Using PAPER to identify active modules in an aneuploidy dataset
Jason Liu 1 , Karen Schindler 1 , Min Xu 2 , Jinchuan Xing 11 Department of Genetics and the Human Genetics Institute of New Jersey, Rutgers, TheState University of New Jersey, Piscataway, NJ;2 Department of Statistics, Rutgers, The State University of New Jersey, Piscataway, NJ
Poster # 42
Aneuploidy in eggs is among the most common contributors to female infertility andmiscarriage. In this study, we utilize active module identification (AMI) as a key methodfor discovering candidate disease mechanisms of aneuploidy. These algorithms projectgene expression or mutation data onto biological networks to pinpoint genesubnetworks exhibiting a strong signal of overrepresentation. Here, we used the PAPER(Preferential Attachment Plus Erdős-Rényi) model for community detection as an AMIalgorithm. To do this, we first built a protein-protein interaction network from 401candidate genes associated with aneuploidy and analyze it with PAPER. We observedseven discrete modules. Several modules are enriched for different biological functions,such as cytokinesis, cell cycle processes, and nuclear division. To validate our result,we use the Empirical Pipeline (EMP) to evaluate PAPER's performance and ability toproduce context-specific enrichment. Of the sixteen enriched GO terms produced byPAPER, fifteen are empirically-validated, outperforming all other algorithms evaluated.This analysis highlights key mechanisms involved in aneuploidy, which will guide thedirection of future studies. We also demonstrated PAPER as an effective tool for AMI.