The MS-CleanR package provides functions for feature filtering and annotation of LC-MS data.
See the publication and tutorials (pdf files included in the master branch) for more information.
Needs MS-DIAL (v4.00 or higher) and MS-FINDER (3.30 or higher): http://prime.psc.riken.jp/compms/index.html
MS-CleanR use as input MS-DIAL peak list processed in data dependent analysis (DDA) or data independent analysis (DIA) using either positive ionization mode (PI) or negative ionization mode (NI) or both. First, MS-CleanR apply generic filters encompassing blank injection signal subtraction, background ions drift removal, unusual mass defect filtering, relative standard deviation threshold (RSD) based on sample class and relative mass defect (RMD) window filtering. All these options are tunable by the user. The second step involves a feature clustering method based on MS-DIAL peak character estimation algorithm followed by parental signal extraction using multi-level optimization of modularity algorithm. Optionally, MS-CleanR can merge PI and NI mode during this step. Then, all selected features are exported to MS-FINDER program for in silico-based annotation using hydrogen rearrangement rules (HRR) scoring system. At this step, multiple databases can be queried and each annotation results will be handled by MS-CleanR. The final step will merge annotation results to the filtered peak list by prioritizing database annotation depending on user choice. Optionally, all results can be exported as .msp file for mass spectral similarity networking purpose.
devtools::install_github("eMetaboHUB/MS-CleanR")
library(mscleanr)
runGUI()
First, read carefully the MS-DIAL/CleanR/FINDER tutorial
Thanks to all users for their feedback!!
- At least 3 blanks and 3 QCs samples are needed for Blank ratio analysis. These samples must be identified as such in the MS-Dial sample list.
- Avoid spaces in samples or classes names and replace it by "-"; "." or "_"
- Avoid class names with only one letter
- MSCleanR handle LCMS acquired in DIA or DDA mode. All features without MS/MS will be discarded during the first step. If data contain MS1 only, the first MS-CleanR step will crash.
Publication link: https://pubs.acs.org/doi/abs/10.1021/acs.analchem.0c01594 MS-CleanR: A Feature-Filtering Workflow for Untargeted LC–MS Based Metabolomics Ophélie Fraisier-Vannier, Justine Chervin, Guillaume Cabanac, Virginie Puech, Sylvie Fournier, Virginie Durand, Aurélien Amiel, Olivier André, Omar Abdelaziz Benamar, Bernard Dumas, Hiroshi Tsugawa, and Guillaume Marti Analytical Chemistry Article ASAP DOI: 10.1021/acs.analchem.0c01594
GPL-3