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Identification of Cell Types from Single-Cell Transcriptomic Data

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Computational Methods for Single-Cell Data Analysis

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1935))

Abstract

Unprecedented technological advances in single-cell RNA-sequencing (scRNA-seq) technology have now made it possible to profile genome-wide expression in single cells at low cost and high throughput. There is substantial ongoing effort to use scRNA-seq measurements to identify the “cell types” that form components of a complex tissue, akin to taxonomizing species in ecology. Cell type classification from scRNA-seq data involves the application of computational tools rooted in dimensionality reduction and clustering, and statistical analysis to identify molecular signatures that are unique to each type. As datasets continue to grow in size and complexity, computational challenges abound, requiring analytical methods to be scalable, flexible, and robust. Moreover, careful consideration needs to be paid to experimental biases and statistical challenges that are unique to these measurements to avoid artifacts. This chapter introduces these topics in the context of cell-type identification, and outlines an instructive step-by-step example bioinformatic pipeline for researchers entering this field.

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Acknowledgments

K. S. would like to acknowledge support from NIH 1K99EY028625-01, the Klarman Cell Observatory, and the laboratory of Dr. Aviv Regev at the Broad Institute. We would like to gratefully acknowledge critical feedback from Drs. Inbal Benhar and Jose Ordovas-Montanes.

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Authors and Affiliations

  1. Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Karthik Shekhar

  2. Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA

    Vilas Menon

  3. Columbia University Medical Center, New York, NY, USA

    Vilas Menon

Authors
  1. Karthik Shekhar
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  2. Vilas Menon
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Corresponding authors

Correspondence to Karthik Shekhar or Vilas Menon .

Editor information

Editors and Affiliations

  1. Dana–Farber Cancer Institute and Harvard Chan, School of Public Health, Boston, MA, USA

    Guo-Cheng Yuan

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Shekhar, K., Menon, V. (2019). Identification of Cell Types from Single-Cell Transcriptomic Data. In: Yuan, GC. (eds) Computational Methods for Single-Cell Data Analysis. Methods in Molecular Biology, vol 1935. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-9057-3_4

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  • DOI: https://doi.org/10.1007/978-1-4939-9057-3_4

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  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-9056-6

  • Online ISBN: 978-1-4939-9057-3

  • eBook Packages: Springer Protocols

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