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

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....

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Bibliographic Details
Published in:Bioinformatics (Oxford, England) England), 2021-08, Vol.37 (16), p.2485-2487
Main Authors: Alquicira-Hernandez, Jose, Powell, Joseph E
Format: Article
Language:English
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Bioinformatics
Density
Gene expression
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Summary: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.
ISSN:1367-4803
1367-4811
DOI:10.1093/bioinformatics/btab003