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 inBioinformatics (Oxford, England) Vol. 37; no. 16; pp. 2485 - 2487
Main Authors Alquicira-Hernandez, Jose, Powell, Joseph E
Format Journal Article
LanguageEnglish
Published England Oxford University Press 25.08.2021
Oxford Publishing Limited (England)
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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.
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ISSN:1367-4803
1367-4811
1367-4811
DOI:10.1093/bioinformatics/btab003