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metamorphr

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install.packages('metamorphr')

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285

Version

0.2.0

License

MIT + file LICENSE

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Maintainer

Yannik Schermer

Last Published

March 4th, 2026

Functions in metamorphr (0.2.0)

impute_nipals

Impute missing values using NIPALS PCA
filter_cv

Filter Features based on their coefficient of variation
impute_ppca

Impute missing values using Probabilistic PCA
impute_median

Impute missing values by replacing them with the Feature median
impute_svd

Impute missing values using Singular Value Decomposition (SVD)
impute_rf

Impute missing values using random forest
filter_mz

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

Filter Features based on occurrence of fragment ions
impute_lls

Impute missing values using Local Least Squares (LLS)
impute_knn

Impute missing values using nearest neighbor averaging
normalize_pqn

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

Normalize intensities across samples using standard Quantile Normalization
impute_user_value

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

Join a featuretable and sample metadata
normalize_quantile_batch

Normalize intensities across samples using grouped Quantile Normalization with multiple batches
%>%

Pipe operator
normalize_quantile_group

Normalize intensities across samples using grouped Quantile Normalization
normalize_ref

Normalize intensities across samples using a reference feature
impute_min

Impute missing values by replacing them with the Feature minimum
normalize_quantile_smooth

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

Normalize intensities across samples by dividing by the sample sum
normalize_cyclic_loess

Normalize intensities across samples using cyclic LOESS normalization
plot_pca

Draws a scores or loadings plot or performs calculations necessary to draw them manually
metamorphr-package

metamorphr: Tidy and Streamlined Metabolomics Data Workflows
impute_lod

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

Group-based feature filtering
read_mgf

Read a MGF file into a tidy tibble
toy_mgf

A small toy data set containing MSn spectra
read_featuretable

Read a feature table into a tidy tibble
scale_vast

Scale intensities of features using vast scaling
scale_range

Scale intensities of features using range scaling
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
normalize_factor

Normalize intensities across samples using a normalization factor
impute_mean

Impute missing values by replacing them with the Feature mean
transform_log

Transforms the intensities by calculating their log
scale_auto

Scale intensities of features using autoscale
summary_featuretable

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

Scale intensities of features using grouped vast scaling
scale_center

Center intensities of features around zero
normalize_median

Normalize intensities across samples by dividing by the sample median
scale_level

Scale intensities of features using level scaling
scale_pareto

Scale intensities of features using Pareto scaling
plot_volcano

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

Transforms the intensities by calculating their nth root
atoms

A tibble containing the NIST standard atomic weights
calc_kmd

Calculate the Kendrick mass defect (KMD)
calc_nominal_km

Calculate the nominal Kendrick mass
collapse_min

Collapse intensities of technical replicates by calculating their minimum
collapse_median

Collapse intensities of technical replicates by calculating their median
calc_km

Calculate the Kendrick mass
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
collapse_max

Collapse intensities of technical replicates by calculating their maximum
filter_neutral_loss

Filter Features based on occurrence of neutral losses
impute_bpca

Impute missing values using Bayesian PCA
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)
formula_to_mass

Calculate the monoisotopic mass from a given formula
filter_blank

Filter Features based on their occurrence in blank samples