schex avoids overplotting for large single-cell RNA-sequencing datasets

Saskia Freytag; Ryan Lister

doi:10.1093/bioinformatics/btz907

schex avoids overplotting for large single-cell RNA-sequencing datasets

Bioinformatics. 2020 Apr 1;36(7):2291-2292. doi: 10.1093/bioinformatics/btz907.

Authors

Saskia Freytag¹, Ryan Lister^{1

2}

Affiliations

¹ Molecular Medicine, Harry Perkins Institute of Medical Research, Perth, 6009, WA, Australia.
² Australian Research Council Centre of Excellence in Plant Energy Biology, School of Molecular Sciences, The University of Western Australia, Perth, 6009, WA, Australia.

PMID: 31794001
DOI: 10.1093/bioinformatics/btz907

Abstract

Summary: Due to the scale and sparsity of single-cell RNA-sequencing data, traditional plots can obscure vital information. Our R package schex overcomes this by implementing hexagonal binning, which has the additional advantages of improving speed and reducing storage for resulting plots.

Availability and implementation: schex is freely available from Bioconductor via http://bioconductor.org/packages/release/bioc/html/schex.html and its development version can be accessed on GitHub via https://github.com/SaskiaFreytag/schex.

Supplementary information: Supplementary data are available at Bioinformatics online.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Base Sequence
Exome Sequencing
RNA*
Sequence Analysis, RNA
Software*

Substances

RNA

Grants and funding

HHMI/Howard Hughes Medical Institute/United States