Package index • metamorphr

All functions

atoms: A tibble containing the NIST standard atomic weights

calc_km(): Calculate the Kendrick mass

calc_kmd(): Calculate the Kendrick mass defect (KMD)

calc_neutral_loss(): Calculate neutral losses from precursor ion mass and fragment ion masses

calc_nominal_km(): Calculate the nominal Kendrick mass

collapse_max(): Collapse intensities of technical replicates by calculating their maximum

collapse_mean(): Collapse intensities of technical replicates by calculating their mean

collapse_median(): Collapse intensities of technical replicates by calculating their median

collapse_min(): Collapse intensities of technical replicates by calculating their minimum

create_metadata_skeleton(): Create a blank metadata skeleton

filter_blank(): Filter Features based on their occurrence in blank samples

filter_cv(): Filter Features based on their coefficient of variation

filter_global_mv(): Filter Features based on the absolute number or fraction of samples it was found in

filter_grouped_mv(): Group-based feature filtering

filter_msn(): Filter Features based on occurrence of fragment ions

filter_mz(): Filter Features based on their mass-to-charge ratios

filter_neutral_loss(): Filter Features based on occurrence of neutral losses

formula_to_mass(): Calculate the monoisotopic mass from a given formula

impute_bpca(): Impute missing values using Bayesian PCA

impute_global_lowest(): Impute missing values by replacing them with the lowest observed intensity (global)

impute_knn(): Impute missing values using nearest neighbor averaging

impute_lls(): Impute missing values using Local Least Squares (LLS)

impute_lod(): Impute missing values by replacing them with the Feature 'Limit of Detection'

impute_mean(): Impute missing values by replacing them with the Feature mean

impute_median(): Impute missing values by replacing them with the Feature median

impute_min(): Impute missing values by replacing them with the Feature minimum

impute_nipals(): Impute missing values using NIPALS PCA

impute_ppca(): Impute missing values using Probabilistic PCA

impute_rf(): Impute missing values using random forest

impute_svd(): Impute missing values using Singular Value Decomposition (SVD)

impute_user_value(): Impute missing values by replacing them with a user-provided value

join_metadata(): Join a featuretable and sample metadata

normalize_cyclic_loess(): Normalize intensities across samples using cyclic LOESS normalization

normalize_factor(): Normalize intensities across samples using a normalization factor

normalize_median(): Normalize intensities across samples by dividing by the sample median

normalize_pqn(): Normalize intensities across samples using a Probabilistic Quotient Normalization (PQN)

normalize_quantile_all(): Normalize intensities across samples using standard Quantile Normalization

normalize_quantile_batch(): Normalize intensities across samples using grouped Quantile Normalization with multiple batches

normalize_quantile_group(): Normalize intensities across samples using grouped Quantile Normalization

normalize_quantile_smooth(): Normalize intensities across samples using smooth Quantile Normalization (qsmooth)

normalize_ref(): Normalize intensities across samples using a reference feature

normalize_sum(): Normalize intensities across samples by dividing by the sample sum

plot_pca(): Draws a scores or loadings plot or performs calculations necessary to draw them manually

plot_volcano(): Draws a Volcano Plot or performs calculations necessary to draw one manually

read_featuretable(): Read a feature table into a tidy tibble

read_mgf(): Read a MGF file into a tidy tibble

scale_auto(): Scale intensities of features using autoscale

scale_center(): Center intensities of features around zero

scale_level(): Scale intensities of features using level scaling

scale_pareto(): Scale intensities of features using Pareto scaling

scale_range(): Scale intensities of features using range scaling

scale_vast(): Scale intensities of features using vast scaling

scale_vast_grouped(): Scale intensities of features using grouped vast scaling

summary_featuretable(): General information about a feature table and sample-wise summary

toy_metaboscape: A small toy data set created from a feature table in MetaboScape style

toy_metaboscape_metadata: Sample metadata for the fictional dataset toy_metaboscape

toy_mgf: A small toy data set containing MSn spectra

transform_log(): Transforms the intensities by calculating their log

transform_power(): Transforms the intensities by calculating their nth root