Nebulosa recovers single-cell gene expression signals by kernel density estimation

Jose Alquicira-Hernandez; Joseph E Powell

doi:10.1093/bioinformatics/btab003

Nebulosa recovers single-cell gene expression signals by kernel density estimation

Bioinformatics. 2021 Aug 25;37(16):2485-2487. doi: 10.1093/bioinformatics/btab003.

Authors

Jose Alquicira-Hernandez^{1

2}, Joseph E Powell^{1

3}

Affiliations

¹ Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, Sydney, NSW 2010, Australia.
² Computational Genomics, Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD 4072, Australia.
³ UNSW Cellular Genomics Futures Institute, University of New South Wales, Sydney, NSW 2052, Australia.

PMID: 33459785
DOI: 10.1093/bioinformatics/btab003

Abstract

Summary: Data sparsity in single-cell experiments prevents an accurate assessment of gene expression when visualized in a low-dimensional space. Here, we introduce Nebulosa, an R package that uses weighted kernel density estimation to recover signals lost through drop-out or low expression.

Availability and implementation: Nebulosa can be easily installed from www.github.com/powellgenomicslab/Nebulosa.

Supplementary information: Supplementary data are available at Bioinformatics online.