Adaptive processing of LC-MS data

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Documentation for package ‘recetox.aplcms’ version 0.13.4

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adaptive.bin Adaptive binning
adjust.time Adjust retention time across spectra.
aggregate_by_rt Aggregate the intensities and select median mz for features with identical rt.
augment_known_table Add newly detected aligned features to a known features table.
clean_data_matrix Replace NA values by zero, relocate 'sample_names' column to the very beginning and convert to a tibble
comb Combines the output (i.e. metadata, intensity and RT) from different clusters to one respective tibble.
compute_boundaries Compute bounds of area using given peak and mass valley points.
compute_clusters Compute clusters of mz and rt and assign cluster id to individual features.
compute_clusters_simple Compute clusters using simple grouping based on numeric thresholds.
compute_curr_rec_with_enough_peaks Compute the rectangle around recovered features given that enough peaks are present.
compute_delta_rt Compute custom smoothed h-method derivative of function.
compute_mass_density Compute the density function of mz values.
compute_min_mz_tolerance Compute minimum mz tolerance to use.
compute_mu_sc_std Compute interpolated retention time, its standard deviation, and intensity values,.
compute_peaks_and_valleys Compute peaks and valleys of density function.
compute_pks_vlys_rt Compute peaks and valleys which have at least 'recover_min_count' peaks.
compute_rectangle Compute rectangle around feature with 'aligned_feature_mz' and 'target_rt' for recovery.
compute_rt_tol_relative Compute rt relative tolerance to use.
compute_target_times Compute target times for regions of interest for recovery.
compute_uniq_grp Computes unique groups
concatenate_feature_tables Concatenate multiple feature lists and add the sample id (origin of feature) as additional column.
count_peaks Count the number of peaks in all valleys
create_aligned_feature_table Align peaks from spectra into a feature table.
create_features_from_cluster Group the mz and RT for particular cluster.
create_intensity_row Compute summed area for each sample
create_output Create a list containing 3 tibbles: metadata, intensities and RTs.
create_rt_row Compute median RT for each sample
duplicate.row.remove Custom way of removing duplicate rows from a specifically formatted table.
enrich_table_by_known_features Add entries from the known features table to the aligned table.
filter_based_on_density Subset data within lower and upper bound from density estimation
find.match Internal function: finding the best match between a set of detected features and a set of known features.
find.tol.time An internal function that find elution time tolerance level.
find.turn.point Find peaks and valleys of a curve.
find_mz_match Compute matches between mz array and specific mass value with a tolerance.
find_mz_tolerance An internal function that is not supposed to be directly accessed by the user. Find m/z tolerance level.
find_optima Compute the kernel density estimation and find the peaks and valleys of a smooth curve.
get_custom_rt_tol Compute custom chromatographic tolerance.
get_features_in_rt_range Get peaks and valleys of smoothed rt values in range.
get_mzrange_bound_indices Compute range of valley indices which are in mz_tol range around aligned_feature_mass.
get_rt_region_indices Get indices where rt in 'features' is within 'rt_tol' of 'target_time'.
get_single_occurrence_mask Get boolean mask for values that occur only once.
get_times_to_use Get retention time values to use
hybrid Runs features extraction in hybrid mode.
interpol.area Interpolate missing intensities and calculate the area for a single EIC.
l2normalize Normalize vector so that sum(vec) = 1
label_val_to_keep This function labels the indices of values kept to perform further calculations
load.lcms Loading LC/MS data.
load_data Load data either from cache or load raw file and detect peaks.
load_file Load raw data from file
match_peaks Match peaks from sample table to already known peaks via similar m/z and rt.
merge_features_and_known_table A wrapper function to join knowledge from aligned features and known table.
peak_characterize Internal function: Updates the information of a feature for the known feature table.
predict_mz_break_indices Predict the indices for the valley points with low mass density.
predict_smoothed_rt Computes the smoothed retention times by using The Nadaraya-Watson kernel regression estimate function.
prof.to.features Generate feature table from noise-removed LC/MS profile.
recover.weaker Recover weak signals in some profiles that is not identified as a peak, but corresponds to identified peaks in other spectra.
refine_selection Refine the selection based on mz and rt differences.
remove_noise Filter noise and detect peaks from LC/MS data in CDF format
rm.ridge Removing long ridges at the same m/z.
run_filter Continuity index.
semi.sup Semi-supervised feature detection
two.step.hybrid Two step hybrid feature detection.
unsupervised Runs features extraction in unsupervised mode.
validate_contents Validates if the data is present in more than "min_occurence" of samples.