Tidy and Streamlined Metabolomics Data Workflows
Calculate the Kendrick mass
Calculate the Kendrick mass defect (KMD)
Calculate neutral losses from precursor ion mass and fragment ion mass...
Calculate the nominal Kendrick mass
Collapse intensities of technical replicates by calculating their maxi...
Collapse intensities of technical replicates by calculating their mean
Collapse intensities of technical replicates by calculating their medi...
Collapse intensities of technical replicates by calculating their mini...
Create a blank metadata skeleton
Filter Features based on their occurrence in blank samples
Filter Features based on their coefficient of variation
Filter Features based on the absolute number or fraction of samples it...
Group-based feature filtering
Filter Features based on occurrence of fragment ions
Filter Features based on their mass-to-charge ratios
Filter Features based on occurrence of neutral losses
Calculate the monoisotopic mass from a given formula
Impute missing values using Bayesian PCA
Impute missing values by replacing them with the lowest observed inten...
Impute missing values using nearest neighbor averaging
Impute missing values using Local Least Squares (LLS)
Impute missing values by replacing them with the Feature 'Limit of Det...
Impute missing values by replacing them with the Feature mean
Impute missing values by replacing them with the Feature median
Impute missing values by replacing them with the Feature minimum
Impute missing values using NIPALS PCA
Impute missing values using Probabilistic PCA
Impute missing values using random forest
Impute missing values using Singular Value Decomposition (SVD)
Impute missing values by replacing them with a user-provided value
Join a featuretable and sample metadata
metamorphr: Tidy and Streamlined Metabolomics Data Workflows
Normalize intensities across samples using cyclic LOESS normalization
Normalize intensities across samples using a normalization factor
Normalize intensities across samples by dividing by the sample median
Normalize intensities across samples using a Probabilistic Quotient No...
Normalize intensities across samples using standard Quantile Normaliza...
Normalize intensities across samples using grouped Quantile Normalizat...
Normalize intensities across samples using grouped Quantile Normalizat...
Normalize intensities across samples using smooth Quantile Normalizati...
Normalize intensities across samples using a reference feature
Normalize intensities across samples by dividing by the sample sum
Pipe operator
Draws a scores or loadings plot or performs calculations necessary to ...
Draws a Volcano Plot or performs calculations necessary to draw one ma...
Read a feature table into a tidy tibble
Read a MGF file into a tidy tibble
Scale intensities of features using autoscale
Center intensities of features around zero
Scale intensities of features using level scaling
Scale intensities of features using Pareto scaling
Scale intensities of features using range scaling
Scale intensities of features using grouped vast scaling
Scale intensities of features using vast scaling
General information about a feature table and sample-wise summary
Transforms the intensities by calculating their log
Transforms the intensities by calculating their nth root
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'.