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Web-based multi-omics integration using the Analyst software suite

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Abstract

The growing number of multi-omics studies demands clear conceptual workflows coupled with easy-to-use software tools to facilitate data analysis and interpretation. This protocol covers three key components involved in multi-omics analysis, including single-omics data analysis, knowledge-driven integration using biological networks and data-driven integration through joint dimensionality reduction. Using the dataset from a recent multi-omics study of human pancreatic islet tissue and plasma samples, the first section introduces how to perform transcriptomics/proteomics data analysis using ExpressAnalyst and lipidomics data analysis using MetaboAnalyst. On the basis of significant features detected in these workflows, the second section demonstrates how to perform knowledge-driven integration using OmicsNet. The last section illustrates how to perform data-driven integration from the normalized omics data and metadata using OmicsAnalyst. The complete protocol can be executed in ~2 h. Compared with other available options for multi-omics integration, the Analyst software suite described in this protocol enables researchers to perform a wide range of omics data analysis tasks via a user-friendly web interface.

Key points

  • This protocol for web-based multi-omics integration covers single-omics data analysis using ExpressAnalyst and MetaboAnalyst, followed by knowledge-driven integration using OmicsNet and data-driven integration using OmicsAnalyst.

  • This series of web-based tools allows researchers to perform a wide range of omics data analysis tasks via a user-friendly web interface, helping to democratize omics data analysis and empower researchers without strong statistics and programming backgrounds.

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Fig. 1: Overview of multi-omics analysis pipelines.
Fig. 2: Results from the single-omics analysis.
Fig. 3: Knowledge-driven multi-omics network created through signal distillation with OmicsNet.
Fig. 4: Knowledge-driven multi-omics network created through signal enrichment with OmicsNet.
Fig. 5: Data-driven MCIA results from OmicsAnalyst.

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Data availability

All datasets used in this protocol are from a previously published study that collected multi-omics data from islet tissue (transcriptomics and proteomics) and plasma (lipidomics) from human donors undergoing pancreatic surgery30. The example datasets used in this protocol are integrated as examples throughout the four web tools. The unprocessed matrices are included as examples in ExpressAnalyst (www.expressanalyst.ca) and MetaboAnalyst (www.metaboanalyst.ca). Lists of significant features are included as examples in OmicsNet (www.omicsnet.ca). Normalized matrices are included as examples in OmicsAnalyst (www.omicsanalyst.ca). See the ‘Materials’ section for further information about the example datasets.

Code availability

ExpressAnalyst, MetaboAnalyst, OmicsNet and OmicsAnalyst are all freely available as web-based applications. The underlying R packages for each tool are freely available as GitHub repositories: ExpressAnalystR (https://github.com/xia-lab/ExpressAnalystR), MetaboAnalystR (https://github.com/xia-lab/MetaboAnalystR), OmicsNetR (https://github.com/xia-lab/OmicsNetR) and OmicsAnalystR (https://github.com/xia-lab/OmicsAnalystR) under the GNU General Public License version 2 or later.

References

  1. Hasin, Y., Seldin, M. & Lusis, A. Multi-omics approaches to disease. Genome Biol. 18, 83 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  2. Han, J.-D. J. Understanding biological functions through molecular networks. Cell Res. 18, 224–237 (2008).

    Article  CAS  PubMed  Google Scholar 

  3. Subramanian, I., Verma, S., Kumar, S., Jere, A. & Anamika, K. Multi-omics data integration, interpretation, and its application. Bioinform. Biol. Insights 14, 1177932219899051 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  4. Eicher, T. et al. Metabolomics and multi-omics integration: a survey of computational methods and resources. Metabolites https://doi.org/10.3390/metabo10050202 (2020).

  5. Huang, S., Chaudhary, K. & Garmire, L. X. More is better: recent progress in multi-omics data integration methods. Front. Genetics https://doi.org/10.3389/fgene.2017.00084 (2017).

  6. Quinn, T. P. et al. A field guide for the compositional analysis of any-omics data. GigaScience https://doi.org/10.1093/gigascience/giz107 (2019).

  7. Verheijen, M. et al. Towards the development of an omics data analysis framework. Reg. Toxicol. Pharmacol. 112, 104621 (2020).

    Article  Google Scholar 

  8. Tarazona, S., Arzalluz-Luque, A. & Conesa, A. Undisclosed, unmet and neglected challenges in multi-omics studies. Nat. Comput. Sci. 1, 395–402 (2021).

    Article  PubMed  Google Scholar 

  9. Gomez-Cabrero, D. et al. Data integration in the era of omics: current and future challenges. BMC Syst. Biol. 8, I1 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  10. Palsson, B. & Zengler, K. The challenges of integrating multi-omic data sets. Nat. Chem. Biol. 6, 787–789 (2010).

    Article  PubMed  Google Scholar 

  11. Jendoubi, T. Approaches to integrating metabolomics and multi-omics data: a primer. Metabolites 11, 184 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Kim, D. et al. Knowledge boosting: a graph-based integration approach with multi-omics data and genomic knowledge for cancer clinical outcome prediction. J. Am. Med. Inform. Assoc. 22, 109–120 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  13. Zhou, G., Li, S. & Xia, J. in Computational Methods and Data Analysis for Metabolomics (ed Li. S) 469–487 (Springer, 2020).

  14. Blatti, C. III et al. Knowledge-guided analysis of “omics” data using the KnowEnG cloud platform. PLoS Biol. 18, e3000583 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  15. Liu, T. et al. PaintOmics 4: new tools for the integrative analysis of multi-omics datasets supported by multiple pathway databases. Nucleic Acids Res. 50, W551–W559 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Zhou, G., Pang, Z., Lu, Y., Ewald, J. & Xia, J. OmicsNet 2.0: a web-based platform for multi-omics integration and network visual analytics. Nucleic Acids Res. 50, W527–W533 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Cantini, L. et al. Benchmarking joint multi-omics dimensionality reduction approaches for the study of cancer. Nat. Commun. 12, 124 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Picard, M., Scott-Boyer, M.-P., Bodein, A., Périn, O. & Droit, A. Integration strategies of multi-omics data for machine learning analysis. Comput. Struct. Biotechnol. J. 19, 3735–3746 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Argelaguet, R. et al. Multi-Omics Factor Analysis—a framework for unsupervised integration of multi-omics data sets. Mol. Syst. Biol. 14, e8124 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  20. Rohart, F., Gautier, B., Singh, A. & Lê Cao, K.-A. mixOmics: an R package for ‘omics feature selection and multiple data integration. PLoS Comput. Biol. 13, e1005752 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  21. McCabe, S. D., Lin, D.-Y. & Love, M. I. Consistency and overfitting of multi-omics methods on experimental data. Brief. Bioinform. 21, 1277–1284 (2019).

    Article  PubMed Central  Google Scholar 

  22. Pang, Z. et al. Using MetaboAnalyst 5.0 for LC–HRMS spectra processing, multi-omics integration and covariate adjustment of global metabolomics data. Nat. Protoc. 17, 1735–1761 (2022).

    Article  CAS  PubMed  Google Scholar 

  23. Xia, J. & Wishart, D. S. Web-based inference of biological patterns, functions and pathways from metabolomic data using MetaboAnalyst. Nat. Protoc. 6, 743–760 (2011).

    Article  CAS  PubMed  Google Scholar 

  24. Chong, J., Liu, P., Zhou, G. & Xia, J. Using MicrobiomeAnalyst for comprehensive statistical, functional, and meta-analysis of microbiome data. Nat. Protoc. 15, 799–821 (2020).

    Article  CAS  PubMed  Google Scholar 

  25. Xia, J., Gill, E. E. & Hancock, R. E. W. NetworkAnalyst for statistical, visual and network-based meta-analysis of gene expression data. Nat. Protoc. 10, 823–844 (2015).

    Article  CAS  PubMed  Google Scholar 

  26. Chang, L. & Xia, J. in Transcription Factor Regulatory Networks (eds Song, Q. & Tao, Z.) 185–204 (Springer, 2023).

  27. Chang, L., Zhou, G., Soufan, O. & Xia, J. miRNet 2.0: network-based visual analytics for miRNA functional analysis and systems biology. Nucleic Acids Res. 48, W244–W251 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Zhou, G., Ewald, J. & Xia, J. OmicsAnalyst: a comprehensive web-based platform for visual analytics of multi-omics data. Nucleic Acids Res. 49, W476–W482 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Zhou, G. & Xia, J. OmicsNet: a web-based tool for creation and visual analysis of biological networks in 3D space. Nucleic Acids Res. 46, W514–W522 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Wigger, L. et al. Multi-omics profiling of living human pancreatic islet donors reveals heterogeneous beta cell trajectories towards type 2 diabetes. Nat. Metab. 3, 1017–1031 (2021).

    Article  CAS  PubMed  Google Scholar 

  31. Conesa, A. et al. A survey of best practices for RNA-seq data analysis. Genome Biol. 17, 13 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  32. Pang, Z., Chong, J., Li, S. & Xia, J. MetaboAnalystR 3.0: toward an optimized workflow for global metabolomics. Metabolites 10, 186 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Röst, H. L. et al. OpenMS: a flexible open-source software platform for mass spectrometry data analysis. Nat. Methods 13, 741–748 (2016).

    Article  PubMed  Google Scholar 

  34. Schmid, R. et al. Integrative analysis of multimodal mass spectrometry data in MZmine 3. Nat. Biotechnol. 41, 447–449 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Tyanova, S., Temu, T. & Cox, J. The MaxQuant computational platform for mass spectrometry-based shotgun proteomics. Nat. Protoc. 11, 2301–2319 (2016).

    Article  CAS  PubMed  Google Scholar 

  36. Li, S., Siddiqa, A., Thapa, M., Chi, Y. & Zheng, S. Trackable and scalable LC–MS metabolomics data processing using asari. Nat. Commun. 14, 4113 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Smith, C. A., Want, E. J., O’Maille, G., Abagyan, R. & Siuzdak, G. XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. Anal. Chem. 78, 779–787 (2006).

    Article  CAS  PubMed  Google Scholar 

  38. Li, S. et al. Predicting network activity from high throughput metabolomics. PLoS Comput. Biol. 9, e1003123 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Xia, J. & Wishart, D. S. MSEA: a web-based tool to identify biologically meaningful patterns in quantitative metabolomic data. Nucleic Acids Res. 38, W71–W77 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Xia, J. & Wishart, D. S. MetPA: a web-based metabolomics tool for pathway analysis and visualization. Bioinformatics 26, 2342–2344 (2010).

    Article  CAS  PubMed  Google Scholar 

  42. Schadt, E. E. Molecular networks as sensors and drivers of common human diseases. Nature 461, 218–223 (2009).

    Article  CAS  PubMed  Google Scholar 

  43. Dianati, N. Unwinding the hairball graph: Pruning algorithms for weighted complex networks. Phys. Rev. E 93, 012304 (2016).

    Article  MathSciNet  PubMed  Google Scholar 

  44. Lovino, M. et al. A survey on data integration for multi-omics sample clustering. Neurocomputing 488, 494–508 (2022).

    Article  Google Scholar 

  45. Meng, C. et al. Dimension reduction techniques for the integrative analysis of multi-omics data. Brief. Bioinform. 17, 628–641 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome biol. 15, 1–21 (2014).

    Article  Google Scholar 

  47. Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47–e47 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  48. Ding, J. et al. Mergeomics 2.0: a web server for multi-omics data integration to elucidate disease networks and predict therapeutics. Nucleic Acids Res. 49, W375–W387 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Hernández-de-Diego, R. et al. PaintOmics 3: a web resource for the pathway analysis and visualization of multi-omics data. Nucleic Acids Res. 46, W503–W509 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  50. Kuo, T.-C., Tian, T.-F. & Tseng, Y. J. 3Omics: a web-based systems biology tool for analysis, integration and visualization of human transcriptomic, proteomic and metabolomic data. BMC Syst. Biol. 7, 64 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  51. Zoppi, J., Guillaume, J.-F., Neunlist, M. & Chaffron, S. MiBiOmics: an interactive web application for multi-omics data exploration and integration. BMC Bioinform. 22, 6 (2021).

    Article  Google Scholar 

  52. Mirza, B. et al. Machine learning and integrative analysis of biomedical big data. Genes 10, 87 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Asada, K. et al. Integrated analysis of whole genome and epigenome data using machine learning technology: toward the establishment of precision oncology. Front. Oncol. https://doi.org/10.3389/fonc.2021.666937 (2021).

  54. Cazaly, E. et al. Making sense of the epigenome using data integration approaches. Front. Pharmacol. https://doi.org/10.3389/fphar.2019.00126 (2019).

  55. Simovski, B. et al. GSuite HyperBrowser: integrative analysis of dataset collections across the genome and epigenome. GigaScience https://doi.org/10.1093/gigascience/gix032 (2017).

  56. Kang, M., Ko, E. & Mersha, T. B. A roadmap for multi-omics data integration using deep learning. Brief. Bioinform. https://doi.org/10.1093/bib/bbab454 (2022).

  57. Lee, J., Hyeon, D. Y. & Hwang, D. Single-cell multiomics: technologies and data analysis methods. Exp. Mol. Med. 52, 1428–1442 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Macaulay, I. C., Ponting, C. P. & Voet, T. Single-cell multiomics: multiple measurements from single cells. Trends Genet. 33, 155–168 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Tarazona, S. et al. Harmonization of quality metrics and power calculation in multi-omic studies. Nat. Commun. 11, 3092 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. de Souza, N. The ENCODE project. Nat. Methods 9, 1046–1046 (2012).

    Article  PubMed  Google Scholar 

  61. Lonsdale, J. et al. The Genotype–Tissue Expression (GTEx) project. Nat. Genet. 45, 580–585 (2013).

    Article  CAS  Google Scholar 

  62. Papatheodorou, I. et al. Expression Atlas: gene and protein expression across multiple studies and organisms. Nucleic Acids Res. 46, D246–D251 (2017).

    Article  PubMed Central  Google Scholar 

  63. Gertsman, I. & Barshop, B. A. Promises and pitfalls of untargeted metabolomics. J. Inherit. Metab. Dis. 41, 355–366 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Liu, P. et al. Ultrafast functional profiling of RNA-seq data for nonmodel organisms. Genome Res. 31, 713–720 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  65. Liu, P. et al. ExpressAnalyst: a unified platform for RNA-sequencing analysis in non-model species. Nat. Commun. 14, 2995 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Bourgon, R., Gentleman, R. & Huber, W. Independent filtering increases detection power for high-throughput experiments. Proc. Natl Acad. Sci. USA 107, 9546–9551 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Law, C. W., Chen, Y., Shi, W. & Smyth, G. K. voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol. 15, R29 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  68. Korotkevich, G. et al. Fast gene set enrichment analysis. Preprint at bioRxiv https://doi.org/10.1101/060012 (2021).

  69. Välikangas, T., Suomi, T. & Elo, L. L. A systematic evaluation of normalization methods in quantitative label-free proteomics. Brief. Bioinform. 19, 1–11 (2016).

    PubMed Central  Google Scholar 

  70. Szklarczyk, D. et al. The STRING database in 2017: quality-controlled protein–protein association networks, made broadly accessible. Nucleic Acids Res. 45, D362–d368 (2017).

    Article  CAS  PubMed  Google Scholar 

  71. Varlet, A. A. et al. Fine-tuning of actin dynamics by the HSPB8–BAG3 chaperone complex facilitates cytokinesis and contributes to its impact on cell division. Cell Stress Chaperones 22, 553–567 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Nevins, A. K. & Thurmond, D. C. Glucose regulates the cortical actin network through modulation of Cdc42 cycling to stimulate insulin secretion. Am. J. Physiol. Cell Physiol. 285, C698–C710 (2003).

    Article  CAS  PubMed  Google Scholar 

  73. Belosludtsev, K. N. et al. Alisporivir treatment alleviates mitochondrial dysfunction in the skeletal muscles of C57BL/6NCrl mice with high-fat diet/streptozotocin-induced diabetes mellitus. Int. J. Mol. Sci. https://doi.org/10.3390/ijms22179524 (2021).

  74. Neuhausen, S. L. et al. Genetic variation in insulin-like growth factor signaling genes and breast cancer risk among BRCA1 and BRCA2 carriers. Breast Cancer Res. 11, R76 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  75. Rosvall, M. & Bergstrom, C. T. Maps of random walks on complex networks reveal community structure. Proc. Natl Acad. Sci. USA 105, 1118–1123 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Brunk, E. et al. Recon3D enables a three-dimensional view of gene variation in human metabolism. Nat. Biotechnol. 36, 272–281 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Han, H. et al. TRRUST v2: an expanded reference database of human and mouse transcriptional regulatory interactions. Nucleic Acids Res. 46, D380–d386 (2018).

    Article  CAS  PubMed  Google Scholar 

  78. Heslegrave, A. J. et al. Leucine-sensitive hyperinsulinaemic hypoglycaemia in patients with loss of function mutations in 3-hydroxyacyl-CoA dehydrogenase. Orphanet. J. Rare Dis. 7, 25 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  79. Zhang, W. & Sang, Y. M. Genetic pathogenesis, diagnosis, and treatment of short-chain 3-hydroxyacyl-coenzyme A dehydrogenase hyperinsulinism. Orphanet. J. Rare Dis. 16, 467 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  80. Gerst, F. et al. The expression of aldolase B in islets is negatively associated with insulin secretion in humans. J. Clin. Endocrinol. Metab. 103, 4373–4383 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  81. Son, J. et al. Genetic and pharmacologic inhibition of ALDH1A3 as a treatment of β-cell failure. Nat. Commun. 14, 558 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Warde-Farley, D. et al. The GeneMANIA prediction server: biological network integration for gene prioritization and predicting gene function. Nucleic Acids Res. 38, W214–W220 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Sun, Y. V. & Hu, Y. J. Integrative analysis of multi-omics data for discovery and functional studies of complex human diseases. Adv. Genet. 93, 147–190 (2016).

    Article  CAS  PubMed  Google Scholar 

  84. Pinu, F. R. et al. Systems biology and multi-omics integration: viewpoints from the metabolomics research community. Metabolites 9, 76 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We thank the Canadian Institutes of Health Research, the Juvenile Diabetes Research Foundation of Canada, Diabetes Canada, the Natural Sciences and Engineering Research Council of Canada and the Canada Research Chairs Program for funding support.

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Authors and Affiliations

  1. Institute of Parasitology, McGill University, Montreal, Quebec, Canada

    Jessica D. Ewald, Guangyan Zhou & Jianguo Xia

  2. Department of Microbiology and Immunology, McGill University, Montreal, Quebec, Canada

    Yao Lu & Jianguo Xia

  3. Life Sciences Institute, Department of Cellular and Physiological Sciences, University of British Columbia, Vancouver, British Columbia, Canada

    Jelena Kolic & James D. Johnson

  4. Department of Pharmacology, University of Alberta, Edmonton, Alberta, Canada

    Cara Ellis & Patrick E. Macdonald

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Contributions

J.D.E. and J.X. prepared the manuscript. J.D.E., G.Z., Y.L. and J.X. contributed to the development of the tools (MetaboAnalyst, ExpressAnalyst, OmicsNet and OmicsAnalyst). J.K., C.E., J.D.J. and P.E.M. validated the tools and protocol steps, resulting in improvements to both based on their feedback. All authors read and approved the final manuscript.

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Correspondence to Jianguo Xia.

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J.D.E., G.Z. and J.X. own shares of OmicSquare Analytics Inc.

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Key references using this protocol

Zhou, G. et al. Nucleic Acids Res. 50, W527–W533 (2022): https://doi.org/10.1093/nar/gkac376

Zhou, G. et al. Nucleic Acids Res. 49, W476–W482 (2021): https://doi.org/10.1093/nar/gkab394

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Ewald, J.D., Zhou, G., Lu, Y. et al. Web-based multi-omics integration using the Analyst software suite. Nat Protoc (2024). https://doi.org/10.1038/s41596-023-00950-4

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