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K-fold bootstrap and permutation over PLS-VIP

Source: R/nmr_data_analysis.R
bp_kfold_VIP_analysis.Rd

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

dataset

An nmr_dataset_family object

y_column

A string with the name of the y column (present in the metadata of the dataset)

k

Number of folds, recomended between 4 to 10

ncomp

number of components for the bootstrap models

nbootstrap

number of bootstrap dataset

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

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

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)
#> Selected VIPs are: