lipidr-package: Analysis workflow for targeted lipidomics
In ahmohamed/lipidr: Data Mining and Analysis of Lipidomics Datasets
lipidr-package R Documentation
Analysis workflow for targeted lipidomics
Description
lipidr implements a series of functions to facilitate inspection,
analysis and visualization of targeted lipidomics datasets. lipidr
takes exported Skyline CSV as input, allowing for multiple methods to
be analyzed together.
Details
lipidr represents Skyline files as SummarizedExperiment objects, which can
easily be integrated with a wide variety of Bioconductor packages.
Sample annotations,
such as sample group or other clinical information can be loaded. lipidr
generates various plots, such as PCA score plots and box plots, for quality
control of samples and measured lipids. Normalization methods with and
without internal standards are also supported.
Differential analysis can be performed using any of the loaded clinical
variables, which can be readily visualized as volcano plots. A novel lipid
set enrichment analysis (LSEA) is implemented to detect preferential
enrichment of certain lipid classes, total chain lengths or unsaturation
patterns. Plots for the visualization of enrichment results are also
implemented.
Author(s)
Ahmed Mohamed ahmed.mohamed@qimrberghofer.edu.au
ahmohamed/lipidr documentation built on July 7, 2023, 2:22 a.m.
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| lipidr-package | R Documentation |
Analysis workflow for targeted lipidomics
Description
lipidr implements a series of functions to facilitate inspection,
analysis and visualization of targeted lipidomics datasets. lipidr
takes exported Skyline CSV as input, allowing for multiple methods to
be analyzed together.
Details
lipidr represents Skyline files as SummarizedExperiment objects, which can
easily be integrated with a wide variety of Bioconductor packages.
Sample annotations,
such as sample group or other clinical information can be loaded. lipidr
generates various plots, such as PCA score plots and box plots, for quality
control of samples and measured lipids. Normalization methods with and
without internal standards are also supported.
Differential analysis can be performed using any of the loaded clinical variables, which can be readily visualized as volcano plots. A novel lipid set enrichment analysis (LSEA) is implemented to detect preferential enrichment of certain lipid classes, total chain lengths or unsaturation patterns. Plots for the visualization of enrichment results are also implemented.
Author(s)
Ahmed Mohamed ahmed.mohamed@qimrberghofer.edu.au
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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