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# peakPantheR <img src="man/figures/peakPantheR-logo.png" align="right" /> [![R-CMD-check-Bioc](]( [![BioC status](]( [![R-CMD-check-Bioc-devel](]( [![BioC dev status](]( [![codecov](]( [![DOI](]( 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]( ``` r install.packages("BiocManager") BiocManager::install("peakPantheR") ``` To install the development version from GitHub: ``` r 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)