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  • Review Article
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Machine learning methods to model multicellular complexity and tissue specificity

Nature Reviews Materials volume 6, pages 717–729 (2021)Cite this article

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Abstract

Experimental approaches to study tissue specificity enable insight into the nature and organization of the cell types and tissues that constitute complex multicellular organisms. Machine learning provides a powerful tool to investigate and interpret tissue-specific experimental data. In this Review, we first provide a brief introduction to key single-cell and whole-tissue approaches that allow investigation of tissue specificity and then highlight two classes of machine-learning-based methods, which can be applied to analyse, model and interpret these experimental data. Deep learning methods can predict tissue-dependent effects of individual mutations on gene expression, alternative splicing and disease phenotypes. Network-based approaches can capture relationships between biomolecules, integrate large heterogeneous data compendia to model molecular circuits and identify tissue-specific functional relationships and regulatory connections. We conclude with an outlook to future possibilities in examining multicellular complexity by combining high-resolution, large-scale multiomics data sets and interpretable machine learning models.

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Fig. 1: Decoding multicellular complexity.
Fig. 2: Machine learning approaches.
Fig. 3: Decoding the tissue-specific impact of sequence variants.
Fig. 4: The GIANT framework for predicting tissue-specific functional networks.
Fig. 5: Applications of tissue-specific networks.

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Acknowledgements

Figures created with Biorender.com. ADAR icon by Emw – Own work, CC BY-SA 3.0, https://commons.wikimedia.org/w/index.php?curid=8761779. This work is supported by NIH/NIDDK grants U24DK100845, UGDK114907 and U2CDK114886 and NIH grant UH3TR002158 to O.G.T. We thank C. Theesfeld for helpful discussion and comments on the manuscript.

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Authors and Affiliations

  1. Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, USA

    Rachel S. G. Sealfon, Aaron K. Wong & Olga G. Troyanskaya

  2. Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA

    Olga G. Troyanskaya

  3. Department of Computer Science, Princeton University, Princeton, NJ, USA

    Olga G. Troyanskaya

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R.S.G.S., A.K.W. and O.G.T. drafted and edited the manuscript.

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Correspondence to Olga G. Troyanskaya.

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Related links

ConsensusPathDB: http://cpdb.molgen.mpg.de/

Critical Assessment of protein Structure Prediction (CASP): https://predictioncenter.org/

DREAM Challenges: https://dreamchallenges.org/

Genome-scale Integrated Analysis of gene Networks in Tissues (GIANT): http://giant-v2.princeton.edu

GWAS Catalog: https://www.ebi.ac.uk/gwas/

Search Tool for the Retrieval of Interacting Genes/Proteins (STRING): https://string-db.org/

UK Biobank: https://www.ukbiobank.ac.uk

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Sealfon, R.S.G., Wong, A.K. & Troyanskaya, O.G. Machine learning methods to model multicellular complexity and tissue specificity. Nat Rev Mater 6, 717–729 (2021). https://doi.org/10.1038/s41578-021-00339-3

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