This is a Python script for calculating Gut Aging Index (GAI) based on OTU (Operational Taxonomic Units) data.

The datasets used in paper can be downloaded with download_data.sh BASH script.

• Python 3.x
• pandas
• pycaret 2.3.5

1. Clone the repository:

git clone https://github.com/zwbao/GAI_paper.git

2. Install the required Python packages:

pip install pandas
pip install pycaret==2.3.5

1. Prepare the input files:

   • meta.tsv: The metadata file containing demographic information, with a column named 'id' as the index column.
   • otu.tsv: The OTU file containing operational taxonomic unit data, with a column named 'id' as the index column.

2. Run the script:

python gai_cal.py meta.tsv otu.tsv

3. Output files:
   • compare_models.tsv: A file containing the results of comparing different regression models.
   • tuned_best_model.tsv: A file containing the results of tuning the best model.
   • final_best_model_<date>.pkl: A file containing the finalized best model, where <date> represents the current date in the format YYYYMMDD.
   • adjust_values.tsv: A file containing average raw GAI values for different age ranges.
   • result.tsv: A file containing the final results, including age, raw GAI, adjusted value, and corrected GAI.

To use the pre-trained models from the 'models' folder, follow these steps:

1. Load a model using PyCaret:

   from pycaret.classification import load_model
   model = load_model('path/to/model')

2. Prepare your data as specified in the meta.tsv and otu.tsv files, ensuring it matches the format the model expects.

3. Predict GAI using the loaded model:

   predictions = predict_model(model, data=unseen_data)

This allows for quick application of our research findings and replication of results using established models.

If you have any questions or issues, please feel free to contact me at [email protected].