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Here, we introduce Nebulosa, an R package that uses weighted kernel density estimation to recover signals lost through drop-out or low expression.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>Nebulosa can be easily installed from www.github.com\/powellgenomicslab\/Nebulosa.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Supplementary information<\/jats:title>\n                    <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btab003","type":"journal-article","created":{"date-parts":[[2021,1,7]],"date-time":"2021-01-07T10:57:48Z","timestamp":1610017068000},"page":"2485-2487","source":"Crossref","is-referenced-by-count":333,"title":["<i>Nebulosa<\/i>\n                    recovers single-cell gene expression signals by kernel density estimation"],"prefix":"10.1093","volume":"37","author":[{"given":"Jose","family":"Alquicira-Hernandez","sequence":"first","affiliation":[{"name":"Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research , Sydney, NSW 2010, Australia"},{"name":"Computational Genomics, Institute for Molecular Bioscience, University of Queensland , Brisbane, QLD 4072, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5070-4124","authenticated-orcid":false,"given":"Joseph E","family":"Powell","sequence":"additional","affiliation":[{"name":"Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research , Sydney, NSW 2010, Australia"},{"name":"UNSW Cellular Genomics Futures Institute, University of New South Wales , Sydney, NSW 2052, Australia"}]}],"member":"286","published-online":{"date-parts":[[2021,1,18]]},"reference":[{"key":"2023051609053393600_btab003-B2","doi-asserted-by":"crossref","first-page":"264","DOI":"10.1186\/s13059-019-1862-5","article-title":"scpred: accurate supervised method for cell-type classification from single-cell rna-seq data","volume":"20","author":"Alquicira-Hernandez","year":"2019","journal-title":"Genome Biol"},{"key":"2023051609053393600_btab003-B3","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1038\/nbt.4314","article-title":"Dimensionality reduction for visualizing single-cell data using UMAP","volume":"37","author":"Becht","year":"2019","journal-title":"Nat. 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