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MB-GAN

Microbiome Simulation via Generative Adversarial Network

Introduction

MB-GAN is a deep learning simulation framework for simulating realistic microbiome data

Manuscript is avaialble at bioRxiv https://doi.org/10.1101/863977

Information provided by this repository

Data folder

The folder “data” contains:

  • The real microbiome data used to generate MB-GAN samples analyzed in the manuscript

  • The csv files of the MB-GAN samples

  • The simulated data by alternative methods: Normal-To-Anything and metaSPARSim

  • The processed data used to generate figures and tables in the manuscript

R code

The folder “Rcode” includes the following R scripts:

  • generate_NorTA.R: Simulate data by Normal-To-Anything (NorTA)

  • generate_metaSPARSim.R: Simulate data by metaSPARSim

  • process_MBGAN.R: Converts the csv files of MB-GAN samples to Rdata files

  • calculate_unifrac_dist.R: Calculate UniFrac distance for real microbiome samples and samples generated by three simulation methods (MBGAN, NorTA, metaSPARSim)

  • check_first_level_property.R: Compare sample sparsity, abundance, alpha diversity (by Shannon index), and beta diversity (by non-metric multidimensional scaling (nMDS) analysis)

  • check_second_level_property.R: Compare taxa-taxa associations using correlation structure and proportionality

  • mbgan_application_supp15.R: Compare differential abundance analysis results based on the real microbiome and MBGAN simulated samples

  • MiRKAT_simulation.R and MiRKAT_result_summary.R: Implement MiRKAT using MBGAN samples

  • generate_abundance_heatmap.R: Compare the abundances of the top 60 most abundant taxa across different datasets

The folder “Rcode_smalldataset” includes the similar R code as above for the small microbiome dataset dicussed in the supplement

Python code

The root folder includes the following Python scripts:

  • mbgan_train_demo.py: codes to train a MB-GAN network
  • mbgan_inference_casectrl.py: codes to simulate new microbiome abundances using trained model parameters

The folder "models_smaller_dataset" includes the following Python scripts:

  • inference_case_ctrl.py: codes to simulate new microbiome abundances for the smaller microbiome dataset using trained model parameters

Contact

Xiaowei Zhan xiaowei.zhan@utsouthwestern.edu, Quantitative Biomedical Research Center, UT Southwestern Medical Center, Dallas, TX 75390-8821