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() deprecated: 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
msn_calc_nl(): Calculate neutral losses from precursor ion mass and fragment ion masses
msn_scale(): Scale intensities in MSn spectra to the highest value within each spectrum
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