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Undisclosed, unmet and neglected challenges in multi-omics studies

Abstract

Multi-omics approaches have become a reality in both large genomics projects and small laboratories. However, the multi-omics research community still faces a number of issues that have either not been sufficiently discussed or for which current solutions are still limited. In this Perspective, we elaborate on these limitations and suggest points of attention for future research. We finally discuss new opportunities and challenges brought to the field by the rapid development of single-cell high-throughput molecular technologies.

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Fig. 1: Schematic representation of analysis goals in multi-omics studies.
Fig. 2: Challenges and opportunities in multi-omics data-integration.
Fig. 3: Comparison of the properties of omics data types.

Data availability

The data used for Fig. 3 were taken from the publicly available STATegra dataset125. Pre-processed values used to generate graphs are provided together with the code.

Code availability

The code and data used to generate Fig. 3 are available on GitHub at https://github.com/ConesaLab/Perspective_Multi-Omics_Integration.

References

  1. 1.

    Fan, T. W. M., Bandura, L. L., Higashi, R. M. & Lane, A. N. Metabolomics-edited transcriptomics analysis of Se anticancer action in human lung cancer cells. Metabolomics 1, 325–339 (2005).

    Google Scholar 

  2. 2.

    Panguluri, S. K. et al. Genomic profiling of messenger RNAs and microRNAs reveals potential mechanisms of TWEAK-induced skeletal muscle wasting in mice. PLoS ONE 5, e8760 (2010).

    Google Scholar 

  3. 3.

    Song, L. et al. Open chromatin defined by DNaseI and FAIRE identifies regulatory elements that shape cell-type identity. Genome Res. 21, 1757–1767 (2011).

    Google Scholar 

  4. 4.

    Kim, S., Jhong, J.-H., Lee, J. & Koo, J.-Y. Meta-analytic support vector machine for integrating multiple omics data. BioData Min. 10, 2 (2017).

    Google Scholar 

  5. 5.

    Vaske, C. J. et al. Inference of patient-specific pathway activities from multi-dimensional cancer genomics data using PARADIGM. Bioinformatics 26, i237–i245 (2010).

    Google Scholar 

  6. 6.

    Mo, Q. et al. A fully Bayesian latent variable model for integrative clustering analysis of multi-type omics data. Biostatistics 19, 71–86 (2017).

    MathSciNet  Google Scholar 

  7. 7.

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

    Google Scholar 

  8. 8.

    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).

    Google Scholar 

  9. 9.

    Zhang, L. et al. Deep learning-based multi-omics data integration reveals two prognostic subtypes in high-risk neuroblastoma. Front. Genet. 9, 477 (2018).

    Google Scholar 

  10. 10.

    Ma, T. & Zhang, A. Integrate multi-omics data with biological interaction networks using multi-view factorization autoencoder (MAE). BMC Genomics 20, 944 (2019).

    Google Scholar 

  11. 11.

    Huang, Z. et al. SALMON: survival analysis learning with multi-omics neural networks on breast cancer. Front. Genet. 10, 166 (2019).

    Google Scholar 

  12. 12.

    Bersanelli, M. et al. Methods for the integration of multi-omics data: mathematical aspects. BMC Bioinformatics 17, 15 (2016).

    Google Scholar 

  13. 13.

    De Bin, R., Boulesteix, A.-L., Benner, A., Becker, N. & Sauerbrei, W. Combining clinical and molecular data in regression prediction models: insights from a simulation study. Brief. Bioinform. 21, 1904–1919 (2020).

    Google Scholar 

  14. 14.

    Pierre-Jean, M., Deleuze, J.-F., Le Floch, E. & Mauger, F. Clustering and variable selection evaluation of 13 unsupervised methods for multi-omics data integration. Brief. Bioinform. 21, 2011–2030 (2020).

    Google Scholar 

  15. 15.

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

    Google Scholar 

  16. 16.

    Buescher, J. M. & Driggers, E. M. Integration of omics: more than the sum of its parts. Cancer Metab. 4, 4 (2016).

    Google Scholar 

  17. 17.

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

    Google Scholar 

  18. 18.

    Kristensen, V. N. et al. Principles and methods of integrative genomic analyses in cancer. Nat. Rev. Cancer 14, 299–313 (2014).

    Google Scholar 

  19. 19.

    Sathyanarayanan, A. et al. A comparative study of multi-omics integration tools for cancer driver gene identification and tumour subtyping. Brief. Bioinform. 21, 1920–1936 (2020).

    Google Scholar 

  20. 20.

    Zeng, H. et al. Integrative radiogenomics analysis for predicting molecular features and survival in clear cell renal cell carcinoma. Aging 13, 9960–9975 (2021).

    Google Scholar 

  21. 21.

    Kirienko, M. et al. Radiomics and gene expression profile to characterise the disease and predict outcome in patients with lung cancer. Eur. J. Nucl. Med. Mol. Imaging https://doi.org/10.1007/s00259-021-05371-7 (2021).

  22. 22.

    Zielinski, J. M., Luke, J. J., Guglietta, S. & Krieg, C. High throughput multi-omics approaches for clinical trial evaluation and drug discovery. Front. Immunol. 12, 590742 (2021).

    Google Scholar 

  23. 23.

    Houle, D., Govindaraju, D. R. & Omholt, S. Phenomics: the next challenge. Nat. Rev. Genet. 11, 855–866 (2010).

    Google Scholar 

  24. 24.

    van Bezouw, R. F. H. M., Keurentjes, J. J. B., Harbinson, J. & Aarts, M. G. M. Converging phenomics and genomics to study natural variation in plant photosynthetic efficiency. Plant J. Cell Mol. Biol. 97, 112–133 (2019).

    Google Scholar 

  25. 25.

    Zhu, R., Zhao, Q., Zhao, H. & Ma, S. Integrating multidimensional omics data for cancer outcome. Biostatistics 17, 605–618 (2016).

    MathSciNet  Google Scholar 

  26. 26.

    Balzano-Nogueira, L. et al. Integrative analyses of TEDDY omics data reveal lipid metabolism abnormalities, increased intracellular ROS and heightened inflammation prior to autoimmunity for type 1 diabetes. Genome Biol. 22, 39 (2021).

    Google Scholar 

  27. 27.

    Shen, R., Olshen, A. B. & Ladanyi, M. Integrative clustering of multiple genomic data types using a joint latent variable model with application to breast and lung cancer subtype analysis. Bioinformatics 25, 2906–2912 (2009).

    Google Scholar 

  28. 28.

    Yener, B. et al. Multiway modeling and analysis in stem cell systems biology. BMC Syst. Biol. 2, 63 (2008).

    Google Scholar 

  29. 29.

    Conesa, A., Prats-Montalbán, J. M., Tarazona, S., Nueda, M. J. & Ferrer, A. A multiway approach to data integration in systems biology based on Tucker3 and N-PLS. Chemom. Intell. Lab. Syst. 104, 101–111 (2010).

    Google Scholar 

  30. 30.

    Meng, C., Kuster, B., Culhane, A. C. & Gholami, A. M. A multivariate approach to the integration of multi-omics datasets. BMC Bioinformatics 15, 162 (2014).

    Google Scholar 

  31. 31.

    van der Kloet, F. M., Sebastián-León, P., Conesa, A., Smilde, A. K. & Westerhuis, J. A. Separating common from distinctive variation. BMC Bioinformatics 17, 195 (2016).

    Google Scholar 

  32. 32.

    O’Connell, M. J. & Lock, E. F. R.JIVE for exploration of multi-source molecular data. Bioinformatics 32, 2877–2879 (2016).

    Google Scholar 

  33. 33.

    Bouhaddani, S. E. et al. Integrating omics datasets with the OmicsPLS package. BMC Bioinformatics 19, 371 (2018).

    Google Scholar 

  34. 34.

    Planell, N. et al. STATegra: multi-omics data integration—a conceptual scheme with a bioinformatics pipeline. Front. Genet. 12, 143 (2021).

    Google Scholar 

  35. 35.

    Boulesteix, A.-L., De Bin, R., Jiang, X. & Fuchs, M. IPF-LASSO: integrative L(1)-penalized regression with penalty factors for prediction based on multi-omics data. Comput. Math. Methods Med. 2017, 7691937 (2017).

    MATH  Google Scholar 

  36. 36.

    Kennedy, E. M. et al. An integrated -omics analysis of the epigenetic landscape of gene expression in human blood cells. BMC Genomics 19, 476 (2018).

    Google Scholar 

  37. 37.

    Wu, M.-Y. et al. Regularized logistic regression with network-based pairwise interaction for biomarker identification in breast cancer. BMC Bioinformatics 17, 108 (2016).

    Google Scholar 

  38. 38.

    Wu, C. et al. A selective review of multi-level omics data integration using variable selection. High Throughput 8, 4 (2019).

    Google Scholar 

  39. 39.

    Lagani, V., Kortas, G. & Tsamardinos, I. Biomarker signature identification in ‘omics’ data with multi-class outcome. Comput. Struct. Biotechnol. J. 6, e201303004 (2013).

    Google Scholar 

  40. 40.

    Le, D.-H. Machine learning-based approaches for disease gene prediction. Brief. Funct. Genomics 19, 350–363 (2020).

    Google Scholar 

  41. 41.

    Fang, H., Huang, C., Zhao, H. & Deng, M. CCLasso: correlation inference for compositional data through Lasso. Bioinformatics 31, 3172–3180 (2015).

    Google Scholar 

  42. 42.

    Klau, S., Jurinovic, V., Hornung, R., Herold, T. & Boulesteix, A.-L. Priority-Lasso: a simple hierarchical approach to the prediction of clinical outcome using multi-omics data. BMC Bioinformatics 19, 322 (2018).

    Google Scholar 

  43. 43.

    Li, J., Lu, Q. & Wen, Y. Multi-kernel linear mixed model with adaptive lasso for prediction analysis on high-dimensional multi-omics data. Bioinformatics 36, 1785–1794 (2020).

    Google Scholar 

  44. 44.

    Park, H., Niida, A., Miyano, S. & Imoto, S. Sparse overlapping group lasso for integrative multi-omics analysis. J. Comput. Biol. 22, 73–84 (2015).

    MathSciNet  Google Scholar 

  45. 45.

    Singh, A. et al. DIABLO: an integrative approach for identifying key molecular drivers from multi-omics assays. Bioinformatics 35, 3055–3062 (2019).

    Google Scholar 

  46. 46.

    Patel-Murray, N. L. et al. A multi-omics interpretable machine learning model reveals modes of action of small molecules. Sci. Rep. 10, 954 (2020).

    Google Scholar 

  47. 47.

    Gusev, A. et al. Transcriptome-wide association study of schizophrenia and chromatin activity yields mechanistic disease insights. Nat. Genet. 50, 538–548 (2018).

    Google Scholar 

  48. 48.

    Rubio, T. et al. Multi-omic analysis unveils biological pathways in peripheral immune system associated to minimal hepatic encephalopathy appearance in cirrhotic patients. Sci. Rep. 11, 1907 (2021).

    Google Scholar 

  49. 49.

    Cai, X., Bazerque, J. A. & Giannakis, G. B. Inference of gene regulatory networks with sparse structural equation models exploiting genetic perturbations. PLoS Comput. Biol. 9, e1003068 (2013).

    Google Scholar 

  50. 50.

    Oberhardt, M. A., Chavali, A. K. & Papin, J. A. Flux balance analysis: interrogating genome-scale metabolic networks. Methods Mol. Biol. 500, 61–80 (2009).

    Google Scholar 

  51. 51.

    Orth, J. D., Thiele, I. & Palsson, B. Ø. What is flux balance analysis? Nat. Biotechnol. 28, 245–248 (2010).

    Google Scholar 

  52. 52.

    Covert, M. W., Schilling, C. H. & Palsson, B. Regulation of gene expression in flux balance models of metabolism. J. Theor. Biol. 213, 73–88 (2001).

    Google Scholar 

  53. 53.

    Tzika, E., Dreker, T. & Imhof, A. Epigenetics and metabolism in health and disease. Front. Genet. 9, 361 (2018).

    Google Scholar 

  54. 54.

    Siebert, J. C. et al. CANTARE: finding and visualizing network-based multi-omic predictive models. BMC Bioinformatics 22, 80 (2021).

    Google Scholar 

  55. 55.

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

    Google Scholar 

  56. 56.

    Soerensen, M. et al. A genome-wide integrative association study of DNA methylation and gene expression data and later life cognitive functioning in monozygotic twins. Front. Neurosci. 14, https://doi.org/10.3389/fnins.2020.00233 (2020).

  57. 57.

    Dai, Y., Pei, G., Zhao, Z. & Jia, P. A convergent study of genetic variants associated with Crohn’s disease: evidence from GWAS, gene expression, methylation, eQTL and TWAS. Front. Genet. 10, https://doi.org/10.3389/fgene.2019.00318 (2019).

  58. 58.

    Karathanasis, N., Tsamardinos, I. & Lagani, V. omicsNPC: applying the non-parametric combination methodology to the integrative analysis of heterogeneous omics data. PLoS ONE 11, e0165545 (2016).

    Google Scholar 

  59. 59.

    Garcia-Alcalde, F., Garcia-Lopez, F., Dopazo, J. & Conesa, A. Paintomics: a web based tool for the joint visualization of transcriptomics and metabolomics data. Bioinformatics 27, 137–139 (2011).

    Google Scholar 

  60. 60.

    Voillet, V., Besse, P., Liaubet, L., San Cristobal, M. & González, I. Handling missing rows in multi-omics data integration: multiple imputation in multiple factor analysis framework. BMC Bioinformatics 17, 402 (2016).

    Google Scholar 

  61. 61.

    Kuo, R. I. et al. Normalized long read RNA sequencing in chicken reveals transcriptome complexity similar to human. BMC Genomics 18, 323 (2017).

    Google Scholar 

  62. 62.

    Conesa, A. & Beck, S. Making multi-omics data accessible to researchers. Sci. Data 6, 251 (2019).

    Google Scholar 

  63. 63.

    Dong, X. et al. TOBMI: trans-omics block missing data imputation using a k-nearest neighbor weighted approach. Bioinformatics 35, 1278–1283 (2019).

    Google Scholar 

  64. 64.

    Zhou, X., Chai, H., Zhao, H., Luo, C.-H. & Yang, Y. Imputing missing RNA-sequencing data from DNA methylation by using a transfer learning–based neural network. GigaScience 9, https://doi.org/10.1093/gigascience/giaa076 (2020).

  65. 65.

    Ugidos, M., Tarazona, S., Prats-Montalbán, J. M., Ferrer, A. & Conesa, A. MultiBaC: a strategy to remove batch effects between different omic data types. Stat. Methods Med. Res. 29, 2851–2864 (2020).

    MathSciNet  Google Scholar 

  66. 66.

    Messer, K., Vaida, F. & Hogan, C. Robust analysis of biomarker data with informative missingness using a two-stage hypothesis test in an HIV treatment interruption trial: AIEDRP AIN503/ACTG A5217. Contemp. Clin. Trials 27, 506–517 (2006).

    Google Scholar 

  67. 67.

    Hong, M.-G., Pawitan, Y., Magnusson, P. K. E. & Prince, J. A. Strategies and issues in the detection of pathway enrichment in genome-wide association studies. Hum. Genet. 126, 289–301 (2009).

    Google Scholar 

  68. 68.

    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).

    Google Scholar 

  69. 69.

    Arneson, D., Bhattacharya, A., Shu, L., Mäkinen, V.-P. & Yang, X. Mergeomics: a web server for identifying pathological pathways, networks, and key regulators via multidimensional data integration. BMC Genomics 17, 722 (2016).

    Google Scholar 

  70. 70.

    Welch, R. P. et al. ChIP-enrich: gene set enrichment testing for ChIP-seq data. Nucleic Acids Res. 42, e105 (2014).

    Google Scholar 

  71. 71.

    Canzler, S. & Hackermüller, J. multiGSEA: a GSEA-based pathway enrichment analysis for multi-omics data. BMC Bioinformatics 21, 561 (2020).

    Google Scholar 

  72. 72.

    Long, Y., Lu, M., Cheng, T., Zhan, X. & Zhan, X. Multiomics-based signaling pathway network alterations in human non-functional pituitary adenomas. Front. Endocrinol. 10, https://doi.org/10.3389/fendo.2019.00835 (2019).

  73. 73.

    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).

    Google Scholar 

  74. 74.

    Sakurai, N. et al. KaPPA-View4: a metabolic pathway database for representation and analysis of correlation networks of gene co-expression and metabolite co-accumulation and omics data. Nucleic Acids Res. 39, D677–D684 (2011).

    Google Scholar 

  75. 75.

    Su, G., Morris, J. H., Demchak, B. & Bader, G. D. Biological network exploration with Cytoscape 3. Curr. Protoc. Bioinformatics 47, 8.13.11–18.13.24 (2014).

    Google Scholar 

  76. 76.

    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, https://doi.org/10.1186/1752-0509-7-64 (2013).

  77. 77.

    Miller, J. J. Graph database applications and concepts with Neo4j. In Proc. Southern Association for Information Systems Conference (AIS, 2013).

  78. 78.

    Yoon, B.-H., Kim, S.-K. & Kim, S.-Y. Use of graph database for the integration of heterogeneous biological data. Genomics Inform. 15, 19–27 (2017).

    Google Scholar 

  79. 79.

    Consortium, T. I. Hi. R. N. The Integrative Human Microbiome Project: dynamic analysis of microbiome-host omics profiles during periods of human health and disease. Cell Host Microbe 16, 276–289 (2014).

    Google Scholar 

  80. 80.

    ICGC Data Portal (The International Cancer Genome Consortium, 2021); https://dcc.icgc.org/

  81. 81.

    Human Microbiome Project Data Portal (Human Microbiome Project, 2021); https://portal.hmpdacc.org/

  82. 82.

    Wilkinson, M. D. et al. The FAIR guiding principles for scientific data management and stewardship. Sci. Data 3, 160018 (2016).

    Google Scholar 

  83. 83.

    Kodama, Y., Shumway, M. & Leinonen, R. The Sequence Read Archive: explosive growth of sequencing data. Nucleic Acids Res. 40, D54–D56 (2012).

    Google Scholar 

  84. 84.

    Tryka, K. A. et al. NCBI’s Database of Genotypes and Phenotypes: dbGaP. Nucleic Acids Res. 42, D975–D979 (2014).

    Google Scholar 

  85. 85.

    Lappalainen, I. et al. The European Genome-phenome Archive of human data consented for biomedical research. Nat. Genet. 47, 692–695 (2015).

    Google Scholar 

  86. 86.

    Haug, K. et al. MetaboLights: a resource evolving in response to the needs of its scientific community. Nucleic Acids Res. 48, D440–D444 (2020).

    Google Scholar 

  87. 87.

    Deutsch, E. W. et al. The ProteomeXchange consortium in 2017: supporting the cultural change in proteomics public data deposition. Nucleic Acids Res. 45, D1100–D1106 (2017).

    Google Scholar 

  88. 88.

    Byrd, J. B., Greene, A. C., Prasad, D. V., Jiang, X. & Greene, C. S. Responsible, practical genomic data sharing that accelerates research. Nat. Rev. Genet. 21, 615–629 (2020).

    Google Scholar 

  89. 89.

    Hernandez-de-Diego, R. et al. STATegra EMS: an experiment management system for complex next-generation omics experiments. BMC Syst. Biol. 8, S9 (2014).

    Google Scholar 

  90. 90.

    Lin, K. et al. MADMAX—management and analysis database for multiple ~omics experiments. J. Integr. Bioinform. 8, 59–74 (2011).

    Google Scholar 

  91. 91.

    Venco, F., Vaskin, Y., Ceol, A. & Muller, H. SMITH: a LIMS for handling next-generation sequencing workflows. BMC Bioinformatics 15, S3 (2014).

    Google Scholar 

  92. 92.

    Perez-Riverol, Y. et al. Discovering and linking public omics data sets using the Omics Discovery index. Nat. Biotechnol. 35, 406–409 (2017).

    Google Scholar 

  93. 93.

    Chervitz, S. A. et al. in Bioinformatics for Omics Data: Methods and Protocols (ed. Mayer, B.) 31–69 (Humana Press, 2011).

  94. 94.

    Karczewski, K. J. & Snyder, M. P. Integrative omics for health and disease. Nat. Rev. Genet. 19, 299–310 (2018).

    Google Scholar 

  95. 95.

    van Karnebeek, C. D. M. et al. The role of the clinician in the multi-omics era: are you ready? J. Inherit. Metab. Dis. 41, 571–582 (2018).

    Google Scholar 

  96. 96.

    Angione, C. Human systems biology and metabolic modelling: a review—from disease metabolism to precision medicine. Biomed. Res. Int. 2019, 8304260 (2019).

    Google Scholar 

  97. 97.

    Hériché, J.-K., Alexander, S. & Ellenberg, J. Integrating imaging and omics: computational methods and challenges. Annu. Rev. Biomed. Data Sci. 2, 175–197 (2019).

    Google Scholar 

  98. 98.

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

    Google Scholar 

  99. 99.

    Stein, L. D. The case for cloud computing in genome informatics. Genome Biol. 11, 207 (2010).

    Google Scholar 

  100. 100.

    Oh, M., Park, S., Kim, S. & Chae, H. Machine learning-based analysis of multi-omics data on the cloud for investigating gene regulations. Brief. Bioinform. 22, 66–76 (2020).

    Google Scholar 

  101. 101.

    Solomonik, E., Carson, E., Knight, N. & Demmel, J. Trade-offs between synchronization, communication, and computation in parallel linear algebra computations. ACM Trans. Parallel Comput. 3, 1–47 (2016).

    Google Scholar 

  102. 102.

    Berger, B., Peng, J. & Singh, M. Computational solutions for omics data. Nat. Rev. Genet. 14, 333–346 (2013).

    Google Scholar 

  103. 103.

    Alyass, A., Turcotte, M. & Meyre, D. From big data analysis to personalized medicine for all: challenges and opportunities. BMC Med. Genet. 8, 33 (2015).

    Google Scholar 

  104. 104.

    Chen, X.-W. & Lin, X. Big data deep learning: challenges and perspectives. IEEE Access 2, 514–525 (2014).

    Google Scholar 

  105. 105.

    Fan, J., Slowikowski, K. & Zhang, F. Single-cell transcriptomics in cancer: computational challenges and opportunities. Exp. Mol. Med. 52, 1452–1465 (2020).

    Google Scholar 

  106. 106.

    Armand, E. J., Li, J., Xie, F., Luo, C. & Mukamel, E. A. Single-cell sequencing of brain cell transcriptomes and epigenomes. Neuron 109, 11–26 (2021).

    Google Scholar 

  107. 107.

    Zhu, C., Preissl, S. & Ren, B. Single-cell multimodal omics: the power of many. Nat. Methods 17, 11–14 (2020).

    Google Scholar 

  108. 108.

    Forcato, M., Romano, O. & Bicciato, S. Computational methods for the integrative analysis of single-cell data. Brief. Bioinform. 22, 20–29 (2021).

    Google Scholar 

  109. 109.

    Adamson, B. et al. A multiplexed single-cell CRISPR screening platform enables systematic dissection of the unfolded protein response. Cell 167, 1867–1882.e1821 (2016).

    Google Scholar 

  110. 110.

    Wang, X. et al. Three-dimensional intact-tissue sequencing of single-cell transcriptional states. Science 361, eaat5691 (2018).

    Google Scholar 

  111. 111.

    Raj, B. et al. Simultaneous single-cell profiling of lineages and cell types in the vertebrate brain. Nat. Biotechnol. 36, 442–450 (2018).

    Google Scholar 

  112. 112.

    Trapnell, C. & Cacchiarelli, D. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat. Biotechnol. 32, 381–386 (2014).

    Google Scholar 

  113. 113.

    Stoeckius, M. et al. Simultaneous epitope and transcriptome measurement in single cells. Nat. Methods 14, 865–868 (2017).

    Google Scholar 

  114. 114.

    Darmanis, S. et al. Simultaneous multiplexed measurement of RNA and proteins in single cells. Cell Rep. 14, 380–389 (2016).

    Google Scholar 

  115. 115.

    Peterson, V. M. et al. Multiplexed quantification of proteins and transcripts in single cells. Nat. Biotechnol. 35, 936–939 (2017).

    Google Scholar 

  116. 116.

    Chen, H. et al. Assessment of computational methods for the analysis of single-cell ATAC-seq data. Genome Biol. 20, 241 (2019).

    Google Scholar 

  117. 117.

    Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902 (2019).

    Google Scholar 

  118. 118.

    Welch, J. D., Hartemink, A. J. & Prins, J. F. MATCHER: manifold alignment reveals correspondence between single cell transcriptome and epigenome dynamics. Genome Biol. 18, 138 (2017).

    Google Scholar 

  119. 119.

    Campbell, K. R. et al. clonealign: statistical integration of independent single-cell RNA and DNA sequencing data from human cancers. Genome Biol. 20, 54 (2019).

    Google Scholar 

  120. 120.

    Fan, J. et al. Linking transcriptional and genetic tumor heterogeneity through allele analysis of single-cell RNA-seq data. Genome Res. 28, 1217–1227 (2018).

    Google Scholar 

  121. 121.

    Van Der Wijst, M. G. P. et al. Single-cell RNA sequencing identifies celltype-specific cis-eQTLs and co-expression QTLs. Nat. Genet. 50, 493–497 (2018).

    Google Scholar 

  122. 122.

    La Manno, G. et al. RNA velocity of single cells. Nature 560, 494–498 (2018).

    Google Scholar 

  123. 123.

    Bray, M.-A. et al. Cell Painting, a high-content image-based assay for morphological profiling using multiplexed fluorescent dyes. Nat. Protoc. 11, 1757–1774 (2016).

    Google Scholar 

  124. 124.

    Sedgewick, A. J., Benz, S. C., Rabizadeh, S., Soon-Shiong, P. & Vaske, C. J. Learning subgroup-specific regulatory interactions and regulator independence with PARADIGM. Bioinformatics 29, i62–i70 (2013).

    Google Scholar 

  125. 125.

    Gomez-Cabrero, D. et al. STATegra, a comprehensive multi-omics dataset of B-cell differentiation in mouse. Sci. Data 6, 256 (2019).

    Google Scholar 

  126. 126.

    Hutter, C. & Zenklusen, J. C. The Cancer Genome Atlas: creating lasting value beyond its data. Cell 173, 283–285 (2018).

    Google Scholar 

  127. 127.

    Ghandi, M. et al. Next-generation characterization of the Cancer Cell Line Encyclopedia. Nature 569, 503–508 (2019).

    Google Scholar 

  128. 128.

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

    Google Scholar 

  129. 129.

    Taliun, D. et al. Sequencing of 53,831 diverse genomes from the NHLBI TOPMed Program. Nature 590, 290–299 (2021).

    Google Scholar 

  130. 130.

    Moore, J. E. et al. Expanded encyclopaedias of DNA elements in the human and mouse genomes. Nature 583, 699–710 (2020).

    Google Scholar 

  131. 131.

    Almeida, A. et al. A new genomic blueprint of the human gut microbiota. Nature 568, 499–504 (2019).

    Google Scholar 

  132. 132.

    Mergner, J. et al. Mass-spectrometry-based draft of the Arabidopsis proteome. Nature 579, 409–414 (2020).

    Google Scholar 

  133. 133.

    O’Connor, T. R., Dyreson, C. & Wyrick, J. J. Athena: a resource for rapid visualization and systematic analysis of Arabidopsis promoter sequences. Bioinformatics 21, 4411–4413 (2005).

    Google Scholar 

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Acknowledgements

This work has been funded by the Spanish Ministry of Science and Innovation with grant number BES-2016-076994 to A.A.-L.

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A.C. drafted the structure of the manuscript and integrated author contributions. A.A.-L., S.T. and A.C. drafted the manuscript, reviewed the literature, contributed to figures and approved the final version of the manuscript.

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Correspondence to Ana Conesa.

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Peer review information Nature Computational Science thanks Casey Greene and Terry Speed for their contribution to the peer review of this work. Handling editor: Fernando Chirigati, in collaboration with the Nature Computational Science team.

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Tarazona, S., Arzalluz-Luque, A. & Conesa, A. Undisclosed, unmet and neglected challenges in multi-omics studies. Nat Comput Sci 1, 395–402 (2021). https://doi.org/10.1038/s43588-021-00086-z

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