peakPantheR

Package for Peak Picking and ANnoTation of High resolution Experiments in R, implemented in R and Shiny

Overview

peakPantheR is an R/Bioconductor package that implements functions to detect, integrate and report pre-defined features in MS files (e.g. compounds, fragments, adducts, …). It is designed for:

• Real time feature detection and integration (see Real Time Annotation)
  • process multiple compounds in one file at a time
• Post-acquisition feature detection, integration and reporting (see Parallel Annotation)
  • process multiple compounds in multiple files in parallel, store results in a single object

peakPantheR can process LC/MS data files in NetCDF, mzML/mzXML and mzData format as data import is achieved using Bioconductor’s mzR package.

The reference versions of peakPantheR is available on the corresponding Bioconductor page (release or dev version).

Active development and issue tracking take place on the github page, while an overview of the package, vignettes and documentation are available on the supporting website.

Installation

To install peakPantheR:

install.packages("BiocManager")
BiocManager::install("peakPantheR")

To install the development version from GitHub:

BiocManager::install("phenomecentre/peakPantheR")

Usage

Both real time and parallel compound integration require a common set of information:

• Path(s) to netCDF / mzML MS file(s)
• An expected region of interest (RT / m/z window) for each compound.

Vignettes

An overview of the package and detailed information on usage are available in the following vignettes:

• Getting Started with peakPantheR
• Real Time Annotation
• Parallel Annotation
• Graphical user interface use

Examples

Besides the vignettes, more tutorials are available via github:

• Targeted integration of metabolites from 3 LC-MS profiling dataset using peakPantheR: Application of peakpPantheR to extract features from 3 LC-MS assays from a human urine metabolic profiling study on cognitive decline and dementia.
  
• Quality-Control of peakPantheR extracted using the nPYc-Toolbox: Tutorial on how to use the nPYC-Toolbox to perform QC on peakPanther extracted datasets.

Contributing

Suggestions and contributions to peakPantheR are welcome, for more information please first refer to the contribution guide and code of conduct, or get in touch by opening a Github issue.

Copyright

peakPantheR is licensed under the GPLv3

As a summary, the GPLv3 license requires attribution, inclusion of copyright and license information, disclosure of source code and changes. Derivative work must be available under the same terms.

© National Phenome Centre (2023)