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Recent Machine Learning Approaches for Single-Cell RNA-seq Data Analysis

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Advanced Computational Intelligence in Healthcare-7

Part of the book series: Studies in Computational Intelligence ((SCI,volume 891))

Abstract

DNA sequencing has become an extremely popular assay with researchers claiming that in the distant future, the DNA sequencing impact will be equal to the microscope impact. Single-cell RNA-seq (scRNA-seq) is an emerging DNA-sequencing technology with promising capabilities, but with major computational challenges due to the large-scaled generated data. Given the fact that sequencing costs are constantly decreasing, the volume and complexity of the data generated by these technologies will be constantly increasing. Toward this direction, major computational challenges are posed at the cell level, in particular, when focusing on the ultra-high dimensionality aspect of the scRNA-seq data. The main challenges are related to three pillars of machine learning (ML) analysis, classification, clustering, and visualization methods. Although there has been remarkable progress in ML methods for single-cell RNA-seq data analysis, numerous questions are still unresolved. This review records the state-of-the-art classification, clustering, and visualization methods tailored for single-cell transcriptomics data.

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Acknowledgements

This project has received funding from the Hellenic Foundation for Research and Innovation (HFRI) and the General Secretariat for Research and Technology (GSRT), under grant agreement No 1901.

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  1. Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, Greece

    Aristidis G. Vrahatis, Sotiris K. Tasoulis & Vassilis P. Plagianakos

  2. Department of Digital Systems, University of Piraeus, Piraeus, Greece

    Ilias Maglogiannis

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  1. Department of Digital Systems, University of Piraeus, Piraeus, Greece

    Ilias Maglogiannis

  2. Computer Information Systems, Missouri State University, Springfield, MO, USA

    Sheryl Brahnam

  3. School of Electrical and Information Engineering, University of South Australia, Adelaide, Australia

    Lakhmi C. Jain

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Vrahatis, A.G., Tasoulis, S.K., Maglogiannis, I., Plagianakos, V.P. (2020). Recent Machine Learning Approaches for Single-Cell RNA-seq Data Analysis. In: Maglogiannis, I., Brahnam, S., Jain, L. (eds) Advanced Computational Intelligence in Healthcare-7. Studies in Computational Intelligence, vol 891. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-61114-2_5

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