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

  • Aristidis G. VrahatisEmail author
  • Sotiris K. Tasoulis
  • Ilias Maglogiannis
  • Vassilis P. Plagianakos
Chapter
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Part of the Studies in Computational Intelligence book series (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.

Keywords

Machine Learning Single-cell RNA-seq Clustering Classification Visualization 

Notes

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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© Springer-Verlag GmbH Germany, part of Springer Nature 2020

Authors and Affiliations

  • Aristidis G. Vrahatis
    • 1
    Email author
  • Sotiris K. Tasoulis
    • 1
  • Ilias Maglogiannis
    • 2
  • Vassilis P. Plagianakos
    • 1
  1. 1.Department of Computer Science and Biomedical InformaticsUniversity of ThessalyLamiaGreece
  2. 2.Department of Digital SystemsUniversity of PiraeusPiraeusGreece

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