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Single-Cell Transcriptome Profiling

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Deep Sequencing Data Analysis

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2243))

Abstract

Over the last decade, single cell RNA sequencing (scRNAseq) became an increasingly viable solution for analyzing cellular heterogeneity and cell-specific expression differences. While not as mature or fully realized as bulk sequencing, newly developed computational methods offer a solution to the challenges of scRNAseq data analysis, providing previously inaccessible biological insight at unprecedented levels of detail. Here, we go over the inherent challenges of single-cell data analysis and the computational methods used to overcome them. We cover current and future applications of scRNAseq in research of cellular dynamics and as an integrative component of biological research.

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Author information

Authors and Affiliations

  1. Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel

    Guy Shapira & Noam Shomron

Authors
  1. Guy Shapira
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  2. Noam Shomron
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Corresponding author

Correspondence to Noam Shomron .

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Editors and Affiliations

  1. Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel

    Noam Shomron

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Shapira, G., Shomron, N. (2021). Single-Cell Transcriptome Profiling. In: Shomron, N. (eds) Deep Sequencing Data Analysis. Methods in Molecular Biology, vol 2243. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-1103-6_16

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  • DOI: https://doi.org/10.1007/978-1-0716-1103-6_16

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-1102-9

  • Online ISBN: 978-1-0716-1103-6

  • eBook Packages: Springer Protocols

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