inst/shiny/www/README.md
In CeMOS-Mannheim/moleculaR: Spatial Probabilistic Mapping of Metabolite Ensembles in Mass Spectrometry Imaging
The moleculaR R package provides a computational framework that introduces probabilistic mapping
and point-for-point statistical testing of metabolites in tissue via Mass spectrometry imaging.
It enables collective projections of metabolites and consequently spatially-resolved investigation
of ion milieus, lipid pathways or user-defined biomolecular ensembles within the same image.
For more information about this package and its applications please refer to the associated preprint.
moleculaR Shiny-App
moleculaR provides an R Shiny web app package-app with intuitive web-based GUI. It contains an example section which comes pre-loaded with an examplary reduced MALDI MSI dataset (see citation below) and a main section which lets the user upload her own centroided imzML data and apply spatial probabilistic mapping through Molecular probabilistic Maps (MPMs) and Collective Projection probabilistic Maps (CPPMs).
The first step involves uploading data. To do this click on imzML & ibd File and navigate to the directory containing the files you want to upload. Then select both imzML- and ibd-Files and upload them. Via Spectrum .tsv File a continuous spectrum in tsv format could be uploaded. This spectrum could either be a single random pixel or a mean spectrum of your imaging dataset. Once this is done, click Load and Initialize to read the data and estimate the FWHM model. For a given m/z value of interest, you can generate the corresponding MPM by providing that m/z value in the corresponding input box. To generate a collective projection probabilstic map (CPPM) of a custom list of m/z values, please paste these values into the corresponding text box, comma separated.
Citing moleculaR
Abu Sammour, Denis, et al. "Spatial Probabilistic Mapping of Metabolite Ensembles in Mass Spectrometry Imaging." bioRxiv (2021). https://doi.org/10.1101/2021.10.27.466114
Contact
You are welcome to:
- submit suggestions and bug-reports at: https://github.com/CeMOS-Mannheim/moleculaR/issues
- send a pull request on: https://github.com/CeMOS-Mannheim/moleculaR/
- compose an e-mail to: d.abu-sammour@hs-mannheim.de
CeMOS-Mannheim/moleculaR documentation built on April 14, 2025, 8:27 a.m.
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
The moleculaR R package provides a computational framework that introduces probabilistic mapping and point-for-point statistical testing of metabolites in tissue via Mass spectrometry imaging. It enables collective projections of metabolites and consequently spatially-resolved investigation of ion milieus, lipid pathways or user-defined biomolecular ensembles within the same image.
For more information about this package and its applications please refer to the associated preprint.
moleculaR Shiny-App
moleculaR provides an R Shiny web app package-app with intuitive web-based GUI. It contains an example section which comes pre-loaded with an examplary reduced MALDI MSI dataset (see citation below) and a main section which lets the user upload her own centroided imzML data and apply spatial probabilistic mapping through Molecular probabilistic Maps (MPMs) and Collective Projection probabilistic Maps (CPPMs).
The first step involves uploading data. To do this click on imzML & ibd File and navigate to the directory containing the files you want to upload. Then select both imzML- and ibd-Files and upload them. Via Spectrum .tsv File a continuous spectrum in tsv format could be uploaded. This spectrum could either be a single random pixel or a mean spectrum of your imaging dataset. Once this is done, click Load and Initialize to read the data and estimate the FWHM model. For a given m/z value of interest, you can generate the corresponding MPM by providing that m/z value in the corresponding input box. To generate a collective projection probabilstic map (CPPM) of a custom list of m/z values, please paste these values into the corresponding text box, comma separated.
Citing moleculaR
Abu Sammour, Denis, et al. "Spatial Probabilistic Mapping of Metabolite Ensembles in Mass Spectrometry Imaging." bioRxiv (2021). https://doi.org/10.1101/2021.10.27.466114
Contact
You are welcome to:
- submit suggestions and bug-reports at: https://github.com/CeMOS-Mannheim/moleculaR/issues
- send a pull request on: https://github.com/CeMOS-Mannheim/moleculaR/
- compose an e-mail to: d.abu-sammour@hs-mannheim.de
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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