# peakPantheR <img src="man/figures/peakPantheR-logo.png" align="right" /> [](https://github.com/phenomecentre/peakPantheR/actions) [](https://bioconductor.org/checkResults/release/bioc-LATEST/peakPantheR) [](https://github.com/phenomecentre/peakPantheR/actions) [](https://bioconductor.org/checkResults/devel/bioc-LATEST/peakPantheR) [](https://codecov.io/gh/phenomecentre/peakPantheR/branch/master) [](https://zenodo.org/badge/latestdoi/116680214) Package for *Peak Picking and ANnoTation of High resolution Experiments in R*, implemented in `R` and `Shiny` ## Overview [**peakPantheR**](https://phenomecentre.github.io/peakPantheR.github.io/) 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](https://phenomecentre.github.io/peakPantheR.github.io/articles/real-time-annotation.html)) - process `multiple` compounds in `one` file at a time - **Post-acquisition** feature detection, integration and reporting (see [Parallel Annotation](https://phenomecentre.github.io/peakPantheR.github.io/articles/parallel-annotation.html)) - 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`](https://bioconductor.org/packages/release/bioc/html/mzR.html) package. The reference versions of `peakPantheR` is available on the corresponding Bioconductor page ([release](https://bioconductor.org/packages/release/bioc/html/peakPantheR.html) or [dev](https://bioconductor.org/packages/devel/bioc/html/peakPantheR.html) version). Active development and issue tracking take place on the [github page](https://github.com/phenomecentre/peakPantheR), while an overview of the package, vignettes and documentation are available on the [supporting website](https://phenomecentre.github.io/peakPantheR.github.io/). ## Installation To install [peakPantheR](https://bioconductor.org/packages/release/bioc/html/peakPantheR.html): ``` 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](https://phenomecentre.github.io/peakPantheR.github.io/articles/getting-started.html) - [Real Time Annotation](https://phenomecentre.github.io/peakPantheR.github.io/articles/real-time-annotation.html) - [Parallel Annotation](https://phenomecentre.github.io/peakPantheR.github.io/articles/parallel-annotation.html) - [Graphical user interface use](https://phenomecentre.github.io/peakPantheR.github.io/articles/peakPantheR-GUI.html) ## Examples Besides the vignettes, more tutorials are available via github: - [Targeted integration of metabolites from 3 LC-MS profiling dataset using peakPantheR](https://github.com/phenomecentre/metabotyping-dementia-urine): 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](https://github.com/phenomecentre/nPYc-toolbox-tutorials): 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](./CONTRIBUTIONS.md), or get in touch by opening a [Github issue](https://github.com/phenomecentre/peakPantheR/issues/). ## Copyright `peakPantheR` is licensed under the [GPLv3](http://choosealicense.com/licenses/gpl-3.0/) 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)