MWAS-Biomarkers

This repo contains the code to reproduce all of the analyses in "Robust biomarker discovery for micorbiome-wide association stuties", Qiang Zhu et al. 2019 (https://doi.org/10.1016/j.ymeth.2019.06.012).

This work is based on Deep Forest: (https://arxiv.org/abs/1702.08835)

The data is available on MetAML: (http://dx.plos.org/10.1371/journal.pcbi.1004977)

Reproducing analyses

If you want to get the feature selection result, you can run

feature_selection.py

then there will be a file under the output directory.

If you want to reproduce the evaluation, please run

plot_auc_curve.py

If you want to calculate the Kuncheva index (https://dl.acm.org/citation.cfm?id=1295370), please run

calculate_kuncheva_index.py

Installing

To re-make all of the analyses, you'll first need to install the required modules.

You should probably do this in a Python 3 virtual environment.

conda create -n MWAS-Biomarkers python=3.6
source activate MWAS-Biomarkers
conda install pip
pip install -r requirements.txt

data

All data-related files are (or will be) in lib/gcforest/data/:

source code

All of the code is in the lib/ folder:

• gcforest: the implementation of Deep Forest
• output: output for feature selection etc
• util: various functions and modules used in other scripts