Ornella Cominetti

Sergio Oller-Moreno

Sumeet Agarwal

Frontiers in Molecular Biosciences

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Editorial: Advances in methodsand tools for multi-omics dataanalysisOrnella Cominetti  1*, Sumeet Agarwal  2 andSergio Oller-Moreno  31Nestlé Research Center, Lausanne, Switzerland, 2Indian Institute of Technology Delhi, New Delhi, India,3Institute for Bioengineering of Catalonia (IBEC), The Barcelona Institute of Science and Technology,Barcelona, SpainKEYWORDSmulti-omics, personalized medicine, machine learning, integrative omics, deepgenerative models, protein protein interaction (PPI) network, EATRIS Europeaninfrastructure for translational medicineEditorial on the Research TopicAdvances in methods and tools for multi-omics data analysisMulti-omics data analysis is a rapidly growing field with significant potential inpersonalized medicine. Despite the many advances in this domain, there are stillimportant challenges that need to be addressed, such as standardization of methods andlimitations in interpreting results.The Research Topic “Advances in Methods and Tools for Multi-Omics Data Analysis”showcases novel techniques and tools, including machine/deep learning tools, multi-factoranalysis, Bayesian statistics, network-based models, and computational approaches fornetwork-based integrative multi-omics analysis.This article Research Topic comprises one perspective article, which addresses thetranslational challenges of multi-omics research in the realm of European personalizedmedicine, four review articles covering challenges in the path towards achieving precisionmedicine in cancer treatment and immuno-oncology, computational approaches fornetwork-based integrative multi-omics analysis, deep generative models for learningjoint embeddings of single-cell multi-omics data, and methods for characterization andvisualization of protein–protein interaction networks in a multi-omics integration context.Additionally, there are five original research articles ranging from the presentation of anew statistical framework for the analysis of longitudinal multi-omics data to the applicationof multi-omics methods to different diseases such as cancer, cutaneous melanoma,inflammatory diseases, myocardial dysfunction, and tuberous sclerosis complex-relatedangiomyolipoma.Various machine learning (ML) techniques that can be used to integrate multi-omicsdata are discussed in this Research Topic. Network-based diffusion/propagation methodsand multiview/multi-modal ML are two such techniques that can exploit informationcaptured in each omics dataset and infer associations between different data types. Deeplearning methods are an example of multiview/multi-modal learning, which can capturecomplex non-linear associations in a multi-layered manner.Complex longitudinal omics datasets require new statistical approaches for theiranalysis, and ALASCA, a new package presented by Jarmund, Madssen andOPEN ACCESSEDITED AND REVIEWED BYWolfram Weckwerth,University of Vienna, Austria*CORRESPONDENCEOrnella Cominetti,Ornella.Cominetti@rd.nestle.comRECEIVED 15 March 2023ACCEPTED 14 April 2023PUBLISHED 24 April 2023CITATIONCominetti O, Agarwal S andOller-Moreno S (2023), Editorial:Advances in methods and tools for multi-omics data analysis.Front. Mol. Biosci. 10:1186822.doi: 10.3389/fmolb.2023.1186822COPYRIGHT© 2023 Cominetti, Agarwal and Oller-Moreno. This is an open-access articledistributed under the terms of theCreative Commons Attribution License(CC BY). The use, distribution orreproduction in other forums ispermitted, provided the original author(s)and the copyright owner(s) are creditedand that the original publication in thisjournal is cited, in accordance withaccepted academic practice. No use,distribution or reproduction is permittedwhich does not comply with these terms.Frontiers in Molecular Biosciences frontiersin.org01TYPE EditorialPUBLISHED 24 April 2023DOI 10.3389/fmolb.2023.1186822Giskeødegård, shows a promising framework for tackling thelongitudinal and multivariate nature of multi-omics studies, aswell as covariate adjustment. In contrast, Magnusson et al. used adifferent approach for longitudinal omics data analysis: they appliedlinear mathematical mixed time-delayed splice variant models topredict protein abundances from mRNA expression.The review article by Brombacher and colleagues provides asystematic overview of current deep generative models (DGM)-based approaches for learning joint embeddings from multi-omics data and illustrates how small sample sizes impact theamount of information that can be recovered from such datasets.Specifically, the review examines how the performance of popularDGM-based approaches to infer joint low-dimensionalrepresentations is influenced by varying numbers of cells,which is particularly relevant at the stage of designing anexperiment.Robin and colleagues discuss the role of protein-proteininteractions (PPIs) in cellular mechanisms and the constructionof PPI networks. PPIs are involved in physical and biochemicalprocesses in structured environments and can be constructed usingprediction methods and high-throughput experiments.Computational methods have emerged as a promising way toidentify PPIs, and integration methods can be used to filter falseinteractions. Visualization is a key step in analyzing PPI networks,but the complexity of proteomes in different organisms presents achallenge.The article by Oldoni and colleagues summarizes the outcome ofthe 2021 European Infrastructure for Translational Medicine(EATRIS)-Plus Multi-omics Stakeholder Group workshop. Thismultidisciplinary and cross-institutional working group aims tobecome a European reference group for implementingpersonalized medicine across Europe. This perspective articlediscusses the potential of precision medicine in healthcare andthe challenges associated with it, such as moving beyondgenomics to integrated multi-omics and multi-modal complexbiomarker generation, new technologies and digital health, datastandardization to enable multi-modal integration and AI-supported drug modeling, variability in omics data at source,data privacy and regulatory aspects, and economic implications.Moreover, the Research Topic includes original research articlesthat demonstrate the application of multi-omics in various diseases.For example, Wang and colleagues present a study where ultra-performance liquid chromatography-mass spectrometer (UPLC-MS) was used to measure plasma proteins and metabolites inpatients with renal cysts, sporadic angiomyolipoma, and tuberoussclerosis complex (TSC)-related angiomyolipoma before and afterimmunosuppressant treatment, with the aim of finding potentialdiagnostic and prognostic biomarkers as well as revealing theunderlying mechanism of TSC tumorigenesis.In another contribution, Wang et al. applied Least AbsoluteShrinkage and Selection Operator (LASSO) and Support VectorMachine-Recursive Feature Elimination (SVM-RFE) algorithms toidentify a cancer cell stemness feature, identifying 3 specific subtypesof melanoma with different survival outcomes.One of the key challenges in multi-omics data analysis is thecombination of different data types to identify composite biomarkersignatures. This merging is complicated by the fact that multi-omicsdata often needs to be coupled with other data, such as imaging data,phenotypic data, and medical data (Electronic Health Records andpatient-related outcomes). The integration of multi-omics andmulti-modal data marks a significant step closer to personalizedmedicine, althoughmany challenges remain before these biomarkerscan be fully implemented in routine clinical care. The contributionsin this Research Topic represent a small but solid base of steptowards achieving these goals.Author contributionsAll authors listed havemade a substantial, direct, and intellectualcontribution to the work and approved it for publication.Conflict of interestThe authors declare that the research was conducted in theabsence of any commercial or financial relationships that could beconstrued as a potential conflict of interest.Publisher’s noteAll claims expressed in this article are solely those of the authorsand do not necessarily represent those of their affiliatedorganizations, or those of the publisher, the editors and thereviewers. Any product that may be evaluated in this article, orclaim that may be made by its manufacturer, is not guaranteed orendorsed by the publisher.Frontiers in Molecular Biosciences frontiersin.org02Cominetti et al. 10.3389/fmolb.2023.1186822

Editorial: Advances in methods and tools for multi-omics data analysis

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Editorial: Advances in methods and tools for multi-omics data analysis

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