The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Automated high-dimensional flow cytometric data analysis
Open accessAbstract
Flow cytometric analysis allows rapid single cell interrogation of surface and intracellular determinants by measuring fluorescence intensity of fluorophore-conjugated reagents. The availability of new platforms, allowing detection of increasing numbers of cell surface markers, has challenged the traditional technique of identifying cell populations by manual gating and resulted in a growing need for the development of automated, high-dimensional analytical methods. We present a direct multivariate finite mixture modeling approach, using skew and heavy-tailed distributions, to address the complexities of flow cytometric analysis and to deal with high-dimensional cytometric data without the need for projection or transformation. We demonstrate its ability to detect rare populations, to model robustly in the presence of outliers and skew, and to perform the critical task of matching cell populations across samples that enables downstream analysis. This advance will facilitate the application of flow cytometry to new, complex biological and clinical problems.
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PLINK: a tool set for whole-genome association and population-based linkage analyses.
Purcell, S., Neale, B., Todd-Brown, K., Thomas, L., Ferreira, M. A. R., Bender, D., Maller, J., Sklar, P., de Bakker, P. I. W., Daly, M. J., et al. (2007) · The American Journal of Human Genetics · doi:10.1086/519795 · Cited by 34,089
Sakaguchi, S., Sakaguchi, N., Asano, M., Itoh, M., & Toda, M. (1995) · The Journal of Immunology · doi:10.4049/jimmunol.155.3.1151 · Cited by 5,608
Seventeen-colour flow cytometry: unravelling the immune system
Perfetto, S. P., Chattopadhyay, P. K., & Roederer, M. (2004) · Nature Reviews Immunology · doi:10.1038/nri1416 · Cited by 1,019
Cited by
McLachlan, G., & Peel, D. (2000) · Wiley Series in Probability and Statistics · doi:10.1002/0471721182 · Cited by 10,991
Self-citationData-Driven Phenotypic Dissection of AML Reveals Progenitor-like Cells that Correlate with Prognosis
Levine, J. H., Simonds, E. F., Bendall, S. C., Davis, K. L., Amir, E.-A. D., Tadmor, M. D., Litvin, O., Fienberg, H. G., Jager, A., Zunder, E. R., et al. (2015) · Cell · doi:10.1016/j.cell.2015.05.047 · Cited by 2,303
FlowSOM: Using self‐organizing maps for visualization and interpretation of cytometry data
Van Gassen, S., Callebaut, B., Van Helden, M. J., Lambrecht, B. N., Demeester, P., Dhaene, T., & Saeys, Y. (2015) · Cytometry Part A · doi:10.1002/cyto.a.22625 · Cited by 1,919
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. (2013) · Nature Biotechnology · doi:10.1038/nbt.2594 · Cited by 1,550
Cellular Heterogeneity: Do Differences Make a Difference?
Altschuler, S. J., & Wu, L. F. (2010) · Cell · doi:10.1016/j.cell.2010.04.033 · Cited by 1,214
Metadata
- Authors
- Pyne, S., Hu, X., Wang, K., Rossin, E., Lin, T.-I., Maier, L. M., Baecher-Allan, C., McLachlan, G. J., Tamayo, P., Hafler, D. A., De Jager, P. L., & Mesirov, J. P.
- Venue
- Proceedings of the National Academy of Sciences
- Published
- 2009-05-26
- Volume
- 106
- Issue
- 21
- Pages
- 8519-8524
- ISSN
- 0027-8424
- 1091-6490
- OpenAlex
- W2097446839
- Cited by
- 285
- References
- 3
- FWCI
- 24.22
- Type
- journal article
- Language
- en
- Open access
- green
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- Pyne, S., Hu, X., Wang, K., Rossin, E., Lin, T.-I., Maier, L. M., Baecher-Allan, C., McLachlan, G. J., Tamayo, P., Hafler, D. A., De Jager, P. L., & Mesirov, J. P. (2009). Automated high-dimensional flow cytometric data analysis. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.0903028106
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
