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metamorphr

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install.packages('metamorphr')

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355

Version

0.3.0

License

MIT + file LICENSE

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Maintainer

Yannik Schermer

Last Published

March 4th, 2026

Functions in metamorphr (0.3.0)

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
impute_global_lowest

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

Filter Features based on their occurrence in blank samples
impute_bpca

Impute missing values using Bayesian PCA
impute_median

Impute missing values by replacing them with the Feature median
filter_neutral_loss

Filter Features based on occurrence of neutral losses
impute_min

Impute missing values by replacing them with the Feature minimum
join_metadata

Join a featuretable and sample metadata
impute_user_value

Impute missing values by replacing them with a user-provided value
normalize_pqn

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

Normalize intensities across samples using standard Quantile Normalization
impute_mean

Impute missing values by replacing them with the Feature mean
msn_scale

Scale intensities in MSn spectra to the highest value within each spectrum
impute_lod

Impute missing values by replacing them with the Feature 'Limit of Detection'
formula_to_mass

Calculate the monoisotopic mass from a given formula
normalize_quantile_group

Normalize intensities across samples using grouped Quantile Normalization
normalize_cyclic_loess

Normalize intensities across samples using cyclic LOESS normalization
normalize_quantile_batch

Normalize intensities across samples using grouped Quantile Normalization with multiple batches
scale_level

Scale intensities of features using level scaling
scale_pareto

Scale intensities of features using Pareto scaling
normalize_median

Normalize intensities across samples by dividing by the sample median
normalize_factor

Normalize intensities across samples using a normalization factor
normalize_quantile_smooth

Normalize intensities across samples using smooth Quantile Normalization (qsmooth)
normalize_ref

Normalize intensities across samples using a reference feature
scale_range

Scale intensities of features using range scaling
filter_grouped_mv

Group-based feature filtering
impute_rf

Impute missing values using random forest
scale_vast

Scale intensities of features using vast scaling
metamorphr-package

metamorphr: Tidy and Streamlined Metabolomics Data Workflows
impute_svd

Impute missing values using Singular Value Decomposition (SVD)
scale_vast_grouped

Scale intensities of features using grouped vast scaling
plot_volcano

Draws a Volcano Plot or performs calculations necessary to draw one manually
scale_auto

Scale intensities of features using autoscale
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
summary_featuretable

General information about a feature table and sample-wise summary
toy_mgf

A small toy data set containing MSn spectra
impute_knn

Impute missing values using nearest neighbor averaging
impute_lls

Impute missing values using Local Least Squares (LLS)
scale_center

Center intensities of features around zero
%>%

Pipe operator
transform_log

Transforms the intensities by calculating their log
msn_calc_nl

Calculate neutral losses from precursor ion mass and fragment ion masses
read_featuretable

Read a feature table into a tidy tibble
transform_power

Transforms the intensities by calculating their nth root
read_mgf

Read a MGF file into a tidy tibble
toy_metaboscape_metadata

Sample metadata for the fictional dataset toy_metaboscape
toy_metaboscape

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

Collapse intensities of technical replicates by calculating their median
collapse_max

Collapse intensities of technical replicates by calculating their maximum
collapse_min

Collapse intensities of technical replicates by calculating their minimum
atoms

A tibble containing the NIST standard atomic weights
collapse_mean

Collapse intensities of technical replicates by calculating their mean
calc_neutral_loss

Calculate neutral losses from precursor ion mass and fragment ion masses
create_metadata_skeleton

Create a blank metadata skeleton
calc_km

Calculate the Kendrick mass
calc_nominal_km

Calculate the nominal Kendrick mass
calc_kmd

Calculate the Kendrick mass defect (KMD)
filter_mz

Filter Features based on their mass-to-charge ratios
filter_msn

Filter Features based on occurrence of fragment ions
impute_ppca

Impute missing values using Probabilistic PCA
impute_nipals

Impute missing values using NIPALS PCA