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Computational Methods for Single-Cell RNA Sequencing

Authors
  • Hie, Brian
  • Peters, Joshua
  • Nyquist, Sarah K.
  • Shalek, Alex K.
  • Berger, Bonnie
  • Bryson, Bryan D.
Type
Published Article
Journal
Annual Review of Biomedical Data Science
Publisher
Annual Reviews
Publication Date
Jul 20, 2020
Volume
3
Pages
339–364
Identifiers
DOI: 10.1146/annurev-biodatasci-012220-100601
Source
Annual Reviews
Keywords
License
Yellow

Abstract

Single-cell RNA sequencing (scRNA-seq) has provided a high-dimensional catalog of millions of cells across species and diseases. These data have spurred the development of hundreds of computational tools to derive novel biological insights. Here, we outline the components of scRNA-seq analytical pipelines and the computational methods that underlie these steps. We describe available methods, highlight well-executed benchmarking studies, and identify opportunities for additional benchmarking studies and computational methods. As the biochemical approaches for single-cell omics advance, we propose coupled development of robust analytical pipelines suited for the challenges that new data present and principled selection of analytical methods that are suited for the biological questions to be addressed.

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