files_to_rDolphin {AlpsNMR} | R Documentation |
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.
files_to_rDolphin(nmr_dataset, biological_origin)
nmr_dataset |
An nmr_dataset object |
biological_origin |
String specify the type of sample (blood, urine, cell) |
a list containing:
meta_rDolphin
: metadata in rDolphin format,
NMR_spectra
: spectra matrix
ROI
: ROI template
Parameters
: parameters file
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()
Other nmr_dataset_1D functions:
[.nmr_dataset_1D()
,
computes_peak_width_ppm()
,
file_lister()
,
format.nmr_dataset_1D()
,
is.nmr_dataset_1D()
,
load_and_save_functions
,
new_nmr_dataset_1D()
,
nmr_align_find_ref()
,
nmr_baseline_removal()
,
nmr_baseline_threshold()
,
nmr_exclude_region()
,
nmr_integrate_regions()
,
nmr_interpolate_1D()
,
nmr_meta_add()
,
nmr_meta_export()
,
nmr_meta_get_column()
,
nmr_meta_get()
,
nmr_normalize()
,
nmr_pca_build_model()
,
nmr_pca_outliers_filter()
,
nmr_pca_outliers_plot()
,
nmr_pca_outliers_robust()
,
nmr_pca_outliers()
,
nmr_ppm_resolution()
,
plot.nmr_dataset_1D()
,
plot_webgl()
,
print.nmr_dataset_1D()
,
rdCV_PLS_RF_ML()
,
rdCV_PLS_RF()
,
save_files_to_rDolphin()
,
to_ChemoSpec()
,
validate_nmr_dataset_peak_table()
,
validate_nmr_dataset()
## 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)