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Related Experiment Video

Updated: May 29, 2025

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Published on: September 20, 2024

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

Samuel S Boyd¹, Chad Slawson^2,3,4, Jeffrey A Thompson^5,3

¹Department of Biostatistics and Data Science, University of Kansas Medical Center, Kansas City, KS, 66160, USA. samsboyd21@gmail.com.

BMC Bioinformatics

|February 5, 2025

Summary

This summary is machine-generated.

AMEND 2.0 offers a versatile framework for analyzing multi-omic data using biological networks. This updated method enhances the generalizability of network analysis across diverse experiments and data types.

Keywords:

Active module identification Biological networks Multi-omic data integration

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Area of Science:

Systems biology
Bioinformatics
Network analysis

Background:

Multi-omic studies offer comprehensive biological insights by examining cellular changes.
Biological networks are effective for omics analysis but current methods lack generalizability for diverse data types.
Existing network methods are often limited to specific omics types, hindering broad application.

Purpose of the Study:

To present AMEND 2.0, an advanced method for active module identification.
To enable analysis of multiplex and heterogeneous networks integrated with multi-omic data.
To provide a highly generalizable framework for diverse biological network analyses.

Main Methods:

AMEND 2.0 utilizes Random Walk with Restart for multiplex-heterogeneous networks.
Incorporates degree bias adjustment and biased random walk for multi-objective module identification.
Applied to renal cell carcinoma and O-GlcNAc Transferase knockout multi-omic datasets.

Main Results:

AMEND 2.0 demonstrates generalizability across multiplex and heterogeneous networks.
The method successfully analyzed diverse multi-omic datasets.
Evaluated performance of subroutines for node ranking and degree bias adjustment.

Conclusions:

Network-based multi-omic data analysis requires advanced methods for deeper biological understanding.
AMEND 2.0 integrates network analysis techniques into a versatile tool for diverse scenarios.
The software is available in R for broader accessibility and application.