N. Aghaeepour, R. Nikolic, H. H. Hoos, and R. R. Brinkman, Rapid cell population identification in flow cytometry data, Cytometry A, vol.79, pp.6-13, 2011.

B. Bagwel, A New Analytic Approach for Live Singlet Identification (6th Annual Mass Cytometry Summit), 2017.

D. R. Bandura, V. I. Baranov, O. I. Ornatsky, A. Antonov, R. Kinach et al., Mass Cytometry: Technique for Real Time Single Cell Multitarget Immunoassay Based on Inductively Coupled Plasma Time-of-Flight Mass Spectrometry, vol.81, pp.6813-6822, 2009.

S. C. Bendall, G. P. Nolan, M. Roederer, and P. K. Chattopadhyay, A deep profiler's guide to cytometry, Trends Immunol, vol.33, pp.323-332, 2012.

R. V. Bruggner, B. Bodenmiller, D. L. Dill, R. J. Tibshirani, and G. P. Nolan, , 2014.

, Automated identification of stratifying signatures in cellular subpopulations, Proc. Natl. Acad. Sci, vol.111, pp.2770-2777

S. Chevrier, H. L. Crowell, V. R. Zanotelli, S. Engler, M. D. Robinson et al., Compensation of Signal Spillover in Suspension and Imaging Mass Cytometry, Cell Syst, vol.6, pp.612-620, 2018.

K. E. Diggins, A. R. Greenplate, N. Leelatian, C. E. Wogsland, and J. M. Irish, Characterizing cell subsets using marker enrichment modeling, Nat. Methods, vol.14, pp.275-278, 2017.

G. Finak, J. Frelinger, W. Jiang, E. W. Newell, J. Ramey et al., OpenCyto: An Open Source Infrastructure for Scalable, Robust, Reproducible, and Automated, End-to-End Flow Cytometry Data Analysis, PLoS Comput. Biol, vol.10, 2014.

R. Finck, E. F. Simonds, A. Jager, S. Krishnaswamy, K. Sachs et al., Normalization of mass cytometry data with bead standards, Cytometry A, vol.83, pp.483-494, 2013.

G. Han, M. H. Spitzer, S. C. Bendall, W. J. Fantl, and G. P. Nolan, Metal-isotopetagged monoclonal antibodies for high-dimensional mass cytometry, Nat. Protoc, vol.13, pp.2121-2148, 2018.

T. Kohonen, The self-organizing map, Proc. IEEE, vol.78, pp.1464-1480, 1990.

L. Mcinnes, J. Healy, and J. Melville, UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction, ArXiv180203426 Cs Stat, 2018.

S. Monti, Consensus Clustering: A Resampling-Based Method for Class Discovery and Visualization of Gene Expression Microarray Data, Mach. Learn, vol.52, pp.91-118, 2003.

M. Nowicka, C. Krieg, L. M. Weber, F. J. Hartmann, S. Guglietta et al., CyTOF workflow: differential discovery in highthroughput high-dimensional cytometry datasets, 2017.

P. Qiu, E. F. Simonds, S. C. Bendall, K. D. Gibbs, R. V. Bruggner et al., Extracting a cellular hierarchy from highdimensional cytometry data with SPADE, Nat. Biotechnol, vol.29, pp.886-891, 2011.

N. Samusik, Z. Good, M. H. Spitzer, K. L. Davis, and G. P. Nolan, Automated mapping of phenotype space with single-cell data, Nat. Methods, vol.13, pp.493-496, 2016.

L. Van-der-maaten and G. Hinton, Visualizing Data using t-SNE, J. Mach. Learn. Res, pp.2579-2605, 2008.

S. Van-gassen, B. Callebaut, M. J. Van-helden, B. N. Lambrecht, P. Demeester et al., FlowSOM: Using self-organizing maps for visualization and interpretation of cytometry data: FlowSOM, Cytometry A, vol.87, pp.636-645, 2015.

L. M. Weber, R. , and M. D. , Comparison of clustering methods for highdimensional single-cell flow and mass cytometry data: Comparison of High-Dim, Cytometry Clustering Methods. Cytometry A, vol.89, pp.1084-1096, 2016.

M. D. Wilkerson and D. N. Hayes, ConsensusClusterPlus: a class discovery tool with confidence assessments and item tracking, Bioinformatics, vol.26, pp.1572-1573, 2010.

E. R. Zunder, R. Finck, G. K. Behbehani, E. D. Amir, S. Krishnaswamy et al., Palladium-based mass tag cell barcoding with a doublet-filtering scheme and single-cell deconvolution algorithm, Nat. Protoc, vol.10, pp.316-333, 2015.

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