https://github.com/odisce/dianmf
Processing of SWATH-DIA metabolomics data
https://github.com/odisce/dianmf
dia ms nmf processing swath
Last synced: 9 months ago
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Processing of SWATH-DIA metabolomics data
- Host: GitHub
- URL: https://github.com/odisce/dianmf
- Owner: odisce
- License: other
- Created: 2025-07-21T15:06:35.000Z (11 months ago)
- Default Branch: main
- Last Pushed: 2025-09-01T08:20:21.000Z (9 months ago)
- Last Synced: 2025-09-01T10:44:34.536Z (9 months ago)
- Topics: dia, ms, nmf, processing, swath
- Language: HTML
- Homepage:
- Size: 34.4 MB
- Stars: 1
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# DIANMF: Combined MS and MS/MS deconvolution of SWATH DIA data with the DIA-NMF workflow for comprehensive annotation in metabolomics
## Description
**DIANMF** is an open-source R package for deconvolving complex SWATH-DIA metabolomics data using sparse non-negative matrix factorization (NMF). It is the first method to jointly unmix MS1 and MS2 spectra in a fully untargeted manner, without relying predefined peak models or on spectral libraries. **DIA-NMF** enables precursor-level interpretation by recovering pure MS1 spectra and enriched, unmixed MS2 fragmentation patterns from all relavent isolation windows. This improves compound identification, especially for co-eluting and low-intensity metabolites.
The workflow detects MS1 peaks, extracts mixed MS1 and MS2 signals within minimally overlapping retention time windows, aligns them, and jointly unimixes these to recover pure precursor and fragment patterns (MS1 and MS2 spectra, respectively).
## Installation
The package can be installed from GitHub with:
``` r
#install.packages("devtools")
devtools::install_github("odisce/DIANMF")
```
## Tutorial
The processing of SWATH-DIA metabolomics data with the DIANMF package is described in the included vignette ([vignettes/Process_SWATH_DIA_Data.html](https://htmlpreview.github.io/?https://github.com/odisce/DIANMF/blob/master/vignettes/Process_SWATH_DIA_Data.html)).
## Dataset
This package includes a sample dataset used in the accompanying examples and vignettes. It consists of selected regions from replicates 1 and 2 of a 10 ng/mL spiked human plasma sample, focusing on the 280–320 m/z range and 280–320 seconds retention time window (Barbier Saint-Hilaire *et al.* 2020).
## Citation
Diana Karaki, Annelaure Damon, Antoine Souloumiac, François Fenaille, Etienne A. Thévenot and Sylvain Dechaumet (in preparation). Combined MS and MS/MS deconvolution of SWATH DIA data with the DIA-NMF workflow for comprehensive annotation in metabolomics.
## Contacts
[diana.karaki\@cea.fr](mailto:diana.karaki@cea.fr), [sylvain.dechaumet\@cea.fr](mailto:sylvain.dechaumet@cea.fr), and [etienne.thevenot\@cea.fr](mailto:etienne.thevenot@cea.fr)
## Licence
[CeCILL V2.1](https://cecill.info/licences/Licence_CeCILL_V2.1-en.html)
## References
Karaki *et al.* (2024) Non-Negative Matrix Factorization of SWATH DIA Data Improves Global Metabolite Identification. *European Signal Processing Conference (EUSIPCO)*, 2387-2391. [DOI:10.23919/EUSIPCO63174.2024.10715181](https://doi.org/10.23919/EUSIPCO63174.2024.10715181).
Wang *et al.* (2019) Advancing untargeted metabolomics using data-independent acquisition mass spectrometry technology. *Analytical and bioanalytical chemistry*, 4349-4357. [DOI:10.1007/s00216-019-01709-1](https://doi.org/10.1007/s00216-019-01709-1).
Barbier Saint-Hilaire *et al.* (2020) Comparative Evaluation of Data Dependent and Data Independent Acquisition Workflows Implemented on an Orbitrap Fusion for Untargeted Metabolomics. Metabolites, 2218-1989. [DOI:10.3390/metabo10040158](https://doi.org/10.3390/metabo10040158).