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metabolomicsR

Tools to process, analyze, and visualize metabolomic data.

Installation

TypeCommand
Developmentremotes::install_github("XikunHan/metabolomicsR")

Getting started

The detailed tutorial.

metabolomicsR is a streamlined R package to preprocess, analyze, and visualize metabolomic data. We included a set of functions for sample and metabolite quality control, outlier detection, missing value imputation, dimensional reduction, normalization, data integration, regression analysis, annotation, enrichment analysis, and visualization of data and results. The metabolomicsR is designed to be a comprehensive R package that can be easily used by researchers with basic R programming skills. The framework designed here is also versatile and is extensible to various methods and metabolomic platforms.

Seamless workflow to preprocess, analyze, and visualize metabolomics data in metabolomicsR

Contact:

Xikun Han maintains this package.

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Version

Install

install.packages('metabolomicsR')

Monthly Downloads

11

Version

1.0.0

License

GPL-2

Issues

Pull Requests

Stars

Forks

Maintainer

Xikun Han

Last Published

April 29th, 2022

Functions in metabolomicsR (1.0.0)

Metabolite-class

The Metabolite class
QCmatrix_norm

QCmatrix normalization
assayData<-

set assayData
QC_pipeline

quality control pipeline
correlation

correlation of features between two Metabolite objects
bridge

bridge different data sets based on conversion factors
batch_norm

batch normalization
impute

impute missing values
filter_column_constant

filter columns if values are constant
filter_column_missing_rate

filter columns using missing rate
featureData

get featureData
is_outlier

is outlier
row_missing_rate

row missing rate
load_data

Load metabolite data from three separate files
replace_outlier

change outlier values as NA or winsorize
filter_row_missing_rate

filter rows using missing rate
fit_lm

available regression methods
merge_data

merge two Metabolite objects
load_excel

Load metabolite data from an excel file
inverse_rank_transform

rank-based inverse normal transformation
plot_injection_order

injection order scatterplot
modelling_norm

LOESS normalization
nearestQC_norm

nearest QC sample normalization
RSD

RSD
create_Metabolite

Create a Metabolite object
sampleData<-

set sampleData
plot_tsne

plot tSNE
featureData<-

set featureData
df_plasma

Example data.
save_data

Save metabolite data
run_PCA

Principal Components Analysis
outlier_rate

outlier rate
plot_volcano

volcano plot for regression results
plot_Metabolite

plot a Metabolite object
plot_PCA

plot PCA
sampleData

get sampleData
regression

regression analysis
pareto_scale

pareto scale transformation
plot_ROC

ROC
show,Metabolite-method

Print a Metabolite class object
plot_UMAP

Plot UMAP
subset

subset a Metabolite object.
update_Metabolite

Update a Metabolite object
transformation

apply transformation to a Metabolite object
assayData

get assayData
column_missing_rate

column missing rate