Bayesian Causal Structural Learning with Zero-Inflated Poisson Bayesian Networks

This repository contains the implementation of a zero-inflated Poisson Bayesian network (ZIPBN) proposed by "Bayesian Causal Structural Learning with Zero-Inflated Poisson Bayesian Networks" by Junsouk Choi, Robert Chapkin, and Yang Ni. Specifically, our code in this repository will reproduce the simulation results corresponding to Table 2 in the paper. We hope that the provided code is helpful to give a detailed description of the procedure we did.

Requirements

Our implemenation requires some dependencies. Please run the following codes to the dependencies:

pkgs <- c("igraph", "pscl", "glmnet", "MXM", "foreach", "doParallel", "doRNG", "ggplot2", "ggpubr")
sapply(pkgs, install.packages, character.only = TRUE)

Training

The training subdirectory contains training scripts that are used to perform simulations under different percentages of zeros in the paper (see Table 2).

• ZIPBNfunctions.R includes functions needed to implement the parallel-tempered Markov chain Monte Carlo (MCMC) algorithm for ZIPBN that is described in the paper.
• zipbn_zero25pct.R and zipbn_zero75pct.R implement our parallel-tempered MCMC algorithm and run it on simulations with ~25% and ~75% zeros, respectively.
• ods_zero25pct.R and ods_zero75pct.R implement the OverDispersion Scoring (ODS) algorithm of Park & Raskutti, 2015 and run it on simulations with ~25% and ~75% zeros, respectively.
• mrs_zero25pct.R and mrs_zero75pct.R implement the Moment Ratio Scoring (MRS) algorithm of Park & Park, 2019 and run it on simulations with ~25% and ~75% zeros, respectively.

Pre-trained Models

The pre-trained subdirectory stores simulation results which can be obtained by running our code in the training/ subdirectory. These results are saved in .RData file format.

Evaluation

In the evaluation/ subdirectory, eval_zero25pct.R and eval_zero75pct.R evaluate ZIPBN by calculating the operating characteristics (TPR, FDR, and MCC) for simulations with different percentages of zeros (~25% vs. ~75%). They produce boxplots of the calculated operating characteristics, which are equivalent to the results in Table 2. You can run eval_zero25pct.R and eval_zero75pct.R with the pre-trained models on simulations (pre-trained/), with appropriate specification of filepaths.

Results

The operating characteristics over 30 simulations for zero-inflated scenarios having ~25% and ~75% zeros are summarized in the boxplots below. Table 2 in the paper and the boxplots below are indeed based on the same results.

ZIPBN clearly outperformed ODS and MRS in both cases. As the percentage of zeros increased from (a) ~25% to (b) ~75%, the overall performance of ZIPBN did not deteriorate much, whereas FDR of MRS was doubled. As MRS and ODS are not aimed to accommodate the zero-inflation, they did not work well in the simulations with excessive zeros.