AMEND 2.0: module identification and multi-omic data integration with multiplex-heterogeneous graphs.

Background: Multi-omic studies provide comprehensive insight into biological systems by evaluating cellular changes between normal and pathological conditions at multiple levels of measurement. Biological networks, which represent interactions or associations between biomolecules, have been highly effective in facilitating omic analysis. However, current network-based methods lack generalizability to accommodate multiple data types across a range of diverse experiments.

Results: We present AMEND 2.0, an updated active module identification method which can analyze multiplex and/or heterogeneous networks integrated with multi-omic data in a highly generalizable framework, in contrast to existing methods, which are mostly appropriate for at most two specific omic types. It is powered by Random Walk with Restart for multiplex-heterogeneous networks, with additional capabilities including degree bias adjustment and biased random walk for multi-objective module identification. AMEND was applied to two real-world multi-omic datasets: renal cell carcinoma data from The cancer genome atlas and an O-GlcNAc Transferase knockout study. Additional analyses investigate the performance of various subroutines of AMEND on tasks of node ranking and degree bias adjustment.

Conclusions: While the analysis of multi-omic datasets in a network context is poised to provide deeper understanding of health and disease, new methods are required to fully take advantage of this increasingly complex data. The current study combines several network analysis techniques into a single versatile method for analyzing biological networks with multi-omic data that can be applied in many diverse scenarios. Software is freely available in the R programming language at https://github.com/samboyd0/AMEND .