Skip to contents

Metaboprep Object

The main S7 class behind the metaboprep R package along with class helper functions. A container for measurement, sample, and feature data.

Metaboprep()
Metaboprep Object
add_layer()
Add a Layer of Data (internal use)

Importing & Exporting Data

Functions importing data from a variety of data sources and formats.

read_metabolon_v1()
Read Metabolon Data (format 1)
read_nightingale_v1()
Read Nightingale Data (format 1)
read_olink_v1()
Read and Process Olink NPX Data File
read_somalogic()
Read and Process SomaLogic adat file
available_data_formats()
List Available Data Formats
export()
Export Data from a Metaboprep Object
export_comets()
Export Data to `COMETS` format
export_metaboanalyst()
Export Data to `MetaboAnalyst` format
export_metaboprep()
Export Data to `Metaboprep` format

Summary functions

Functions to summarise data.

summarise()
Summary Statistics
feature_summary()
Feature Summary Statistics
sample_summary()
Sample Summary Statistics
pc_and_outliers()
Principal Component Analysis
tree_and_independent_features()
Identify Independent Features in a Numeric Matrix

Quality control & Reporting

Functions to run the quality control pipeline and generate a report.

quality_control()
Metabolite Quality Control
available_report_templates()
List Available Report Templates
generate_report()
Generate Output Report

Other processing tools

Stand alone data processing or filtering tools.

batch_normalise()
Batch Normalisation

Helper functions

Helper functions, mainly used internally.

feature_describe()
Summary Statistics for Features
missingness()
Estimate Missingness
outlier_detection()
Identify indexes of outliers in data
outliers()
Identify Outliers
total_peak_area()
Estimates total peak abundance
continuous_power_plot()
continuous trait power analysis plot
multivariate_anova()
multivariate analysis
cramerV()
Cramer's V (phi)
eval.power.binary.imbalanced()
Estimate power for a binary variable in an imbalanced design
eval.power.cont()
estimate power for continuous variable
find.PA.effect.sizes.2.sim()
identify effect sizes
find.cont.effect.sizes.2.sim()
identify continuos trait effect sizes
imbalanced_power_plot()
binary trait imbalanced design power analysis plot
variable_by_factor()
ggplot2 violin plot
clean_names()
Standardize Column or Feature Names