R: Tidy and Streamlined Metabolomics Data Workflows

# Tidy and Streamlined Metabolomics Data Workflows

## Documentation for package ‘metamorphr’ version 0.4.1

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package NEWS
- Package README

## Help Pages

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
convert_from_matrix | Convert a wide matrix to a tidy tibble
convert_from_wide | Convert a wide feature table to a tidy tibble
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_featuretable_mzmine | Read a 'full_feature_table' from 'mzmine' into a tidy tibble
read_mgf | Read a MGF file into a tidy tibble
remove_empty_cols | Remove empty columns from a tibble or data frame
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 _n_th root