MiMeNet predicts the metabolomic profile from microbiome data and learns undelrying relationships between the two.
- MiMeNet is tested to work on Python 3.7+
- MiMeNet requires the following Python libraries:
- Tensorflow 1.14
- Numpy 1.17.2
- Pandas 0.25.1
- Scipy 1.3.1
- Scikit-learn 0.21.3
- Matplotlib 3.0.3
- Seaborn 0.9.0
A Conda Python environment is provided in pseudocell_tracer.yml
usage: MiMeNet_train.py [-h] -micro MICRO -metab METAB
[-external_micro EXTERNAL_MICRO]
[-external_metab EXTERNAL_METAB]
[-annotation ANNOTATION] [-labels LABELS] -output
OUTPUT [-net_params NET_PARAMS]
[-background BACKGROUND]
[-num_background NUM_BACKGROUND]
[-micro_norm MICRO_NORM] [-metab_norm METAB_NORM]
[-threshold THRESHOLD] [-num_run_cv NUM_RUN_CV]
[-num_cv NUM_CV] [-num_run NUM_RUN]
-h, --help Show this help message and exit
-micro MICRO Comma delimited file representing matrix of samples by microbial features
-metab METAB Comma delimited file representing matrix of samples by metabolomic features
-external_micro EXTERNAL_MICRO Comma delimited file representing matrix of samples by microbial features
-external_metab EXTERNAL_METAB Comma delimited file representing matrix of samples by metabolomic features
-annotation ANNOTATION Comma delimited file annotating subset of metabolite features
-labels LABELS Comma delimited file for sample labels to associate clusters with
-output OUTPUT Output directory
-net_params NET_PARAMS JSON file of network hyperparameters
-background BACKGROUND Directory with previously generated background
-num_background NUM_BACKGROUND Number of background CV Iterations
-micro_norm MICRO_NORM Microbiome normalization (RA, CLR, or None)
-metab_norm METAB_NORM Metabolome normalization (RA, CLR, or None)
-threshold THRESHOLD Define significant correlation threshold
-num_run_cv NUM_RUN_CV Number of iterations for cross-validation
-num_cv NUM_CV Number of cross-validated folds
-num_run NUM_RUN Number of iterations for training full model
python MiMeNet_train.py -micro data/IBD/microbiome_PRISM.csv -metab data/IBD/metabolome_PRISM.csv \
-external_micro data/IBD/microbiome_external.csv -external_metab data/IBD/metabolome_external.csv \
-micro_norm None -metab_norm CLR -net_params results/IBD/network_parameters.txt \
-annotation data/IBD/metabolome_annotation.csv -labels data/IBD/diagnosis_PRISM.csv \
-num_run_cv 10 -output IBD
The provided command will run MiMeNet on the IBD dataset and store results in the directory results/output_dir.
1.0.0 (2020/07/28)
TBA
- Please contact Derek Reiman dreima2@uic.edu or Yand Dai yangdai@uic.edu for any questions or comments.
Software provided to academic users under MIT License