Epigenetics studies inheritable and reversible modifications of DNA that allow cells to control gene expression throughout their development and in response to environmental conditions. In computational epigenomics, machine learning is applied to study various epigenetic mechanisms genome wide. Its aim is to expand our understanding of cell differentiation, that is their specialization, in health and disease. Thus far, most efforts focus on understanding the functional encoding of the genome and on unraveling cell-type heterogeneity. Here, we provide an overview of state-of-the-art computational methods and their underlying statistical concepts, which range from matrix factorization and regularized linear regression to deep learning methods. We further show how the rise of single-cell technology leads to new computational challenges and creates opportunities to further our understanding of epigenetic regulation.
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This work was supported by the German Federal Ministry of Education and Research (BMBF) within the framework of the e:Med research and funding concept (grant Sys_CARE [01ZX1908A]) to M.L. and J.B.; VILLUM Young Investor Grant 13154 and European Union’s Horizon 2020 project RepoTrial 777111 to J.B.; the Bavarian State Ministry of Science and the Arts as part of the Bavarian Research Institute for Digital Transformation (bidt) to O.L.; BMBF project de.NBI-epi (031L0101D) to M.S. and J.W.; DZHK (German Centre for Cardiovascular Research, 81Z0200101), the Cardio-Pulmonary Institute (CPI) EXC 2026 to M.H.S. and the Agency for Science, Technology and Research, Singapore (Enabling Data Analytics Technologies for Next-Generation Pathology from 3D Transcriptomics – SERC Data Analytics, 1727600056) to F.S.
The authors declare no competing interests.
Peer review information Nature Computational Science thanks Lukas Chavez and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Fernando Chirigati was the primary editor on this Review and managed its editorial process and peer review in collaboration with the rest of the editorial team.
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Scherer, M., Schmidt, F., Lazareva, O. et al. Machine learning for deciphering cell heterogeneity and gene regulation. Nat Comput Sci 1, 183–191 (2021). https://doi.org/10.1038/s43588-021-00038-7
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