Cited by 1,550Single-cell and spatial transcriptomicsData Visualization and AnalyticsAcute Myeloid Leukemia Research

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viSNE enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia

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Amir, El-ad David, Davis, Kara L, Tadmor, Michelle D, Simonds, Erin F, Levine, Jacob H, Bendall, Sean C, Shenfeld, Daniel K, Krishnaswamy, Smita, Nolan, Garry P, Pe'er, Dana·2013·Nature Biotechnology

DOI 10.1038/nbt.2594PMC PMC4076922

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Abstract

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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Schulz, A. R., Stanislawiak, S., Baumgart, S., Grützkau, A., & Mei, H. E. (2016) · Cytometry Part A · doi:10.1002/cyto.a.22904 · Cited by 49
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Pieren, D. K. J., Smits, N. A. M., van de Garde, M. D. B., & Guichelaar, T. (2019) · Scientific Reports · doi:10.1038/s41598-019-42120-1 · Cited by 49
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Earl, D. C., Ferrell, P. B., Leelatian, N., Froese, J. T., Reisman, B. J., Irish, J. M., & Bachmann, B. O. (2018) · Nature Communications · doi:10.1038/s41467-017-02470-8 · Cited by 48
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Gyngard, F., & Steinhauser, M. L. (2019) · Journal of Analytical Atomic Spectrometry · doi:10.1039/c9ja00171a · Cited by 46
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Visualizing Structure and Transitions for Biological Data ExplorationPreprint
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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)
ORCID: Davis, Kara L
Simonds, Erin F
Bendall, Sean C
Krishnaswamy, Smita
Nolan, Garry P
Venue: Nature Biotechnology
Publisher: Springer Science and Business Media LLC
Published: 2013-05-19
Volume: 31
Issue: 6
Pages: 545-552
ISSN: 0733-222X (ISSN-L)
1087-0156
1546-1696
DOI: 10.1038/nbt.2594
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 Graphics and Computer-Aided Design › 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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viSNE enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia

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