Computational Methods for Single-Cell RNA Sequencing
Name
annurev-biodatasci-012220-100601.pdf
Description
Accepted version
Size
928.22 KB
Format
Adobe PDF
Checksum (MD5)
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Author(s)
Hie, Brian
Peters, Joshua
Nyquist, Sarah K
Shalek, Alex K
Berger, Bonnie
Bryson, Bryan D
Date Issued
2020
Journal
Annual Review of Biomedical Data Science
Publisher
Annual Reviews
Version
Author's final manuscript
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.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Biological Engineering
Ragon Institute of MGH, MIT and Harvard
Massachusetts Institute of Technology. Computational and Systems Biology Program
Massachusetts Institute of Technology. Department of Chemistry
Massachusetts Institute of Technology. Institute for Medical Engineering & Science
Koch Institute for Integrative Cancer Research at MIT
Massachusetts Institute of Technology. Department of Mathematics
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
10.1146/ANNUREV-BIODATASCI-012220-100601
https://doi.org/10.1146/ANNUREV-BIODATASCI-012220-100601
