qmtools-package: qmtools: Quantitative Metabolomics Data Processing Tools

In HimesGroup/qmtools: Quantitative Metabolomics Data Processing Tools

qmtools: Quantitative Metabolomics Data Processing Tools

Description

The qmtools (quantitative metabolomics tools) package provides basic tools for processing quantitative metabolomics data with the standard SummarizedExperiment class. This includes functions for imputation, normalization, feature filtering, feature clustering, dimension-reduction, and visualization to help users prepare data for statistical analysis. This package also offers a convenient way to compute empirical Bayes statistics for which metabolic features are different between two sets of study samples. Several functions in this package could also be used in other types of omics data.

Details

The qmtools package provides six categories of important functions:

• Imputation: the imputeIntensity function performs data imputation on missing values.

• Normalization: the normalizeIntensity function performs data-driven normalization on metabolomics data.

• Feature filtering: the removeFeatures function removes uninformative features based on missing values, QC and blank samples.

• Feature clustering: the clusterFeatures function clusters metabolic features according to their retention time and intensity correlation.

• Dimension-reduction: the reduceFeatures function performs dimensionality reduction.

• Visualization: the plotBox, plotCorr, plotMiss, plotReduced, and plotRTgroup functions help to visualize metabolomics data.

Please refer to the vignette to see how the aforementioned functions work.

Author(s)

Maintainer: Jaehyun Joo [e-mail]

Authors:

• Blanca Himes [e-mail]

See Also

Useful links:

• https://github.com/HimesGroup/qmtools

• Report bugs at https://github.com/HimesGroup/qmtools/issues

HimesGroup/qmtools documentation built on April 16, 2023, 8 p.m.