Nebulosa recovers single-cell gene expression signals by kernel density estimation
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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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High-confidence Trem2+ monocytes were identified by expression of known TREM2+ monocyte markers (TREM2, APOE, CSF1R) using gene-weighted density estimation in Nebulosa.
“High-confidence Trem2+ monocytes were identified by expression of known TREM2+ monocyte markers (TREM2, APOE, CSF1R81,89), identified by gene-weighted density estimation implemented in the Nebulosa package for R.78”
From Cystatin C is glucocorticoid responsive, directs recruitment of Trem2+ macrophages, and predicts failure of cancer immunotherapy (2023) · cites this paper as [78]
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- Authors
- Alquicira-Hernandez, J., & Powell, J. E.
- Affiliations
- Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research , Sydney, NSW 2010, Australia
- Garvan Institute of Medical Research (AU)
- Corresponding author
- Powell, Joseph E
- ORCID
- Powell, Joseph E
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2021-01-18
- Volume
- 37
- Issue
- 16
- Pages
- 2485-2487
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- OpenAlex
- W3125797470
- Cited by
- 381
- References
- 1
- Citations by year
- 2026: 65 · 2025: 108 · 2024: 85 · 2023: 62 · 2022: 46 · 2021: 14 · 2020: 1
- FWCI
- 16.24
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Bioinformatics (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nd)
- PubMed (publishedVersion)
- Funders
- National Health and Medical Research Council Investigator (1175781)
- National Health and Medical Research Council project grant (APP1143163)
- Australian Research Council Discovery project (DP180101405)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Keywords
- single-cell gene expression, kernel density estimation, data sparsity, dropout, signal reconstruction, dimensionality reduction, R package
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