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Tutorial: guidelines for the computational analysis of single-cell RNA sequencing data

Nature Protocols volume 16, pages 1–9 (2021)Cite this article

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Abstract

Single-cell RNA sequencing (scRNA-seq) is a popular and powerful technology that allows you to profile the whole transcriptome of a large number of individual cells. However, the analysis of the large volumes of data generated from these experiments requires specialized statistical and computational methods. Here we present an overview of the computational workflow involved in processing scRNA-seq data. We discuss some of the most common tasks and the tools available for addressing central biological questions. In this article and our companion website (https://scrnaseq-course.cog.sanger.ac.uk/website/index.html), we provide guidelines regarding best practices for performing computational analyses. This tutorial provides a hands-on guide for experimentalists interested in analyzing their data as well as an overview for bioinformaticians seeking to develop new computational methods.

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Fig. 1: Overview of the workflow.

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Acknowledgements

We thank J. Eliasova for help with Fig. 1. We also thank S. Ballerau, M. Büttner, M. Do Nascimentoo Lopes Primo, J. Lee, R. Lyu, E. Madissoon, R. Martinez Nunez, S. Y. Müller, K. Polanski, P. Qiao and J. Westoby for their contributions to teaching the course and developing the material.

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Authors and Affiliations

  1. Wellcome Sanger Institute, Hinxton, UK

    Tallulah S. Andrews, Vladimir Yu Kiselev & Martin Hemberg

  2. Bioinformatics and Cellular Genomics, St Vincent’s Institute of Medical Research, Fitzroy, Victoria, Australia

    Davis McCarthy

  3. Melbourne Integrative Genomics, Faculty of Science, University of Melbourne, Melbourne, Victoria, Australia

    Davis McCarthy

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  1. Tallulah S. Andrews
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T.S.A., V.Y.K., D.M. and M.H. planned the tutorial and wrote the text together.

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Correspondence to Martin Hemberg.

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Andrews, T.S., Kiselev, V.Y., McCarthy, D. et al. Tutorial: guidelines for the computational analysis of single-cell RNA sequencing data. Nat Protoc 16, 1–9 (2021). https://doi.org/10.1038/s41596-020-00409-w

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