Nebulosa recovers single-cell gene expression signals by kernel density estimation
J Alquicira-Hernandez, JE Powell - Bioinformatics, 2021 - academic.oup.com
Bioinformatics, 2021•academic.oup.com
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.
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.
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.
Oxford University Press