlpsNMR")
nmr_dataset <- nmr_read_samples_dir(dataset)

# At first we just have the NMRExperiment column
nmr_meta_get(nmr_dataset, groups = "external")
# Get a table with NMRExperiment -> SubjectID
dummy_metadata <- system.file("dataset-demo", "dummy_metadata.xlsx", package = "AlpsNMR")
NMRExp_SubjID <- readxl::read_excel(dummy_metadata, sheet = 1)

NMRExp_SubjID
# We can link the SubjectID column of the first excel into the dataset
nmr_dataset <- nmr_meta_add(nmr_dataset, NMRExp_SubjID, by = "NMRExperiment")
nmr_meta_get(nmr_dataset, groups = "external")
# The second excel can use the SubjectID:
SubjID_Age <- readxl::read_excel(dummy_metadata, sheet = 2)
SubjID_Age
# Add the metadata by its SubjectID:
nmr_dataset <- nmr_meta_add(nmr_dataset, SubjID_Age, by = "SubjectID")
# The final loaded metadata:
nmr_meta_get(nmr_dataset, groups = "external")

# Read a tidy excel file:

dataset <- system.file("dataset-demo", package = "AlpsNMR")
nmr_dataset <- nmr_read_samples_dir(dataset)

# At first we just have the NMRExperiment column
nmr_meta_get(nmr_dataset, groups = "external")
# Get a table with NMRExperiment -> SubjectID
dummy_metadata <- system.file("dataset-demo", "dummy_metadata.xlsx", package = "AlpsNMR")

nmr_dataset <- nmr_meta_add_tidy_excel(nmr_dataset, dummy_metadata)
# Updated Metadata:
nmr_meta_get(nmr_dataset, groups = "external")

Description

Export Metadata to an Excel file

Usage

nmr_meta_export(
  nmr_dataset,
  xlsx_file,
  groups = c("info", "orig", "title", "external")
)

Arguments

Value

The Excel file name

See Also

Other metadata functions: Pipelines, nmr_meta_add(), nmr_meta_get(), nmr_meta_get_column(), nmr_meta_groups()

Other nmr_dataset functions: nmr_meta_add(), nmr_meta_get(), nmr_meta_get_column()

Other nmr_dataset_1D functions: [.nmr_dataset_1D(), format.nmr_dataset_1D(), get_integration_with_metadata(), is.nmr_dataset_1D(), nmr_integrate_peak_positions(), nmr_integrate_regions(), nmr_meta_add(), nmr_meta_get(), nmr_meta_get_column(), nmr_ppm_resolution(), print.nmr_dataset_1D()

Other nmr_dataset_peak_table functions: nmr_meta_add(), nmr_meta_get(), nmr_meta_get_column()

Other import/export functions: Pipelines, files_to_rDolphin(), load_and_save_functions, nmr_data(), nmr_read_bruker_fid(), nmr_read_samples(), nmr_zip_bruker_samples(), save_files_to_rDolphin(), save_profiling_output(), to_ChemoSpec()

Examples

dir_to_demo_dataset <- system.file("dataset-demo", package = "AlpsNMR")
dataset <- nmr_read_samples_dir(dir_to_demo_dataset)
# nmr_meta_export(dataset, "metadata.xlsx")

Description

Get metadata

Usage

nmr_meta_get(samples, columns = NULL, groups = NULL)

Arguments

Value

a data frame with the injection metadata

See Also

Other metadata functions: Pipelines, nmr_meta_add(), nmr_meta_export(), nmr_meta_get_column(), nmr_meta_groups()

Other nmr_dataset functions: nmr_meta_add(), nmr_meta_export(), nmr_meta_get_column()

Other nmr_dataset_1D functions: [.nmr_dataset_1D(), format.nmr_dataset_1D(), get_integration_with_metadata(), is.nmr_dataset_1D(), nmr_integrate_peak_positions(), nmr_integrate_regions(), nmr_meta_add(), nmr_meta_export(), nmr_meta_get_column(), nmr_ppm_resolution(), print.nmr_dataset_1D()

Other nmr_dataset_peak_table functions: nmr_meta_add(), nmr_meta_export(), nmr_meta_get_column()

Examples

dir_to_demo_dataset <- system.file("dataset-demo", package = "AlpsNMR")
dataset <- nmr_read_samples_dir(dir_to_demo_dataset)
metadata <- nmr_meta_get(dataset)

Description

Get a single metadata column

Usage

nmr_meta_get_column(samples, column = "NMRExperiment")

Arguments

Value

A vector with the column

See Also

Other metadata functions: Pipelines, nmr_meta_add(), nmr_meta_export(), nmr_meta_get(), nmr_meta_groups()

Other nmr_dataset functions: nmr_meta_add(), nmr_meta_export(), nmr_meta_get()

Other nmr_dataset_1D functions: [.nmr_dataset_1D(), format.nmr_dataset_1D(), get_integration_with_metadata(), is.nmr_dataset_1D(), nmr_integrate_peak_positions(), nmr_integrate_regions(), nmr_meta_add(), nmr_meta_export(), nmr_meta_get(), nmr_ppm_resolution(), print.nmr_dataset_1D()

Other nmr_dataset_peak_table functions: nmr_meta_add(), nmr_meta_export(), nmr_meta_get()

Examples

dir_to_demo_dataset <- system.file("dataset-demo", package = "AlpsNMR")
dataset <- nmr_read_samples_dir(dir_to_demo_dataset)
metadata_column <- nmr_meta_get_column(dataset)

Description

Get the names of metadata groups

Usage

nmr_meta_groups(samples)

Arguments

Value

A character vector with group names

See Also

Other metadata functions: Pipelines, nmr_meta_add(), nmr_meta_export(), nmr_meta_get(), nmr_meta_get_column()

Examples

dir_to_demo_dataset <- system.file("dataset-demo", package = "AlpsNMR")
dataset <- nmr_read_samples_dir(dir_to_demo_dataset)
metadata_column <- nmr_meta_get_column(dataset)

Description

The nmr_normalize function is used to normalize all the samples according to a given criteria.

Usage

nmr_normalize(
  samples,
  method = c("area", "max", "value", "region", "pqn", "none"),
  ...
)

nmr_normalize_extra_info(samples)

Arguments

Details

The aim is to correct from changes between samples, so no matter the criteria used to normalize, once we get the factors (e.g. the areas), we divide them by the median normalization factor to avoid introducing global scaling factors.

The nmr_normalize_extra_info function is used to extract additional information after the normalization. Typically, we want to know what was the actual normalization factor applied to each sample. The extra information includes a plot, representing the dispersion of the normalization factor for each sample.

Value

The nmr_dataset_1D object, with the samples normalized. Further information for diagnostic of the normalization process is also saved and can be extracted by calling nmr_normalize_extra_info() afterwards.

See Also

Other basic functions: nmr_exclude_region()

Examples

nmr_dataset <- nmr_dataset_load(system.file("extdata", "nmr_dataset.rds", package = "AlpsNMR"))
nmr_dataset <- nmr_normalize(nmr_dataset, method = "area")
norm_dataset <- nmr_normalize(nmr_dataset)
norm_dataset$plot
nmr_dataset <- nmr_dataset_load(system.file("extdata", "nmr_dataset.rds", package = "AlpsNMR"))
nmr_dataset <- nmr_normalize(nmr_dataset, method = "area")
norm_extra_info <- nmr_normalize_extra_info(nmr_dataset)
norm_extra_info$plot

Description

This function builds a PCA model with all the NMR spectra. Regions with zero values (excluded regions) or near-zero variance regions are automatically excluded from the analysis.

Usage

nmr_pca_build_model(
  nmr_dataset,
  ncomp = NULL,
  center = TRUE,
  scale = FALSE,
  ...
)

## S3 method for class 'nmr_dataset_1D'
nmr_pca_build_model(
  nmr_dataset,
  ncomp = NULL,
  center = TRUE,
  scale = FALSE,
  ...
)

Arguments

Value

A PCA model as given by mixOmics::pca with two additional attributes:

• nmr_data_axis containing the full ppm axis

• nmr_included with the data points included in the model These attributes are used internally by AlpsNMR to create loading plots

See Also

Other PCA related functions: nmr_pca_outliers(), nmr_pca_outliers_filter(), nmr_pca_outliers_plot(), nmr_pca_outliers_robust(), nmr_pca_plots

Examples

dir_to_demo_dataset <- system.file("dataset-demo", package = "AlpsNMR")
dataset <- nmr_read_samples_dir(dir_to_demo_dataset)
dataset_1D <- nmr_interpolate_1D(dataset, axis = c(min = -0.5, max = 10, by = 2.3E-4))
model <- nmr_pca_build_model(dataset_1D)

dir_to_demo_dataset <- system.file("dataset-demo", package = "AlpsNMR")
dataset <- nmr_read_samples_dir(dir_to_demo_dataset)
dataset_1D <- nmr_interpolate_1D(dataset, axis = c(min = -0.5, max = 10, by = 2.3E-4))
model <- nmr_pca_build_model(dataset_1D)

Description

Compute PCA residuals and score distance for each sample

Usage

nmr_pca_outliers(
  nmr_dataset,
  pca_model,
  ncomp = NULL,
  quantile_critical = 0.975
)

Arguments

Value

A list with:

• outlier_info: A data frame with the NMRExperiment, the Q residuals and T scores

• ncomp: Number of components used to compute Q and T

• Tscore_critical, QResidual_critical: Critical values, given a quantile, for both Q and T.

See Also

Other PCA related functions: nmr_pca_build_model(), nmr_pca_outliers_filter(), nmr_pca_outliers_plot(), nmr_pca_outliers_robust(), nmr_pca_plots

Other outlier detection functions: Pipelines, nmr_pca_outliers_filter(), nmr_pca_outliers_plot(), nmr_pca_outliers_robust()

Examples

dir_to_demo_dataset <- system.file("dataset-demo", package = "AlpsNMR")
dataset <- nmr_read_samples_dir(dir_to_demo_dataset)
dataset_1D <- nmr_interpolate_1D(dataset, axis = c(min = -0.5, max = 10, by = 2.3E-4))
model <- nmr_pca_build_model(dataset_1D)
outliers_info <- nmr_pca_outliers(dataset_1D, model)

Description

Exclude outliers

Usage

nmr_pca_outliers_filter(nmr_dataset, pca_outliers)

Arguments

Value

An nmr_dataset_1D without the detected outliers

See Also

Other PCA related functions: nmr_pca_build_model(), nmr_pca_outliers(), nmr_pca_outliers_plot(), nmr_pca_outliers_robust(), nmr_pca_plots

Other outlier detection functions: Pipelines, nmr_pca_outliers(), nmr_pca_outliers_plot(), nmr_pca_outliers_robust()

Other subsetting functions: [.nmr_dataset(), [.nmr_dataset_1D(), [.nmr_dataset_peak_table(), filter.nmr_dataset_family()

Examples

dir_to_demo_dataset <- system.file("dataset-demo", package = "AlpsNMR")
dataset <- nmr_read_samples_dir(dir_to_demo_dataset)
dataset_1D <- nmr_interpolate_1D(dataset, axis = c(min = -0.5, max = 10, by = 2.3E-4))
model <- nmr_pca_build_model(dataset_1D)
outliers_info <- nmr_pca_outliers(dataset_1D, model)
dataset_whitout_outliers <- nmr_pca_outliers_filter(dataset_1D, outliers_info)