nmr_data_analysis {AlpsNMR} | R Documentation |
Data analysis on AlpsNMR can be performed on both nmr_dataset_1D full spectra as well as nmr_dataset_peak_table peak tables.
nmr_data_analysis( dataset, y_column, identity_column, external_val, internal_val, data_analysis_method )
dataset |
An nmr_dataset_family object |
y_column |
A string with the name of the y column (present in the metadata of the dataset) |
identity_column |
|
external_val, internal_val |
A list with two elements: |
data_analysis_method |
An nmr_data_analysis_method object |
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)
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
.
# 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), stringsAsFactors = FALSE ) ### 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)