viSNE enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia
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New high-dimensional, single-cell technologies offer unprecedented resolution in the analysis of heterogeneous tissues. However, because these technologies can measure dozens of parameters simultaneously in individual cells, data interpretation can be challenging. Here we present viSNE, a tool that allows one to map high-dimensional cytometry data onto two dimensions, yet conserve the high-dimensional structure of the data. viSNE plots individual cells in a visual similar to a scatter plot, while using all pairwise distances in high dimension to determine each cell's location in the plot. We integrated mass cytometry with viSNE to map healthy and cancerous bone marrow samples. Healthy bone marrow automatically maps into a consistent shape, whereas leukemia samples map into malformed shapes that are distinct from healthy bone marrow and from each other. We also use viSNE and mass cytometry to compare leukemia diagnosis and relapse samples, and to identify a rare leukemia population reminiscent of minimal residual disease. viSNE can be applied to any multi-dimensional single-cell technology.
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t-SNE is a powerful nonlinear dimensionality-reduction alternative, especially when computed with an efficient approximation that scales to large genomic data sets.
“A powerful, nonlinear alternative5,105–107 is t-distributed stochastic neighbor embedding108 (t-SNE; introduced for single-cell analysis as viSNE105), especially when computed with an efficient approximation109 compatible with the scale of large genomic data sets110.”
From Revealing the vectors of cellular identity with single-cell genomics (2016) · cites this paper as [109]
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- Authors
- Amir, E.-A. D., Davis, K. L., Tadmor, M. D., Simonds, E. F., Levine, J. H., Bendall, S. C., Shenfeld, D. K., Krishnaswamy, S., Nolan, G. P., & Pe'er, D.
- Affiliations
- Columbia University (US)
- Stanford University (US)
- Venue
- Nature Biotechnology
- Published
- 2013-05-19
- Volume
- 31
- Issue
- 6
- Pages
- 545-552
- ISSN
- 0733-222X (ISSN-L)
- 1087-0156
- 1546-1696
- PMC
- PMC4076922
- OpenAlex
- W1982729887
- Cited by
- 1,550
- References
- 36
- Citations by year
- 2026: 24 · 2025: 59 · 2024: 70 · 2023: 84 · 2022: 132 · 2021: 181 · 2020: 177 · 2019: 208 · 2018: 192 · 2017: 178 · 2016: 112 · 2015: 83 · 2014: 44 · 2013: 5
- FWCI
- 36.40
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Biotechnology (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Howard Hughes Medical Institute
- National Institutes of Health (DP2OD002414)
- National Heart, Lung, and Blood Institute (HV-10-05_)
- National Cancer Institute (U54CA121852, R01CA130826, P01CA034233, CA 09011, R01CA164729)
- National Institute of General Medical Sciences (T32GM008798)
- National Institute of Allergy and Infectious Diseases (U19AI100627, HHSN272200700038C, U19AI057229)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Data Visualization and Analytics
- Health Sciences › Medicine › Hematology › Acute Myeloid Leukemia Research
- Keywords
- mass cytometry, leukemia, phenotypic heterogeneity, bone marrow, minimal residual disease, leukemia relapse, dimensionality reduction
- MeSH terms
- Humans, Leukemia, Neoplasm Recurrence, Local, Recurrence, Biomarkers, Tumor, Immunophenotyping, Image Cytometry, Bone Marrow Neoplasms, Cell Lineage, Single-Cell Analysis
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