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Statistical Modelling and Machine Learning Principles for Bioinformatics Techniques, Tools, and Applications pp 289–317Cite as

Single-Cell Multiomics: Dissecting Cancer

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Part of the book series: Algorithms for Intelligent Systems ((AIS))

Abstract

Rapid progress in next generation sequencing technologies provided deeper insights into the mechanism underlying disease pathology. It has broadened our horizon to understand the cellular processes at individual cell level. Single-cell analysis allowed uncovering new dimensions that could track the trajectories of distant cell lineage in tumor development. In recent years, the popularity of single-cell omics gained utmost momentum. Our review focuses on the use of single-cell omics in cellular model of cancer and its clinical application. It also highlights the potential of using multiomics approach to understand the cellular heterogeneity at multiple layers. The data generated using single-cell multiomics revealed the key biological processes, mechanism of cellular heterogeneity, and resistance mechanism. The knowledge captured from the single-cell analysis facilitated a wider understanding of disease and in developing efficient treatment strategies.

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Authors and Affiliations

  1. Institute of Bioinformatics, International Technology Park, Bangalore, 560066, India

    Janani Sambath, Krishna Patel & Prashant Kumar

  2. Amrita School of Biotechnology, Amrita Vishwa Vidyapeetham, Kollam, 690525, India

    Krishna Patel

  3. Kokilaben Dhirubhai Ambani Hospital and Medical Research Institute, Mumbai, 400053, India

    Sewanti Limaye

  4. Manipal Academy of Higher Education (MAHE), Manipal, Karnataka, 576104, India

    Prashant Kumar

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  1. Janani Sambath
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  2. Krishna Patel
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  4. Prashant Kumar
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Correspondence to Prashant Kumar .

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Editors and Affiliations

  1. Department of Informatics, Computer Science & Engineering, National Institute of Technical Teachers Training and Research, Chandigarh, Chandigarh, India

    K. G. Srinivasa

  2. Department of Information Science and Engineering, Ramaiah Institute of Technology, Bengaluru, Karnataka, India

    G. M. Siddesh

  3. Department of Information Science and Engineering, Ramaiah Institute of Technology, Bengaluru, Karnataka, India

    S. R. Manisekhar

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Sambath, J., Patel, K., Limaye, S., Kumar, P. (2020). Single-Cell Multiomics: Dissecting Cancer. In: Srinivasa, K., Siddesh, G., Manisekhar, S. (eds) Statistical Modelling and Machine Learning Principles for Bioinformatics Techniques, Tools, and Applications. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-15-2445-5_14

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