AOP: An R Package For Sufficient Causal Analysis in Pathway-based Screening of Drugs and Chemicals for Adversity

Abstract

Summary: How can I quickly find the key events in a pathway that I need to monitor to predict that a/an beneficial/adverse event/outcome will occur? This is a key question when using signaling pathways for drug/chemical screening in pharmacology, toxicology and risk assessment. By identifying these sufficient causal key events, we have fewer events to monitor for a pathway, thereby decreasing assay costs and time, while maximizing the value of the information. I have developed the "aop" package which uses back-door analysis of causal networks to identify these minimal sets of key events that are sufficient for making causal predictions. Availability and Implementation: The source for the aop package is available online at Github at https://github.com/DataSciBurgoon/aop and can be installed using the R devtools package. The aop package runs within the R statistical environment. The package has functions that can take pathways (as directed graphs) formatted as a Cytoscape JSON file as input, or pathways can be represented as directed graphs using the R/Bioconductor "graph" package. The "aop" package has functions that can perform backdoor analysis to identify the minimal set of key events for making causal predictions. Contact: lyle.d.burgoon@usace.army.mil