Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Single-cell RNA-seq analysis software providers scramble to offer solutions

Nature Biotechnology volume 38, pages 254–257 (2020)Cite this article

A Publisher Correction to this article was published on 15 April 2020

This article has been updated

A raft of tools have sprung up to help biologists work through the single-cell transcriptomic bottleneck, but integration remains elusive.

For over a decade, researchers have been developing RNA sequencing (RNA-seq) techniques to analyze changes in gene expression in individual cells rather than whole tissue samples. This technology is allowing researchers to tackle complex biological systems and phenomena — for example, profiling rare cell types within a tissue, analyzing tumor heterogeneity, or tracing lineages in differentiating cells — at single-cell resolution. Achieving such insights requires specialized algorithms that can account for the distinctive artifacts and biases associated with data arising from these experiments. “Single-cell data is much noisier than bulk RNA-seq,” says biostatistician Stephanie Hicks, of the Johns Hopkins Bloomberg School of Public Health. Although bioinformaticians have devised computational tools to overcome that noise, as the scale of analysis expands, other challenges will grow too.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Learn more

Prices may be subject to local taxes which are calculated during checkout

Change history

Acknowledgements

Additional reporting by Chris Lieu, Redwood City, CA, USA.

Author information

Authors and Affiliations

  1. Philadelphia, PA, USA

    Michael Eisenstein

Authors
  1. Michael Eisenstein
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Eisenstein, M. Single-cell RNA-seq analysis software providers scramble to offer solutions. Nat Biotechnol 38, 254–257 (2020). https://doi.org/10.1038/s41587-020-0449-8

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41587-020-0449-8

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing