This package is considered a duplicate. The official version of this package is found at:https://yasche.r-universe.dev/metamorphr

Package: metamorphr 0.4.1

Yannik Schermer

metamorphr: Tidy and Streamlined Metabolomics Data Workflows

Facilitate tasks typically encountered during metabolomics data analysis including data import, filtering, missing value imputation (Stacklies et al. (2007) <doi:10.1093/bioinformatics/btm069>, Stekhoven et al. (2012) <doi:10.1093/bioinformatics/btr597>, Tibshirani et al. (2017) <doi:10.18129/B9.BIOC.IMPUTE>, Troyanskaya et al. (2001) <doi:10.1093/bioinformatics/17.6.520>), normalization (Bolstad et al. (2003) <doi:10.1093/bioinformatics/19.2.185>, Dieterle et al. (2006) <doi:10.1021/ac051632c>, Zhao et al. (2020) <doi:10.1038/s41598-020-72664-6>) transformation, centering and scaling (Van Den Berg et al. (2006) <doi:10.1186/1471-2164-7-142>) as well as statistical tests and plotting. 'metamorphr' introduces a tidy (Wickham et al. (2019) <doi:10.21105/joss.01686>) format for metabolomics data and is designed to make it easier to build elaborate analysis workflows and to integrate them with 'tidyverse' packages including 'dplyr' and 'ggplot2'.

Authors:Yannik Schermer [aut, cre, cph]

metamorphr_0.4.1.tar.gz
metamorphr_0.4.1.tar.gz(r-4.7-any)metamorphr_0.4.1.tar.gz(r-4.6-any)
metamorphr_0.4.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
metamorphr/json (API)
NEWS

# Install 'metamorphr' in R:

install.packages('metamorphr', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/yasche/metamorphr/issues

Pkgdown/docs site:https://yasche.github.io

Datasets:

atoms - A tibble containing the NIST standard atomic weights
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

On CRAN:

3.60 score 5 scripts 170 downloads 62 exports 62 dependencies

Last updated from:594805f524. Checks:4 OK. Indexed: no.

Target	Result	Time
linux-devel-x86_64	OK	190
source / vignettes	OK	303
linux-release-x86_64	OK	224
wasm-release	OK	164

Exports:%>%calc_km calc_kmd calc_neutral_loss calc_nominal_km collapse_max collapse_mean collapse_median collapse_min convert_from_matrix convert_from_wide create_metadata_skeleton filter_blank filter_cv filter_global_mv filter_grouped_mv filter_msn filter_mz filter_neutral_loss formula_to_mass impute_bpca impute_global_lowest impute_knn impute_lls impute_lod impute_mean impute_median impute_min impute_nipals impute_ppca impute_rf impute_svd impute_user_value join_metadata msn_calc_nl msn_scale normalize_cyclic_loess normalize_factor normalize_median normalize_pqn normalize_quantile_all normalize_quantile_batch normalize_quantile_group normalize_quantile_smooth normalize_ref normalize_sum plot_pca plot_volcano read_featuretable read_featuretable_mzmine read_mgf remove_empty_cols scale_auto scale_center scale_level scale_pareto scale_range scale_vast scale_vast_grouped summary_featuretable transform_log transform_power

Dependencies:backports Biobase BiocGenerics bit bit64 broom cli clipr codetools cpp11 crayon digest doRNG dplyr farver foreach generics ggplot2 glue gtable hms impute isoband iterators itertools labeling lattice lifecycle magrittr MASS Matrix missForest pcaMethods pillar pkgconfig prettyunits progress purrr R6 randomForest ranger rbibutils RColorBrewer Rcpp RcppEigen Rdpack readr rlang rngtools S7 scales stringi stringr tibble tidyr tidyselect tzdb utf8 vctrs viridisLite vroom withr

Conjugate Screening

Rendered fromconjugate-screening.Rmdusingknitr::rmarkdownon Jun 10 2026.

Last update: 2026-03-04
Started: 2025-09-01

metamorphr

Rendered frommetamorphr.Rmdusingknitr::rmarkdownon Jun 10 2026.

Last update: 2025-09-01
Started: 2025-09-01

Citation

Readme and manuals

Help Manual

Help page	Topics
A tibble containing the NIST standard atomic weights	atoms
Calculate the Kendrick mass	calc_km
Calculate the Kendrick mass defect (KMD)	calc_kmd
Calculate neutral losses from precursor ion mass and fragment ion masses	calc_neutral_loss
Calculate the nominal Kendrick mass	calc_nominal_km
Collapse intensities of technical replicates by calculating their maximum	collapse_max
Collapse intensities of technical replicates by calculating their mean	collapse_mean
Collapse intensities of technical replicates by calculating their median	collapse_median
Collapse intensities of technical replicates by calculating their minimum	collapse_min
Convert a wide matrix to a tidy tibble	convert_from_matrix
Convert a wide feature table to a tidy tibble	convert_from_wide
Create a blank metadata skeleton	create_metadata_skeleton
Filter Features based on their occurrence in blank samples	filter_blank
Filter Features based on their coefficient of variation	filter_cv
Filter Features based on the absolute number or fraction of samples it was found in	filter_global_mv
Group-based feature filtering	filter_grouped_mv
Filter Features based on occurrence of fragment ions	filter_msn
Filter Features based on their mass-to-charge ratios	filter_mz
Filter Features based on occurrence of neutral losses	filter_neutral_loss
Calculate the monoisotopic mass from a given formula	formula_to_mass
Impute missing values using Bayesian PCA	impute_bpca
Impute missing values by replacing them with the lowest observed intensity (global)	impute_global_lowest
Impute missing values using nearest neighbor averaging	impute_knn
Impute missing values using Local Least Squares (LLS)	impute_lls
Impute missing values by replacing them with the Feature 'Limit of Detection'	impute_lod
Impute missing values by replacing them with the Feature mean	impute_mean
Impute missing values by replacing them with the Feature median	impute_median
Impute missing values by replacing them with the Feature minimum	impute_min
Impute missing values using NIPALS PCA	impute_nipals
Impute missing values using Probabilistic PCA	impute_ppca
Impute missing values using random forest	impute_rf
Impute missing values using Singular Value Decomposition (SVD)	impute_svd
Impute missing values by replacing them with a user-provided value	impute_user_value
Join a featuretable and sample metadata	join_metadata
Calculate neutral losses from precursor ion mass and fragment ion masses	msn_calc_nl
Scale intensities in MSn spectra to the highest value within each spectrum	msn_scale
Normalize intensities across samples using cyclic LOESS normalization	normalize_cyclic_loess
Normalize intensities across samples using a normalization factor	normalize_factor
Normalize intensities across samples by dividing by the sample median	normalize_median
Normalize intensities across samples using a Probabilistic Quotient Normalization (PQN)	normalize_pqn
Normalize intensities across samples using standard Quantile Normalization	normalize_quantile_all
Normalize intensities across samples using grouped Quantile Normalization with multiple batches	normalize_quantile_batch
Normalize intensities across samples using grouped Quantile Normalization	normalize_quantile_group
Normalize intensities across samples using smooth Quantile Normalization (qsmooth)	normalize_quantile_smooth
Normalize intensities across samples using a reference feature	normalize_ref
Normalize intensities across samples by dividing by the sample sum	normalize_sum
Draws a scores or loadings plot or performs calculations necessary to draw them manually	plot_pca
Draws a Volcano Plot or performs calculations necessary to draw one manually	plot_volcano
Read a feature table into a tidy tibble	read_featuretable
Read a 'full_feature_table' from 'mzmine' into a tidy tibble	read_featuretable_mzmine
Read a MGF file into a tidy tibble	read_mgf
Remove empty columns from a tibble or data frame	remove_empty_cols
Scale intensities of features using autoscale	scale_auto
Center intensities of features around zero	scale_center
Scale intensities of features using level scaling	scale_level
Scale intensities of features using Pareto scaling	scale_pareto
Scale intensities of features using range scaling	scale_range
Scale intensities of features using vast scaling	scale_vast
Scale intensities of features using grouped vast scaling	scale_vast_grouped
General information about a feature table and sample-wise summary	summary_featuretable
A small toy data set created from a feature table in MetaboScape style	toy_metaboscape
Sample metadata for the fictional dataset 'toy_metaboscape'	toy_metaboscape_metadata
A small toy data set containing MSn spectra	toy_mgf
Transforms the intensities by calculating their log	transform_log
Transforms the intensities by calculating their _n_th root	transform_power