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Integrating Molecular Perspectives: Strategies for Comprehensive Multi-Omics Integrative Data Analysis and Machine Learning Applications in Transcriptomics, Proteomics, and Metabolomics

Pedro H. Godoy Sanches, Nicolly Clemente de Melo, Andreia M. Porcari and 1 more

Biology · 2024 · Review · CC BY

doi:10.3390/biology13110848

Abstract

Simple Summary. Recent high-throughput technologies such as transcriptomics, proteomics, and metabolomics have allowed progress in understanding biological systems at different levels of detail. Even so, it is necessary to integrate multiple omics data sets to achieve a comprehensive understanding of the subject under study. In this article, we review the methods used for integrating transcriptomics, proteomics, and metabolomics data and summarize them in three approaches: combined omics integration, correlation-based integration strategies, and machine learning integrative approaches. Our goal is to showcase the uses and limitations of each approach, allowing researchers to choose the more appropriate tool for each scenario to extract a comprehensive view of a biological system.

Abstract. With the advent of high-throughput technologies, the field of omics has made significant strides in characterizing biological systems at various levels of complexity. Transcriptomics, proteomics, and metabolomics are the three most widely used omics technologies, each providing unique insights into different layers of a biological system. However, analyzing each omics data set separately may not provide a comprehensive understanding of the subject under study. Therefore, integrating multi-omics data has become increasingly important in bioinformatics research. In this article, we review strategies for integrating transcriptomics, proteomics, and metabolomics data, including co-expression analysis, metabolite–gene networks, constraint-based models, pathway enrichment analysis, and interactome analysis. We discuss combined omics integration approaches, correlation-based strategies, and machine learning techniques that utilize one or more types of omics data. By presenting these methods, we aim to provide researchers with a better understanding of how to integrate omics data to gain a more comprehensive view of a biological system, facilitating the identification of complex patterns and interactions that might be missed by single-omics analyses.

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    Yue X., Huan P., Hu Y., Liu B. Integrated Transcriptomic and Proteomic Analyses Reveal Potential Mechanisms Linking Thermal Stress and Depressed Disease Resistance in the Turbot Scophthalmus Maximus. Sci. Rep. 2018;8:1896.

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  91. 91.

    Li G., Zhang B., Zhang H., Xu A., Qian H. Integration of Transcriptomic and Proteomic Analyses Reveals New Insights into the Regulation of Immune Pathways in Midgut of Samia Ricini upon SariNPV Infection. Insects. 2022;13:294.

    Not in our library10.3390/insects13030294

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    Sun Z., Liu Y., He X., Di R., Wang X., Ren C., Zhang Z., Chu M. Integrative Proteomics and Transcriptomics Profiles of the Oviduct Reveal the Prolificacy-Related Candidate Biomarkers of Goats (Capra hircus) in Estrous Periods. Int. J. Mol. Sci. 2022;23:14888.

    Not in our library10.3390/ijms232314888

  93. 93.

    Zhang Q., Li Y., Sun L., Chu S., Xu H., Zhou X. Integration of Transcriptomic and Proteomic Analyses of Rhododendron chrysanthum Pall. in Response to Cold Stress in the Changbai Mountains. Mol. Biol. Rep. 2023;50:3607-3616.

    Not in our library10.1007/s11033-022-08114-5

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    Miao J., Yang Z., Guo W., Liu L., Song P., Ding C., Guan W. Integrative Analysis of the Proteome and Transcriptome in Gastric Cancer Identified LRP1B as a Potential Biomarker. Biomark. Med. 2022;16:1101-1111.

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    Colak D., Alaiya A.A., Kaya N., Muiya N.P., AlHarazi O., Shinwari Z., Andres E., Dzimiri N. Integrated Left Ventricular Global Transcriptome and Proteome Profiling in Human End-Stage Dilated Cardiomyopathy. PLoS ONE. 2016;11:e0162669.

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    Du Y., Clair G.C., Al Alam D., Danopoulos S., Schnell D., Kitzmiller J.A., Misra R.S., Bhattacharya S., Warburton D., Mariani T.J., et al. Integration of Transcriptomic and Proteomic Data Identifies Biological Functions in Cell Populations from Human Infant Lung. Am. J. Physiol. Cell. Mol. Physiol. 2019;317:L347.

    Not in our library10.1152/ajplung.00475.2018

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    Peng Z., He S., Gong W., Xu F., Pan Z., Jia Y., Geng X., Du X. Integration of Proteomic and Transcriptomic Profiles Reveals Multiple Levels of Genetic Regulation of Salt Tolerance in Cotton. BMC Plant Biol. 2018;18:128.

    Not in our library10.1186/s12870-018-1350-1

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    Zheng W., Zhang Y., Sun C., Ge S., Tan Y., Shen H., Yang P. A Multi-Omics Study of Human Testis and Epididymis. Molecules. 2021;26:3345.

    Not in our library10.3390/molecules26113345

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    Liu Y., Beyer A., Aebersold R. On the Dependency of Cellular Protein Levels on MRNA Abundance. Cell. 2016;165:535-550.

    Not in our library10.1016/j.cell.2016.03.014

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    Zhang G., Zhong F., Chen L., Qin P., Li J., Zhi F., Tian L., Zhou D., Lin P., Chen H., et al. Integrated Proteomic and Transcriptomic Analyses Reveal the Roles of Brucella Homolog of BAX Inhibitor 1 in Cell Division and Membrane Homeostasis of Brucella Suis S2. Front. Microbiol. 2021;12:632095.

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    ReactomeGSA - Efficient Multi-Omics Comparative Pathway Analysis · 2020
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    Stark C., Breitkreutz B.J., Reguly T., Boucher L., Breitkreutz A., Tyers M. BioGRID: A General Repository for Interaction Datasets. Nucleic Acids Res. 2006;34:D535.

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    STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets · 2018
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    Wang X.W., Wang T., Schaub D.P., Chen C., Sun Z., Ke S., Hecker J., Maaser-Hecker A., Zeleznik O.A., Zeleznik R., et al. Benchmarking Omics-Based Prediction of Asthma Development in Children. Respir. Res. 2023;24:62.

    Not in our library10.1186/s12931-023-02368-8

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    Arjmand B., Hamidpour S.K., Tayanloo-Beik A., Goodarzi P., Aghayan H.R., Adibi H., Larijani B. Machine Learning: A New Prospect in Multi-Omics Data Analysis of Cancer. Front. Genet. 2022;13:824451.

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    Wekesa J.S., Kimwele M. A Review of Multi-Omics Data Integration through Deep Learning Approaches for Disease Diagnosis, Prognosis, and Treatment. Front. Genet. 2023;14:1199087.

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    Li R., Li L., Xu Y., Yang J. Machine Learning Meets Omics: Applications and Perspectives. Brief. Bioinform. 2022;23:bbab460.

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    Leite D.M.C., Brochet X., Resch G., Que Y.A., Neves A., Peña-Reyes C. Computational Prediction of Inter-Species Relationships through Omics Data Analysis and Machine Learning. BMC Bioinform. 2018;19:151-159.

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    Alharbi F., Vakanski A. Machine Learning Methods for Cancer Classification Using Gene Expression Data: A Review. Bioengineering. 2023;10:173.

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    Bashiri A., Ghazisaeedi M., Safdari R., Shahmoradi L., Ehtesham H. Improving the Prediction of Survival in Cancer Patients by Using Machine Learning Techniques: Experience of Gene Expression Data: A Narrative Review. Iran. J. Public Health. 2017;46:165.

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    Hijazi H., Chan C. A Classification Framework Applied to Cancer Gene Expression Profiles. J. Healthc. Eng. 2013;4:255-283.

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    Khalsan M., MacHado L.R., Al-Shamery E.S., Ajit S., Anthony K., Mu M., Agyeman M.O. A Survey of Machine Learning Approaches Applied to Gene Expression Analysis for Cancer Prediction. IEEE Access. 2022;10:27522-27534.

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    Ravindran U., Gunavathi C. A Survey on Gene Expression Data Analysis Using Deep Learning Methods for Cancer Diagnosis. Prog. Biophys. Mol. Biol. 2023;177:1-13.

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    Tabares-Soto R., Orozco-Arias S., Romero-Cano V., Bucheli V.S., Rodríguez-Sotelo J.L., Jiménez-Varón C.F. A Comparative Study of Machine Learning and Deep Learning Algorithms to Classify Cancer Types Based on Microarray Gene Expression Data. PeerJ Comput. Sci. 2020;2020:e270.

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    Yuan F., Lu L., Zou Q. Analysis of Gene Expression Profiles of Lung Cancer Subtypes with Machine Learning Algorithms. Biochim. Biophys. Acta (BBA)-Mol. Basis Dis. 2020;1866:165822.

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    He B., Thomson M., Subramaniam M., Perez R., Ye C.J., Zou J. CloudPred: Predicting Patient Phenotypes from Single-Cell RNA-Seq. Pac. Symp. Biocomput. 2022;2021:337-348.

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    Ma Y., Chen J., Wang T., Zhang L., Xu X., Qiu Y., Xiang A.P., Huang W. Accurate Machine Learning Model to Diagnose Chronic Autoimmune Diseases Utilizing Information From B Cells and Monocytes. Front. Immunol. 2022;13:870531.

    Not in our library10.3389/fimmu.2022.870531

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    Galdos F.X., Xu S., Goodyer W.R., Duan L., Huang Y.V., Lee S., Zhu H., Lee C., Wei N., Lee D., et al. DevCellPy Is a Machine Learning-Enabled Pipeline for Automated Annotation of Complex Multilayered Single-Cell Transcriptomic Data. Nat. Commun. 2022;13:5271.

    Not in our library10.1038/s41467-022-33045-x

  119. 119.

    Liu J., Fan Z., Zhao W., Zhou X. Machine Intelligence in Single-Cell Data Analysis: Advances and New Challenges. Front. Genet. 2021;12:655536.

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    Patil A.R., Schug J., Liu C., Lahori D., Descamps H.C., Naji A., Kaestner K.H., Faryabi R.B., Vahedi G. Modeling Type 1 Diabetes Progression Using Machine Learning and Single-Cell Transcriptomic Measurements in Human Islets. Cell Rep. Med. 2024;5:101535.

    Not in our library10.1016/j.xcrm.2024.101535

  121. 121.

    Hu Y., Hase T., Li H.P., Prabhakar S., Kitano H., Ng S.K., Ghosh S., Wee L.J.K. A Machine Learning Approach for the Identification of Key Markers Involved in Brain Development from Single-Cell Transcriptomic Data. BMC Genom. 2016;17:1025.

    Not in our library10.1186/s12864-016-3317-7

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    Vrahatis A.G., Tasoulis S.K., Maglogiannis I., Plagianakos V.P. Recent Machine Learning Approaches for Single-Cell RNA-Seq Data Analysis. Stud. Comput. Intell. 2020;891:65-79.

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    Barrett T., Wilhite S.E., Ledoux P., Evangelista C., Kim I.F., Tomashevsky M., Marshall K.A., Phillippy K.H., Sherman P.M., Holko M., et al. NCBI GEO: Archive for Functional Genomics Data Sets—Update. Nucleic Acids Res. 2013;41:D991-D995.

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    Weinstein J.N., Collisson E.A., Mills G.B., Shaw K.R.M., Ozenberger B.A., Ellrott K., Sander C., Stuart J.M., Chang K., Creighton C.J., et al. The Cancer Genome Atlas Pan-Cancer Analysis Project. Nat. Genet. 2013;45:1113-1120.

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    Johnson N.T., Dhroso A., Hughes K.J., Korkin D. Biological Classification with RNA-Seq Data: Can Alternatively Spliced Transcript Expression Enhance Machine Learning Classifiers? RNA. 2018;24:1119-1132.

    Not in our library10.1261/rna.062802.117

  126. 126.

    Kuo W.P., Kim E.Y., Trimarchi J., Jenssen T.K., Vinterbo S.A., Ohno-Machado L. A Primer on Gene Expression and Microarrays for Machine Learning Researchers. J. Biomed. Inform. 2004;37:293-303.

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    Kelchtermans P., Bittremieux W., De Grave K., Degroeve S., Ramon J., Laukens K., Valkenborg D., Barsnes H., Martens L. Machine Learning Applications in Proteomics Research: How the Past Can Boost the Future. Proteomics. 2014;14:353-366.

    Not in our library10.1002/pmic.201300289

  128. 128.

    Swan A.L., Mobasheri A., Allaway D., Liddell S., Bacardit J. Application of Machine Learning to Proteomics Data: Classification and Biomarker Identification in Postgenomics Biology. OMICS. 2013;17:595-610.

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    Barla A., Jurman G., Riccadonna S., Merler S., Chierici M., Furlanello C. Machine Learning Methods for Predictive Proteomics. Brief. Bioinform. 2008;9:119-128.

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    Neely B.A., Dorfer V., Martens L., Bludau I., Bouwmeester R., Degroeve S., Deutsch E.W., Gessulat S., Käll L., Palczynski P., et al. Toward an Integrated Machine Learning Model of a Proteomics Experiment. J. Proteome Res. 2023;22:681-696.

    Not in our library10.1021/acs.jproteome.2c00711

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    Desaire H., Go E.P., Hua D. Advances, Obstacles, and Opportunities for Machine Learning in Proteomics. Cell Rep. Phys. Sci. 2022;3:101069.

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    Highly accurate protein structure prediction with AlphaFold · 2021
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  133. 133.

    Sonsare P.M., Gunavathi C. Investigation of Machine Learning Techniques on Proteomics: A Comprehensive Survey. Prog. Biophys. Mol. Biol. 2019;149:54-69.

    Not in our library10.1016/j.pbiomolbio.2019.09.004

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    Vishnoi S., Matre H., Garg P., Pandey S.K. Artificial Intelligence and Machine Learning for Protein Toxicity Prediction Using Proteomics Data. Chem. Biol. Drug Des. 2020;96:902-920.

    Not in our library10.1111/cbdd.13701

  135. 135.

    Mann M., Kumar C., Zeng W.F., Strauss M.T. Artificial Intelligence for Proteomics and Biomarker Discovery. Cell Syst. 2021;12:759-770.

    Not in our library10.1016/j.cels.2021.06.006

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    Li J., Zhou K., Mu B. Machine Learning for Mass Spectrometry Data Analysis in Proteomics. Curr. Proteom. 2020;18:620-634.

    Not in our library10.2174/1570164617999201023145304

  137. 137.
    Deep Learning in Proteomics · 2020
    No notice
  138. 138.

    Hindson J. Proteomics and Machine-Learning Models for Alcohol-Related Liver Disease Biomarkers. Nat. Rev. Gastroenterol. Hepatol. 2022;19:488.

    Not in our library10.1038/s41575-022-00655-1

  139. 139.

    Cox J. Prediction of Peptide Mass Spectral Libraries with Machine Learning. Nat. Biotechnol. 2022;41:33-43.

    Not in our library10.1038/s41587-022-01424-w

  140. 140.

    Sengupta A., Naresh G., Mishra A., Parashar D., Narad P. Proteome Analysis Using Machine Learning Approaches and Its Applications to Diseases. Adv. Protein Chem. Struct. Biol. 2021;127:161-216.

    Not in our library10.1016/bs.apcsb.2021.02.003

  141. 141.

    Tilocca B., Britti D., Urbani A., Roncada P. Computational Immune Proteomics Approach to Target COVID-19. J. Proteome Res. 2020;19:4233-4241.

    Not in our library10.1021/acs.jproteome.0c00553

  142. 142.

    Bernardes J.S., Pedreira C.E. A Review of Protein Function Prediction Under Machine Learning Perspective. Recent Pat. Biotechnol. 2013;7:122-141.

    Not in our library10.2174/18722083113079990006

  143. 143.

    Worley B., Powers R. Multivariate Analysis in Metabolomics. Curr. Metabolomics. 2013;1:92.

    Not in our library10.2174/2213235x11301010092

  144. 144.
    Metabolomics based predictive biomarker model of ARDS: A systemic measure of clinical hypoxemia. · 2017
    Not checked
  145. 145.

    Martín-Blázquez A., Díaz C., González-Flores E., Franco-Rivas D., Jiménez-Luna C., Melguizo C., Prados J., Genilloud O., Vicente F., Caba O., et al. Untargeted LC-HRMS-Based Metabolomics to Identify Novel Biomarkers of Metastatic Colorectal Cancer. Sci. Rep. 2019;9:20198.

    Not in our library10.1038/s41598-019-55952-8

  146. 146.

    Goldberg E., Ievari-Shariati S., Kidane B., Kim J., Banerji S., Qing G., Srinathan S., Murphy L., Aliani M. Comparative Metabolomics Studies of Blood Collected in Streck and Heparin Tubes from Lung Cancer Patients. PLoS ONE. 2021;16:e0249648.

    Not in our library10.1371/journal.pone.0249648

  147. 147.

    French C.D., Willoughby R.E., Pan A., Wong S.J., Foley J.F., Wheat L.J., Fernandez J., Encarnacion R., Ondrush J.M., Fatteh N., et al. NMR Metabolomics of Cerebrospinal Fluid Differentiates Inflammatory Diseases of the Central Nervous System. PLoS Negl. Trop. Dis. 2018;12:e0007045.

    Not in our library10.1371/journal.pntd.0007045

  148. 148.

    Collakova E., Aghamirzaie D., Fang Y., Klumas C., Tabataba F., Kakumanu A., Myers E., Heath L.S., Grene R. Metabolic and Transcriptional Reprogramming in Developing Soybean (Glycine Max) Embryos. Metabolites. 2013;3:347-372.

    Not in our library10.3390/metabo3020347

  149. 149.
    Transcriptomic and metabolomic profiling of drought-tolerant and susceptible sesame genotypes in response to drought stress · 2019
    No notice
  150. 150.

    Alreshidi M.M. Selected Metabolites Profiling of Staphylococcus Aureus Following Exposure to Low Temperature and Elevated Sodium Chloride. Front. Microbiol. 2020;11:512774.

    Not in our library10.3389/fmicb.2020.00834

  151. 151.

    Broughton-Neiswanger L.E., Rivera-Velez S.M., Suarez M.A., Slovak J.E., Piñeyro P.E., Hwang J.K., Villarino N.F. Urinary Chemical Fingerprint Left behind by Repeated NSAID Administration: Discovery of Putative Biomarkers Using Artificial Intelligence. PLoS ONE. 2020;15:e0228989.

    Not in our library10.1371/journal.pone.0228989

  152. 152.
    Increased mortality of acute respiratory distress syndrome was associated with high levels of plasma phenylalanine · 2020
    Not checked
  153. 153.

    Monteleone A.M., Troisi J., Serena G., Fasano A., Grave R.D., Cascino G., Marciello F., Calugi S., Scala G., Corrivetti G., et al. The Gut Microbiome and Metabolomics Profiles of Restricting and Binge-Purging Type Anorexia Nervosa. Nutrients. 2021;13:507.

    Not in our library10.3390/nu13020507

  154. 154.

    Caterino M., Costanzo M., Fedele R., Cevenini A., Gelzo M., Di Minno A., Andolfo I., Capasso M., Russo R., Annunziata A., et al. The Serum Metabolome of Moderate and Severe COVID-19 Patients Reflects Possible Liver Alterations Involving Carbon and Nitrogen Metabolism. Int. J. Mol. Sci. 2021;22:9548.

    Not in our library10.3390/ijms22179548

  155. 155.

    Silva A.A.R., Cardoso M.R., Cardoso De Oliveira D., Godoy P., Cecília M., Talarico R., Marrero Gutiérrez J., Rodrigues Peres R.M., De Carvalho L.M., Angelo Da N., et al. Plasma Metabolome Signatures to Predict Responsiveness to Neoadjuvant Chemotherapy in Breast Cancer. Cancers. 2024;16:2473.

    Not in our library10.3390/cancers16132473

  156. 156.
    Applications of machine learning in metabolomics: Disease modeling and classification · 2022
    No notice
  157. 157.

    Auslander N., Yizhak K., Weinstock A., Budhu A., Tang W., Wang X.W., Ambs S., Ruppin E. A Joint Analysis of Transcriptomic and Metabolomic Data Uncovers Enhanced Enzyme-Metabolite Coupling in Breast Cancer. Sci. Rep. 2016;6:29662.

    Not in our library10.1038/srep29662

  158. 158.

    Ghosh T., Zhang W., Ghosh D., Kechris K. Predictive Modeling for Metabolomics Data. Methods Mol. Biol. 2020;2104:313.

    Not in our library10.1007/978-1-0716-0239-3_16

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