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Challenges and perspectives in computational deconvolution of genomics data

Nature Methods volume 21, pages 391–400 (2024)Cite this article

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Abstract

Deciphering cell-type heterogeneity is crucial for systematically understanding tissue homeostasis and its dysregulation in diseases. Computational deconvolution is an efficient approach for estimating cell-type abundances from a variety of omics data. Despite substantial methodological progress in computational deconvolution in recent years, challenges are still outstanding. Here we enlist four important challenges related to computational deconvolution: the quality of the reference data, generation of ground truth data, limitations of computational methodologies, and benchmarking design and implementation. Finally, we make recommendations on reference data generation, new directions of computational methodologies, and strategies to promote rigorous benchmarking.

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Fig. 1: Overview of computational deconvolution in various genomics data types and related challenges.
Fig. 2: Lack of agreement among different benchmark studies.
Fig. 3: Comparison among different spatial transcriptomics benchmark studies.

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Acknowledgements

We thank B. Nadel, S. Gupta, S. Mangul and A. Sharma for the discussions in the early stage of this project. A.E.T. is supported by NSFC grants 32370699 and 32170652. L.X.G. is supported by NIH/NIGMS R01 LM012373 and R01 LM012907 and NICHD R01 HD084633. Q.N. is supported by NHMRC investigator fellowship GNT2008928 and NHMRC project grant 2001514.

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Authors and Affiliations

  1. Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA

    Lana X. Garmire & Qianhui Huang

  2. Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA

    Yijun Li

  3. Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK

    Chuan Xu & Sarah A. Teichmann

  4. Pulmonary, Critical Care & Sleep Medicine, Yale University School of Medicine, New Haven, CT, USA

    Naftali Kaminski

  5. Molecular, Cell and Developmental Biology, University of California, Los Angeles, Los Angeles, CA, USA

    Matteo Pellegrini

  6. Institute for Molecular Bioscience, The University of Queensland and QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia

    Quan Nguyen

  7. CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China

    Andrew E. Teschendorff

  8. UCL Cancer Institute, University College London, London, UK

    Andrew E. Teschendorff

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Contributions

L.X.G. initiated and led the project. L.X.G., Y.L. and Q.H. wrote the initial manuscript, Y.L. and L.X.G. made the figures with the help of Q.N., L.X.G., Y.L., C.X., S.T., N.K., M.P., A.E.T. and Q.N. C.X. revised the manuscript.

Corresponding author

Correspondence to Lana X. Garmire.

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Competing interests

S.A.T. is a remunerated member of the scientific advisory boards of Qiagen, Foresite Labs and Element Biosciences, a co-founder and equity holder of TransitionBio and part-time employee of GlaxoSmithKline since January 2024. L.X.G. is a remunerated member of the scientific advisory boards of Simulations Plus.

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Nature Methods thanks the anonymous reviewers for their contribution to the peer review of this work. Primary Handling Editor: Lin Tang, in collaboration with the Nature Methods team.

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Garmire, L.X., Li, Y., Huang, Q. et al. Challenges and perspectives in computational deconvolution of genomics data. Nat Methods 21, 391–400 (2024). https://doi.org/10.1038/s41592-023-02166-6

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