The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Automatic Classification of Cellular Expression by Nonlinear Stochastic Embedding (ACCENSE)
Open accessAbstract
Significance: Mass cytometry enables the measurement of nearly 40 different proteins at the single-cell level, providing an unprecedented level of multidimensional information. Because of the complexity of these datasets across diverse populations of cells, new computational tools are needed to glean useful biological insights. Here we describe ACCENSE (Automatic Classification of Cellular Expression by Nonlinear Stochastic Embedding), a tool that computes a two-dimensional nonlinear distillation of the raw data, and automatically stratifies cells into phenotypic subpopulations based on their distribution of markers. Applying this tool to murine CD8 + T-cell data recovers known naive and memory subpopulations, and reveals additional diversity within these. In particular, we identify a novel subpopulation with a distinct multivariate phenotype, but which is not distinguishable on a biaxial plot of conventional markers.
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Cites
Two subsets of memory T lymphocytes with distinct homing potentials and effector functions
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“CD8 T cells are conventionally divided into naive, central memory (TCM), and effector memory subpopulations (TEM) based on their expression of homing receptors and their propensity to traffic into secondary lymphoid organs (15).”
BackgroundBendall, S. C., Simonds, E. F., Qiu, P., Amir, E.-A. D., Krutzik, P. O., Finck, R., Bruggner, R. V., Melamed, R., Trejo, A., Ornatsky, O. I., et al. (2011) · Science · doi:10.1126/science.1198704 · Cited by 2,279
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“These phenotypes are dynamic and alterations often occur, for example during the differentiation of lymphocytes from naive to memory cells upon encountering their specific antigens (1).”
BackgroundAmir, 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. (2013) · Nature Biotechnology · doi:10.1038/nbt.2594 · Cited by 1,550
Extracting a cellular hierarchy from high-dimensional cytometry data with SPADE
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Interpreting flow cytometry data: a guide for the perplexed
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Virus-specific CD4(+) memory-phenotype T cells are abundant in unexposed adults
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“Traditionally, it was believed that naive cells differentiate to acquire a memory phenotype only upon antigen encounter, but this may also occur in the context of homeostatic proliferation (16), or T-cell receptor (TCR) cross-reactivity (17).”
BackgroundSelf-citation
Cited by
SCANPY: large-scale single-cell gene expression data analysis
Wolf, F. A., Angerer, P., & Theis, F. J. (2018) · Genome Biology · doi:10.1186/s13059-017-1382-0 · Cited by 8,881
Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets.
Macosko, E. Z., Basu, A., Satija, R., Nemesh, J., Shekhar, K., Goldman, M., Tirosh, I., Bialas, A. R., Kamitaki, N., Martersteck, E. M., et al. (2015) · Cell · doi:10.1016/j.cell.2015.05.002 · Cited by 7,110
Self-citationRevealing the vectors of cellular identity with single-cell genomics
Wagner, A., Regev, A., & Yosef, N. (2016) · Nature Biotechnology · doi:10.1038/nbt.3711 · Cited by 632
histoCAT: analysis of cell phenotypes and interactions in multiplex image cytometry data
Schapiro, D., Jackson, H. W., Raghuraman, S., Fischer, J. R., Zanotelli, V. R. T., Schulz, D., Giesen, C., Catena, R., Varga, Z., & Bodenmiller, B. (2017) · Nature Methods · doi:10.1038/nmeth.4391 · Cited by 659
Gury-BenAri, M., Thaiss, C. A., Serafini, N., Winter, D. R., Giladi, A., Lara-Astiaso, D., Levy, M., Salame, T. M., Weiner, A., David, E., et al. (2016) · Cell · doi:10.1016/j.cell.2016.07.043 · Cited by 587
Metadata
- Authors
- Shekhar, K., Brodin, P., Davis, M. M., & Chakraborty, A. K.
- Venue
- Proceedings of the National Academy of Sciences
- Published
- 2013-12-16
- Volume
- 111
- Issue
- 1
- Pages
- 202-207
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- OpenAlex
- W2047594345
- Cited by
- 215
- References
- 7
- FWCI
- 8.07
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Shekhar, K., Brodin, P., Davis, M. M., & Chakraborty, A. K. (2014). Automatic Classification of Cellular Expression by Nonlinear Stochastic Embedding (ACCENSE). Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.1321405111
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