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+# 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
+