https://github.com/borgwardtlab/longcovid

Prediction of long COVID from proteomic and clinical data
https://github.com/borgwardtlab/longcovid

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Prediction of long COVID from proteomic and clinical data

• Host: GitHub
• URL: https://github.com/borgwardtlab/longcovid
• Owner: BorgwardtLab
• License: mit
• Created: 2022-12-09T10:49:30.000Z (over 3 years ago)
• Default Branch: master
• Last Pushed: 2024-02-08T08:48:08.000Z (over 2 years ago)
• Last Synced: 2025-01-22T04:14:00.778Z (over 1 year ago)
• Language: Python
• Size: 38.1 KB
• Stars: 1
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# LongCOVID

A repository to share code for long COVID predictions based on a random forest classifier as well as a univariate association analysis of proteomic features to longCOVID labels.

# Requirements

python 3.7.4 

scikit-learn 1.1.3

pandas 1.5.2

scipy 1.9.3

numpy 1.23.5

shap 0.41.0

statsmodels 0.13.5

openpyxl 3.0.10

# Required input data

The input data required to execute these scripts can be obtained from ![image](https://user-images.githubusercontent.com/54959592/210764179-206b07c3-1845-44e4-9b4f-dcac18ed5de8.png). Please include these in a folder ***Data***. This should comprise: 

- Proteomics_Clinical_Data_220902_Acute_plus_healthy_v5.xlsx

- Proteomics_Clinical_Data_220902_6M_timepoint_v4.xlsx

- Proteomics_Clinical_Data_220902_Labels_v2.xlsx

- Table S2 Biological protein cluster compositions.xlsx

# Execution

We provide the data splits used in ***partitions***. Relevant label dictionaries need to be generated based on the label data file listed above.

Run the file ***prediction_RF.py*** to generate model predictions, 

association analysis either for individual proteomic features, or clusters thereof can be obtained using ***associationAnalysis.py***, and ***associationClusters.py*** respectively. 

In ***combineInterpreations.py*** we combine the SHAP analysis results of multiple cross validation folds. 

# Acknowledgements

This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 813533 (K.B.).