DeepPhylo: A phylogeny-aware machine learning approach to enhance predictive accuracy in human microbiome data analysis

DeepPhylo, a method that employs phylogeny-aware amplicon embeddings to integrate abundance and phylogenetic information, thereby improving both the unsupervised discriminatory power and supervised predictive accuracy of microbiome data analysis.

Compared to the existing methods, DeepPhylo demonstrated superiority in informing biologically relevant insights across four real-world microbiome use cases, including clustering of skin microbiomes, prediction of host chronological age and gender, and inflammatory bowel disease (IBD) diagnosis across 15 datasets.

This repository contains script which were used to train the DeepPhylo model with the scripts for evaluating the model's performance.

Dependencies

The code was developed and tested using python 3.9.
To install python dependencies run: pip install -r requirements.txt

Installation

You can either clone the public repository:

# clone project
git clone https://github.com/CNwangbin/DeepPhylo
# First, install dependencies
pip install -r requirements.txt

Once you have a copy of the source, you can install it with:

python setup.py install

Running

Download all the data files and place them into data folder

Scripts

The scripts here are using to run the model.

to train and evaluate a model using DeepPhylo in gender prediction run sh:

python deepphylo_classification.py --epochs 500 -hs 80 -kec 3 -l 0.0001 -bs 32 -kep 7 -act relu

to test a model using DeepPhylo in gender prediction run sh:

python deepphylo_classification_inference.py -test_X 'data/gender_classification/X_test.npy' -test_Y 'data/gender_classification/Y_test.npy'  -hs 80 -kec 3 -l 0.0001 -bs 32 -kep 7 -act relu

to train and evaluate a model using DeepPhylo in age prediction run sh:

python deepphylo_regression.py --epochs 500 -hs 40 -kec 7 -l 0.0002 -bs 8 -kep 7 -act tanh

to test a model using DeepPhylo in age prediction run sh:

python deepphylo_regression_inference.py -test_X '/home/syl/DeepPhylo/data/age_regression/X_test.npy' -test_Y 'data/age_regression/Y_test.npy' -hs 40 -kec 7 -l 0.0002 -bs 8 -kep 7 -act tanh

to train and evaluate a model using DeepPhylo in IBD microbiome-based diagnosis run sh:

python deepphylo_ibd_diagnosis.py --epochs 200 -hs 16 -kec 7 -l 1e-4 -bs 32 -kep 2

to test a model using DeepPhylo in IBD microbiome-based diagnosis run sh:

python deepphylo_ibd_diagnosis_inference.py  -hs 16 -kec 7 -l 1e-4 -bs 64 -kep 2 -d 0.2 -p 0.0 -act 'sigmoid'

mystem_rpca.ipynb - Jupyter notebook to run unsupervised method on skin microbiome samples.

Citation

If you use DeepPhylo for your research, or incorporate our learning algorithms in your work, please cite:

New version specifications

Current dependencies can be found in the requirements.txt file. The used Python version is 3.9.12.

CNwangbin / DeepPhylo