RT Journal Article
SR Electronic
T1 AOP: An R Package For Sufficient Causal Analysis in Pathway-based Screening of Drugs and Chemicals for Adversity
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 029694
DO 10.1101/029694
A1 Lyle D. Burgoon
YR 2015
UL http://biorxiv.org/content/early/2015/10/23/029694.1.abstract
AB 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{at}usace.army.mil