Beyond Single Algorithms: A Framework for Validating and Aggregating Active Modules in Genetic Interaction Networks

1High-throughput sequencing methods have generated vast amounts of genetic data for candidate gene studies. However, the complexity of the disease genetic structure often results in a large number of candidate genes and poses a significant challenge for these studies. To explore the multi-gene interactions and elucidate the genetic mechanism, candidate genes are often analyzed through Gene-Gene interaction (GGI) networks. These networks can become very large, necessitating efficient methods to reduce their complexity. Active Module Identification (AMI) is a common method to analyze GGI networks by identifying enriched subnetworks representing relevant biological processes. Multiple AMI algorithms have been developed for biological datasets, and a comparative analysis of their behaviors across a variety of datasets is crucial to their application. In this study, we introduce a framework to compare and aggregate the modules produced by multiple AMI algorithms. We first used a modified Empirical Pipeline to validate the output of four AMI algorithms - PAPER, DOMINO, FDRnet, and HotNet2 - and find that no single algorithm performs well across the different datasets. Using the Earth Movers Distance to measure pairwise module similarity, we find that the outputs of different algorithms are structurally distinct, suggesting that each captures different aspects of the underlying biology. These findings suggest that a comprehensive analysis requires the aggregation of outputs from multiple algorithms. We propose two methods to this end: a spectral clustering approach for module aggregation, and an algorithm that combines modules with similar network structures called Greedy Conductance-based Merging (GCM). The merging algorithm not only allows researchers to obtain a set of cohesive modules from multiple algorithms, it also has the potential of identifying "hidden" genes that are not present in the original input data from the network. Overall, our results advance our understanding of AMI algorithms and how they should be applied. Tools and workflows developed in this study will facilitate researchers working with GGI and AMI algorithms to enhance their analyses. Our code is freely available at https://github.com/LiuJ0/AMI-Benchmark/.