Package index • metaboprep

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(): Read Metabolon Data

read_nightingale(): 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