Dimensionality reduction for visualizing single-cell data using UMAP
A benchmarking analysis on single-cell RNA-seq and mass cytometry data reveals the best-performing technique for dimensionality reduction. Advances in single-cell technologies have enabled high-resolution dissection of tissue composition. Several tools for dimensionality reduction are available to a...
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| Veröffentlicht in: | Nature biotechnology 2019-01, Vol.37 (1), p.38-44 |
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| creator | Becht, Etienne McInnes, Leland Healy, John Dutertre, Charles-Antoine Kwok, Immanuel W H Ng, Lai Guan Ginhoux, Florent Newell, Evan W |
| description | A benchmarking analysis on single-cell RNA-seq and mass cytometry data reveals the best-performing technique for dimensionality reduction.
Advances in single-cell technologies have enabled high-resolution dissection of tissue composition. Several tools for dimensionality reduction are available to analyze the large number of parameters generated in single-cell studies. Recently, a nonlinear dimensionality-reduction technique, uniform manifold approximation and projection (UMAP), was developed for the analysis of any type of high-dimensional data. Here we apply it to biological data, using three well-characterized mass cytometry and single-cell RNA sequencing datasets. Comparing the performance of UMAP with five other tools, we find that UMAP provides the fastest run times, highest reproducibility and the most meaningful organization of cell clusters. The work highlights the use of UMAP for improved visualization and interpretation of single-cell data. |
| doi_str_mv | 10.1038/nbt.4314 |
| format | Article |
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Advances in single-cell technologies have enabled high-resolution dissection of tissue composition. Several tools for dimensionality reduction are available to analyze the large number of parameters generated in single-cell studies. Recently, a nonlinear dimensionality-reduction technique, uniform manifold approximation and projection (UMAP), was developed for the analysis of any type of high-dimensional data. Here we apply it to biological data, using three well-characterized mass cytometry and single-cell RNA sequencing datasets. Comparing the performance of UMAP with five other tools, we find that UMAP provides the fastest run times, highest reproducibility and the most meaningful organization of cell clusters. The work highlights the use of UMAP for improved visualization and interpretation of single-cell data.</description><identifier>ISSN: 1087-0156</identifier><identifier>EISSN: 1546-1696</identifier><identifier>DOI: 10.1038/nbt.4314</identifier><identifier>PMID: 30531897</identifier><language>eng</language><publisher>New York: Nature Publishing Group US</publisher><subject>631/114 ; 631/114/2164 ; 631/250 ; Agriculture ; analysis ; Approximation ; B cells ; Bioinformatics ; Biomedical Engineering/Biotechnology ; Biomedicine ; Biotechnology ; Bone marrow ; Cells (Biology) ; Cytometry ; Datasets ; Dendritic cells ; Flow cytometry ; Gene sequencing ; Life Sciences ; Lymphocytes ; Medical research ; Principal components analysis ; Reduction ; Reproducibility ; Ribonucleic acid ; RNA ; RNA sequencing ; Surgery ; Transcription (Genetics) ; Visualization (Computer)</subject><ispartof>Nature biotechnology, 2019-01, Vol.37 (1), p.38-44</ispartof><rights>Springer Nature America, Inc. 2018</rights><rights>COPYRIGHT 2019 Nature Publishing Group</rights><rights>Copyright Nature Publishing Group Jan 2019</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c579t-4af6b88dd6ce3cd7efba359829e55470eb09134dcd005659fe6f00c6655aea783</citedby><cites>FETCH-LOGICAL-c579t-4af6b88dd6ce3cd7efba359829e55470eb09134dcd005659fe6f00c6655aea783</cites><orcidid>0000-0002-2857-7755 ; 0000-0003-2143-6834 ; 0000-0002-2889-243X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1038/nbt.4314$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1038/nbt.4314$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27923,27924,41487,42556,51318</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/30531897$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Becht, Etienne</creatorcontrib><creatorcontrib>McInnes, Leland</creatorcontrib><creatorcontrib>Healy, John</creatorcontrib><creatorcontrib>Dutertre, Charles-Antoine</creatorcontrib><creatorcontrib>Kwok, Immanuel W H</creatorcontrib><creatorcontrib>Ng, Lai Guan</creatorcontrib><creatorcontrib>Ginhoux, Florent</creatorcontrib><creatorcontrib>Newell, Evan W</creatorcontrib><title>Dimensionality reduction for visualizing single-cell data using UMAP</title><title>Nature biotechnology</title><addtitle>Nat Biotechnol</addtitle><addtitle>Nat Biotechnol</addtitle><description>A benchmarking analysis on single-cell RNA-seq and mass cytometry data reveals the best-performing technique for dimensionality reduction.
Advances in single-cell technologies have enabled high-resolution dissection of tissue composition. Several tools for dimensionality reduction are available to analyze the large number of parameters generated in single-cell studies. Recently, a nonlinear dimensionality-reduction technique, uniform manifold approximation and projection (UMAP), was developed for the analysis of any type of high-dimensional data. Here we apply it to biological data, using three well-characterized mass cytometry and single-cell RNA sequencing datasets. Comparing the performance of UMAP with five other tools, we find that UMAP provides the fastest run times, highest reproducibility and the most meaningful organization of cell clusters. The work highlights the use of UMAP for improved visualization and interpretation of single-cell data.</description><subject>631/114</subject><subject>631/114/2164</subject><subject>631/250</subject><subject>Agriculture</subject><subject>analysis</subject><subject>Approximation</subject><subject>B cells</subject><subject>Bioinformatics</subject><subject>Biomedical Engineering/Biotechnology</subject><subject>Biomedicine</subject><subject>Biotechnology</subject><subject>Bone marrow</subject><subject>Cells (Biology)</subject><subject>Cytometry</subject><subject>Datasets</subject><subject>Dendritic cells</subject><subject>Flow cytometry</subject><subject>Gene sequencing</subject><subject>Life Sciences</subject><subject>Lymphocytes</subject><subject>Medical research</subject><subject>Principal components analysis</subject><subject>Reduction</subject><subject>Reproducibility</subject><subject>Ribonucleic acid</subject><subject>RNA</subject><subject>RNA 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Advances in single-cell technologies have enabled high-resolution dissection of tissue composition. Several tools for dimensionality reduction are available to analyze the large number of parameters generated in single-cell studies. Recently, a nonlinear dimensionality-reduction technique, uniform manifold approximation and projection (UMAP), was developed for the analysis of any type of high-dimensional data. Here we apply it to biological data, using three well-characterized mass cytometry and single-cell RNA sequencing datasets. Comparing the performance of UMAP with five other tools, we find that UMAP provides the fastest run times, highest reproducibility and the most meaningful organization of cell clusters. The work highlights the use of UMAP for improved visualization and interpretation of single-cell data.</abstract><cop>New York</cop><pub>Nature Publishing Group US</pub><pmid>30531897</pmid><doi>10.1038/nbt.4314</doi><tpages>7</tpages><orcidid>https://orcid.org/0000-0002-2857-7755</orcidid><orcidid>https://orcid.org/0000-0003-2143-6834</orcidid><orcidid>https://orcid.org/0000-0002-2889-243X</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | 631/114 631/114/2164 631/250 Agriculture analysis Approximation B cells Bioinformatics Biomedical Engineering/Biotechnology Biomedicine Biotechnology Bone marrow Cells (Biology) Cytometry Datasets Dendritic cells Flow cytometry Gene sequencing Life Sciences Lymphocytes Medical research Principal components analysis Reduction Reproducibility Ribonucleic acid RNA RNA sequencing Surgery Transcription (Genetics) Visualization (Computer) |
| title | Dimensionality reduction for visualizing single-cell data using UMAP |
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