https://github.com/gauravpandeylab/metaclean
https://github.com/gauravpandeylab/metaclean
Last synced: over 1 year ago
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- Host: GitHub
- URL: https://github.com/gauravpandeylab/metaclean
- Owner: GauravPandeyLab
- Created: 2020-04-08T21:24:43.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2020-12-08T02:21:23.000Z (over 5 years ago)
- Last Synced: 2025-01-20T17:35:14.309Z (over 1 year ago)
- Language: R
- Size: 127 KB
- Stars: 3
- Watchers: 2
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# **MetaClean**: Detection of Low-Quality Peaks in Untargeted LC-MS Metabolomics Data
MetaClean utilizes 11 peak-quality metrics and 8 diverse machine learning algorithms to build a classifier to detect and flag low-quality peaks in untargeted metabolomics data. It is an R package and can be easily incorporated into existing untargeted LC-MS metabolomics pipelines that utilize the pre-processing software XCMS.
The 11 peak-quality metrics used by MetaClean are adapted from the following publications:
Zhang, W., & Zhao, P. X. (2014). Quality evaluation of extracted ion chromatograms and chromatographic peaks in liquid chromatography/mass spectrometry-based metabolomics data. BMC Bioinformatics, 15(Suppl 11), S5. https://doi.org/10.1186/1471-2105-15-S11-S5
Eshghi, S. T., Auger, P., & Mathews, W. R. (2018). Quality assessment and interference detection in targeted mass spectrometry data using machine learning. Clinical Proteomics, 15. https://doi.org/10.1186/s12014-018-9209-x
## Installation
To install MetaClean from this GitHub page, run the following command:
```
remotes::install_github("KelseyChetnik/MetaClean")
```
Or install the latest stable CRAN version:
https://cran.r-project.org/package=MetaClean
## Usage
Check out this [vignette](https://github.com/KelseyChetnik/MetaClean/blob/master/vignettes/MetaClean_WalkThrough.Rmd) for step-by-step instructions on how to use the MetaClean package.
## Citation
Chetnik, K., Petrick, L. & Pandey, G. MetaClean: a machine learning-based classifier for reduced false positive peak detection in untargeted LC–MS metabolomics data. Metabolomics 16, 117 (2020). https://doi.org/10.1007/s11306-020-01738-3