This package is initiated from the University of Washington eScience capstone project.
Mass-suite is a package used for general MS data process and advanced data analysis.
It provided a full capacity including data import, alignment, noise removal, visualization and fragment searching using online database.
The mass-suite package is also providing a data mining option for the feature clustering using dilution datasets, aiming at dilution prediction, source tracking and matrix effect assessment.
- 2022/03/12: Source identification/pattern recognition algorithm developed
- 2020/09/01: Feature extraction pipeline validated comparing with msdial pipeline output
- 2020/09/07: Updated formula prediction and isotopic score function, currently support formula prediction for small molecules under 500 Da
- 2020/12/15: Rewrite alignment module, fixed bugs in peak picking functions
- Included within the root of the repository is a virtual environment
pre-suited to run
mss- The virtual environment is located within environment.yml
- To create the virtual environment from the .yml file:
conda env create -f environment.yml - To activate the virtual environment:
conda activate mss
Mass-suite requires major dependencies:
- Pandas 1.0.3 or higher
- Numpy 1.16.5 or higher
- Scikit-learn 0.23.0 or higher
- Scipy 1.4.1 or higher
- pymzml 2.4.6 or higher
- plotly 4.5.2 or higher
Optional dependencies for machine-learning models:
- Keras 2.3.1
- Tensorflow 2.0.0
- Pytorch 1.4.0 or higher
Please make sure the major dependencies are up to date before the installation.
The package can be installed & imported as following:
pip install mass-suite
import mss
The project has the following major structure:
mass-suite/
|- mss/
|- __init__.py
|- alignment.py
|- dm.py
|- frag.py
|- mssdata.py
|- mssmain.py
|- version.py
|- visreader.py
|- *.pkl
|- tests/
|- __init__.py
|- test_*.py
|- dev/
|- *.ipynb
|- script/
|- mss_processor.py
|- doc/
|- example_data/
|- README.md
|- LICENSE
|- environment.yml
Supported formats:
- .mzml
MS data will be imported and filtered to create a dataframe in readable format from the mzml file.
A random forest model is integrated in the function as optional assessing tool for peak quality assessment.
Data from different experiments will be aligned together according to user defined error range.
Supported export formats: .csv/.xlsx
There is also a script version of the functions that integrated the functions into a automated workflow, please see script/mss_processor.py
The automated workflow is excutable via command line:
python -m mss_processor.py
example:
Currently the script only support .csv export.
Supported formats: formats supported by Pandas
This is a data analysis module using cluster algorithm to find grouping features, detecting noises and mining for dilution prediction/source tracking.
ms_cluster function will grouping the features into clusters using selected algorithm. Dimension reduction and cluster report are optional within the function settings.
Current clustering algorithms:
example output:
cluster report output:
After clustering, modeling function is provided to predict the dilution rate from samples and catch the behavior of individual clusters
feature_model(d_transpose, cluster_algorithm=True, model_method='ensemble_bagging_svc', report=True)
User can define either to use all features for modeling or individual clusters for modeling
Currently supporting models: linear regression, random forest regression, ensemble bagging regression
Model assessment report is also optional upon function settings:
User can use the model and cluster information for new sample prediction:
Simple source identification tool is also incorporated in this module:
Please refer to function documentation for more details.
All the online searching is using MoNA MassBank database.
All the data used in the project is giving credit to Center of Urban Water.
Application of Nontarget High Resolution Mass Spectrometry Data to Quantitative Source Apportionment
Katherine T. Peter, Christopher Wu, Zhenyu Tian, and Edward P. Kolodziej
Environmental Science & Technology 2019 53 (21), 12257-12268
DOI: 10.1021/acs.est.9b04481
Transformation Product Formation upon Heterogeneous Ozonation of the Tire Rubber Antioxidant 6PPD (N-(1,3-dimethylbutyl)-N′-phenyl-p-phenylenediamine)
Ximin Hu, Haoqi Nina Zhao, Zhenyu Tian, Katherine T. Peter, Michael C. Dodd, and Edward P. Kolodziej
Environmental Science & Technology Letters 2022 9 (5), 413-419
DOI: 10.1021/acs.estlett.2c00187
All the data used for model training is under 'example_data' folder and can be repeatly trained by users according to the settings.
The majority of code is tested under unittest and pushed through GitHub Actions.
The package is open source and can be utilized under MIT license. Please find the detail in licence file.
Mass-suite welcomes help from wide range of different backgrounds. As the package will keep developing, suggestions and improvements are always appreciated. If you are interested in testing the package using your own MS data, giving suggestions for adding modules or improving existing ones, have some question or seeking for cooperation, please contact the main contributor(xhu66@uw.edu).
- A big shout to Professor Edward Kolodziej, Professor Dave Beck, Katherine Peter, and the Center of Urban Water for all of their help during the development of this package.