Interpretation of network-based integration from multi-omics longitudinal data

Abstract

Multi-omics integration is key to fully understand complex biological processes in an holistic manner. Furthermore, multi-omics combined with new longitudinal experimental design can unreveal dynamic relationships between omics layers and identify key players or interactions in system development or complex phenotypes. However, integration methods have to address various experimental designs and do not guarantee interpretable biological results. The new challenge of multi-omics integration is to solve interpretation and unlock the hidden knowledge within the multi-omics data. In this paper, we go beyond integration and propose a generic approach to face the interpretation problem. From multi-omics longitudinal data, this approach builds and explores hybrid multi-omics networks composed of both inferred and known relationships within and between omics layers. With smart node labelling and propagation analysis, this approach predicts regulation mechanisms and multi-omics functional modules. We applied the method on 3 case studies with various multi-omics designs and identified new multi-layer interactions involved in key biological functions that could not be revealed with single omics analysis. Moreover, we highlighted interplay in the kinetics that could help identify novel biological mechanisms. This method is available as an R package netOmics to readily suit any application.

Overview of methods for characterization and visualization of a protein–protein interaction network in a multi-omics integration context

“…Integration of a PPI network in a multi-omics context is nowadays an essential issue in the understanding of biological mechanisms ( Hawe, Theis and Heinig, 2019 ; Bodein et al, 2021 ; Dimitrakopoulos et al, 2021 ). To integrate an interaction into a network, it must first be estimated by a so-called confidence score ( Stelzl and Wanker, 2006 ; Li et al, 2016 ; Xu et al, 2021 ), representing probability that the interaction is accurately identified by algorithms and is expressed as a percentage ( Kamburov et al, 2012 ; Peng et al, 2017 ).…”

“…However, in a multi-omics integrations context one seeks above all to connect information from different omics fields (transcriptomics, proteomics, metabolomics, lipidomics, and metabolomics ( Haas et al, 2017 ; Fan, Zhou and Ressom, 2020 ; Cansu Demirel, Kaan Arici and Tuncbag, 2022 ). In this context, multi-layer algorithms for visualization are preferable to force-directed algorithms ( Bodein et al, 2021 ; Dursun, Kwitek and Bozdag, 2021 ; Marín-Llaó et al, 2021 ). There are several algorithms for implementing multi-layer networks, in the context of multi-omics integration, the most highlighted implementation is the one named by Hammoud and Kramer, (2020) : “Interactive/Interconnected/Interdependent Networks and Networks of Networks Implementation.” This implementation has as input a set of monoplex networks (single layer networks, e.g., PPI network).…”

Merging the exposome into an integrated framework for “omics” sciences

“…Mapping environment-to-phenotype has already been shown as an effective theoretical approach to study population adaptation strategies ( Xue et al., 2019 ), new frameworks to model complex gene-environment interaction show promise for more accurate disease risk estimation ( Li et al., 2019a ), and advanced approaches to integrate multimodal ( Pan et al., 2022 ) and longitudinal multi-omics are emerging ( Bodein et al., 2021 ; Kaur et al., 2020 ; Li et al., 2020 ). However, extending functional exposomics models to complex organisms and societies entails challenges, including i) the difficulty to incorporate measures of psychosocial and perceived factors alongside biological and physical factors, ii) the complexity to integrate individualized molecular measures with community-level data, iii) the fusion of exposure, response, and effect measures in interpretable dynamic networks and most critically, iv) the translation of knowledge to operable practices that improve wellbeing.…”

SUBATOMIC: a SUbgraph BAsed mulTi-OMIcs Clustering framework to analyze integrated multi-edge networks

“…Methods can be co-expression based, topology based, pan-sample based and multi-edge based including tools such as WGCNA, SimMod, ModulOmics, and LemonTree [31][32][33][34][35][36][37][38][39][40][41].…”